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cases/case_1/case_1_description.md
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cases/case_1/case_1_description.md
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# Case 1: Simulating Stock Policies
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- Title: Choosing stock policies under uncertainty
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- Description: Students role-play their participation as consultants in a
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project for Beanie Limited, a coffee beans roasting company. Elisa, the
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regional manager for the italian region, is not happy with their inventory
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policies for raw beans. The students are asked to analyse the problems posed
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by Elisa and apply simulation techniques, together with real data, to
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recommend a stock policy for the company's warehouse in the italian region.
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Python notebooks with some helpful prepared functions are provided to the
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students. The final delivery is a report with their recommendation to the
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client company, along with the used code.
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Stuff I want them to understand:
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- The model/hypothesis/validate
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- That in a simulation you set parameters, and you observe results
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- To write in a problem-solving manner.
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- That there are trade-offs and it's not trivial to find optimal solutions.
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Elements of the simulation:
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- Demand behavior
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- Lead time and standard deviation of provider (or providers)
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- Service level
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- Punishment for sales lost
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Observable effects of policies:
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- Mean inventory at hand
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- Service level
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- Warehousing/Capital Cost
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- Lost sales cost
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# Case 1: Choosing ordering goods under uncertainty
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You are part of an expert simulation team in SimiUPF SL. You have been assigned
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to a new project with a client company, Beanie Limited. Beanie Limited is a
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coffee roasting company and also distributes raw coffee beans through Europe
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and Middle East.
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Specifically, you will be working for Elisa Bolzano, the Director of Beanie
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Limited's warehouse located in Caserta, near Naples. Elisa is the full
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responsible for all the operations in the warehouse. She has requested the help
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of the SimiUPF team because she is worried about how certain things are managed
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in the warehouse and wants your help.
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The Caserta warehouse serves the raw coffee beans distribution business of
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Beanie Limited in southern europe and the mediterranean. The warehouse and its
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team are responsible for serving clients and also other smaller regional
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warehouses from Beanie Limited in this geography. From the warehouse point of
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view, they are usually just called "the clients". Whenever one of the clients
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needes raw beans, they arrange a transport truck that goes to the warehouse to
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pick up a certain amount of goods. Elisa's team fill up the truck with the
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requested goods, and then the clients take care of receiving that in their own
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locations.
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The Caserta warehouse itself has only one way to source coffee beans to store
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in their warehouse: requesting them to the Beanie Limited central offices in
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Diemen, near Amsterdam. Whenever Elisa's team considers that more stock is
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needed, they post a sourcing order to the central office for a certain amount
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of beans. The central office arranges the goods and the delivery and, after a
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few days, the goods reach Caserta and are stored. The central office tries to
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ensure a lead time of 7 days (lead time is the time that passess between an
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order being placed and the goods reaching their destination), but the reality
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is they do what they can and this time is not always respected.
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Stock is a necessary evil (it implies a lot of cost), but Elisa's warehouse
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plays a key role in serving the clients in their region properly. Having too
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little stock means the clients need to wait long times to get their goods,
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which is risky for the business. On the other hand, having a lot of stock means
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high warehouse costs and financial opportunity cost (if Beanie Limited has 1
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million € in coffee beans in a warehouse, that is 1 million € they can't invest
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somewhere else to improve their business). Thus, Elisa needs the stock to be as
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small as possible, without disappointing clients.
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Elisa is calling you because 2021 was a terrible year for the warehouse. The
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year was a chaotic one, and Elisa's team was not able to run operations
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smoothly. Although Elisa is not providing exact numbers, she is very well aware
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that the warehouse stock was unnecessarily high at times, and that there were
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too many periods were the warehouse was out of stock and clients had to wait to
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get their goods.
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Elisa thinks that the main reason for this is the lack of a clear policy for
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when to order and how much to order from Diemen. Her team decides independently
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when to do it, and Elisa has a feeling that they are not approaching these
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decisions the right way. This means that sometimes they order when there is no
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need to, sometimes they don't order when they should be, and that the amounts
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being ordered might not be the best ones.
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Here is where you come in. As simulation experts, Elisa expects from you that
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you can help design an ordering policy to fix these issues. Doing this implies
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examining data from last year, building a proper simulation to examine the
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different factors being involved, and deciding when and how should Elisa's team
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order more goods from Diemen.
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Elisa expects a report where you share your findings and recommendations in a
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clear way that can help her team. Also, Elisa does not trust you blindly: you
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need to motivate the reasoning behind your recommendations. Otherwise, she will
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not feel comfortable implementing your recommendations and the bosses at
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SimiUPF will be mad at you...
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## Detailed task definition
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- Below you will find four levels of questions. Levels 1 to 3 are compulsory.
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Level 4 is optional.
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- You need to write a report document where you answer the questions of the
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different levels. This report should be directed towards Elisa, should give
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her clear recommendations and should justify these recommendations.
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- Each level is worth 2 points out of a total of 10. The 2 missing points will
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grade the clearness and structure of your report.
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- You need to use Python notebooks to solve all levels. A helper notebook is
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provided. For each level, please attach a notebook that shows your
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solution/proposal/analysis.
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## Data
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- You are provided with three tables that contain real data from 2021.
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- demand_events: this table shows how many beans left the Caserta warehouse
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to serve clients. There is some amount leaving every day because the
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warehouse serves many small orders from small clients, so there is always
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some order being fulfilled. The amount is measured in kilograms, and
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represents the total amount that left during that day.
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- sourcing_events: this table shows the beans orders that Elisa's team
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placed to Diemen. For each order, there are two dates: the date when
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Elisa's team placed the order, and the date where the beans actually
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reached the Caserta warehouse. The amount is measured in kilograms.
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- stock_state: this table shows the stock at the warehouse at the end of
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each date. As you can guess, the stock for a certain date is the stock of
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the previous day, plus the goods that reached Caserta coming from Diemen,
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minus the goods that left the warehouse to serve client orders. A
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negative stock is not a challenge to the laws of physics: it means
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clients are waiting for their requested beans. If one row shows -1.000,
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it means that the warehouse is empty, and clients are awaiting for a
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total amount of 1.000 kgs of beans. If next morning, a 1.000 kgs reach
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Caserta from Diemen, those will be used immediately to satisfy those
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waiting clients, and the warehouse stock will become 0.
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## Notebook
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A notebook with some helping code has been provided. The code contains a small
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simulation engine that can help you simulate a year of activity for the
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warehouse. The instructions on how to use the code are in the notebook itself.
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## Levels
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- Level 1
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- Elisa wants you to measure the performance of the last year, providing
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quantitative metrics. She knows it was a bad year, but hasn't looked at
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the real data to summarize how bad it was. Remember that there is a
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trade-off:
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too much stock, is not desired, but running out of stock and making
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clients wait is also negative.
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- Going one step further, Elisa wants to know: what was done wrong?
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- Level 2
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- Elisa wants you to propose an ordering policy. This means, that you need
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to define a rule that, once each day, should answer the questions: should
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be place an order to request material today? If yes, how much should we
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order?
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- Use simulation to present metrics on what is the expected performance
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with the policy you are proposing. Remember, you need to convince Elisa
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that this is better than what happens today.
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- As a specific constraint, Elisa explains that she wants that the
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probability of a stockout is at most of 5%.
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-
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- Level 3
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- Right after you finished designing your policy for level 2, Elisa called
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with some news: she has just been informed by the management in Diemen
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that a new Minimum Order Quantity (MOQ) rule will begin soon. This rule
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means that, when the Caserta warehouse places an order to request
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material from, the order should be of at least 500,000 kgs of beans, and
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not less than that.
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- Elisa wants you to take this into account. Does it affect the policy you
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proposed for level 2? If so, you need to come up with a new one that
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adapts to this rule.
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- Level 4
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- Elisa briefly discussed with you in one meeting that there is an option
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to come to an agreement with the team in Diemen to improve the lead time
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stability. The proposal from Diemen is that, if the target lead time was
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set to something higher that the current 7 days target, providing a more
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stable delivery would be feasible.
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- The specific proposal from Diemen is: if the lead time target is changed
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to 15 days, they provide a 100% guarantee that orders will be delivered
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in exactly 15 days.
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- Elisa would love if you could take some additional time to study this
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proposal. What is better for Caserta? The current 7 days target
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lead-time, with unstable deliveries? Or a fixed, 15-day lead time?
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- The MOQ rule of level 3 still applies.
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366
cases/case_1/demand_events.csv
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cases/case_1/demand_events.csv
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date,demand_quantity
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2021-01-01,54609.49281314914
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2021-01-02,36208.63648649295
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2021-01-03,77784.17276763407
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2021-01-04,76481.81360421646
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2021-01-05,52305.87658918292
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2021-01-06,57098.56436860317
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2021-01-07,41565.68706138541
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2021-01-08,81995.500619844
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2021-01-09,71041.91466404148
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2021-01-10,31787.17080818402
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2021-01-11,32735.09633866546
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2021-01-12,32855.44553254065
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2021-01-13,55420.934082626205
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2021-01-14,48883.311263507494
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2021-01-15,48368.597773147136
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2021-01-16,40225.99478591274
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2021-01-17,69003.66723779934
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2021-01-18,67378.93368511106
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2021-01-19,59444.432628854185
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2021-01-20,54441.80415596864
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2021-01-21,52796.814721541414
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2021-01-22,30193.150803735854
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2021-01-23,62328.53756562836
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2021-01-24,43690.320158519615
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2021-01-25,78451.89473980921
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2021-01-26,47794.13927746792
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2021-01-27,51454.93947489077
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2021-01-28,64633.17690683539
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2021-01-29,67371.66310250101
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2021-01-30,51137.068372905895
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2021-01-31,62192.931782584405
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2021-02-01,62381.245234820446
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2021-02-02,62744.03145531537
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2021-02-03,51305.706023572566
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2021-02-04,52618.66719247759
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2021-02-05,51961.10865929137
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2021-02-06,55154.27434352692
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2021-02-07,39799.62917632264
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2021-02-08,65486.97890826721
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2021-02-09,55355.23228947571
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2021-02-10,46211.4777291026
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2021-02-11,53132.95392507133
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2021-02-12,31537.03525349067
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2021-02-13,46447.72089889987
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2021-02-14,63731.03176553111
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2021-02-15,54454.77009849779
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2021-02-16,40659.50720269109
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2021-02-17,63493.99813149876
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2021-02-18,54931.266644895266
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2021-02-19,28278.734877540137
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2021-02-20,36379.638867181835
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2021-02-21,62202.758260545044
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2021-02-22,54523.210135004185
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2021-02-23,42395.85236943305
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2021-02-24,42934.425415725156
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2021-02-25,51494.77047631462
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2021-02-26,44220.2960470736
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2021-02-27,45670.120416197926
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2021-02-28,57107.49381367681
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2021-03-01,28972.234058115788
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2021-03-02,43048.734607813065
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2021-03-03,41505.53405595842
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2021-03-04,47926.03548243223
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2021-03-05,61278.99549030161
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2021-03-06,39044.50052424295
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2021-03-07,37142.63665375576
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2021-03-08,59385.01021647509
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2021-03-09,19622.861200135892
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2021-03-10,42875.82033258566
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2021-03-11,37298.094228973925
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2021-03-12,53411.899019061944
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2021-03-13,45345.99865109816
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2021-03-14,53211.40616195306
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2021-03-15,40974.40081655905
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2021-03-16,56025.67583148412
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2021-03-17,42957.88421097572
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2021-03-18,65464.99283743926
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2021-03-19,28628.77720679815
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2021-03-20,50873.13077669
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2021-03-21,47215.115350042746
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2021-03-22,44982.481462385775
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2021-03-23,72845.44784612038
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2021-03-24,36657.28355561715
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2021-03-25,35932.62440127316
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2021-03-26,90802.53749884429
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2021-03-27,54150.36198995029
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2021-03-28,57725.715294590715
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2021-03-29,49797.541628930994
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2021-03-30,39842.574327318325
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2021-03-31,46648.05822011224
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2021-04-01,38251.20061495644
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2021-04-02,54968.95147105346
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2021-04-03,39223.33668121346
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2021-04-04,55196.72314245463
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2021-04-05,60193.96623402014
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2021-04-06,107790.97235982082
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2021-04-07,58927.3553815537
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2021-04-08,52570.524217849554
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2021-04-09,40996.74684261808
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2021-04-10,52952.91853803685
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2021-04-11,55117.279622249654
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2021-04-12,62330.90239991735
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2021-04-13,60352.159875666686
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2021-04-14,46481.1929993728
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2021-04-15,26740.04853400801
|
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2021-04-16,58824.75809726865
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2021-04-17,48919.848176294996
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2021-04-18,69164.97343682637
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2021-04-19,65052.99346838036
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2021-04-20,86948.6316872793
|
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2021-04-21,47600.922050548594
|
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2021-04-22,53629.43407349051
|
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2021-04-23,58802.85640700405
|
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2021-04-24,27277.291629712032
|
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2021-04-25,72991.08369503866
|
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2021-04-26,46319.17825995694
|
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2021-04-27,42473.64434623195
|
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2021-04-28,62986.327912551824
|
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2021-04-29,59770.868769586974
|
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2021-04-30,46967.110213491585
|
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2021-05-01,31283.252270527257
|
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2021-05-02,45270.96133039481
|
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2021-05-03,40200.06151139432
|
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2021-05-04,28769.438869243786
|
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2021-05-05,41597.284397045456
|
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2021-05-06,42053.59694349442
|
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2021-05-07,32056.90063878994
|
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2021-05-08,24126.23251230451
|
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2021-05-09,61874.94040944404
|
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2021-05-10,69582.18210731493
|
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2021-05-11,54713.709988929106
|
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2021-05-12,77986.61766717135
|
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2021-05-13,51047.031274850284
|
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2021-05-14,59715.32807151039
|
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2021-05-15,73688.19223261088
|
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2021-05-16,65807.03078052355
|
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2021-05-17,55779.76069593255
|
||||
2021-05-18,53655.30817237868
|
||||
2021-05-19,53863.25586084147
|
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2021-05-20,42447.86518825701
|
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2021-05-21,34634.185379985654
|
||||
2021-05-22,81385.8091352819
|
||||
2021-05-23,53674.49856663084
|
||||
2021-05-24,32130.44754196027
|
||||
2021-05-25,35141.955123039676
|
||||
2021-05-26,35379.77494659018
|
||||
2021-05-27,47723.22357446625
|
||||
2021-05-28,50903.4531491154
|
||||
2021-05-29,52597.71388776773
|
||||
2021-05-30,37762.845725518426
|
||||
2021-05-31,31687.345250434668
|
||||
2021-06-01,1380.9898989639114
|
||||
2021-06-02,39384.958015718286
|
||||
2021-06-03,62407.74873554036
|
||||
2021-06-04,65856.83339328374
|
||||
2021-06-05,41159.52864583683
|
||||
2021-06-06,78292.78851815795
|
||||
2021-06-07,45085.067801033474
|
||||
2021-06-08,63969.20178674298
|
||||
2021-06-09,61865.47920564571
|
||||
2021-06-10,66481.65277980786
|
||||
2021-06-11,51663.83884564799
|
||||
2021-06-12,49462.60941335073
|
||||
2021-06-13,48907.56631014691
|
||||
2021-06-14,27822.17014448859
|
||||
2021-06-15,35277.37023428074
|
||||
2021-06-16,58563.35766039751
|
||||
2021-06-17,62202.644540009576
|
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2021-06-18,59361.79725578232
|
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2021-06-19,37617.54204811233
|
||||
2021-06-20,84719.87850010264
|
||||
2021-06-21,50153.4959152938
|
||||
2021-06-22,40323.203680923136
|
||||
2021-06-23,34962.05953043287
|
||||
2021-06-24,46613.355492701965
|
||||
2021-06-25,53246.878843729624
|
||||
2021-06-26,38693.95753463766
|
||||
2021-06-27,64529.674857993334
|
||||
2021-06-28,41126.42916746255
|
||||
2021-06-29,71984.7315338233
|
||||
2021-06-30,28888.043384351673
|
||||
2021-07-01,60293.9028556177
|
||||
2021-07-02,49816.29840729628
|
||||
2021-07-03,42812.38643232065
|
||||
2021-07-04,34909.73927750447
|
||||
2021-07-05,37591.53584671515
|
||||
2021-07-06,39469.203591839716
|
||||
2021-07-07,54396.08709948022
|
||||
2021-07-08,28047.275778018222
|
||||
2021-07-09,41834.25913212226
|
||||
2021-07-10,42604.985980117504
|
||||
2021-07-11,57706.78926368313
|
||||
2021-07-12,37965.84096167571
|
||||
2021-07-13,42719.54678256345
|
||||
2021-07-14,36541.18442771253
|
||||
2021-07-15,45179.2123752051
|
||||
2021-07-16,49107.119659072996
|
||||
2021-07-17,28547.87933059051
|
||||
2021-07-18,49714.75688145967
|
||||
2021-07-19,33936.612529083315
|
||||
2021-07-20,72130.34325424329
|
||||
2021-07-21,21218.431770514377
|
||||
2021-07-22,47114.58552828316
|
||||
2021-07-23,62864.89434803029
|
||||
2021-07-24,48265.27576417639
|
||||
2021-07-25,82856.83438714968
|
||||
2021-07-26,71619.09933599173
|
||||
2021-07-27,31313.91831932018
|
||||
2021-07-28,51702.76017876872
|
||||
2021-07-29,51759.91074963173
|
||||
2021-07-30,43399.332699545244
|
||||
2021-07-31,55424.54037571451
|
||||
2021-08-01,42225.94672589529
|
||||
2021-08-02,34807.53319498364
|
||||
2021-08-03,73469.6548372101
|
||||
2021-08-04,27209.4505106898
|
||||
2021-08-05,57237.08622864778
|
||||
2021-08-06,45169.07725691487
|
||||
2021-08-07,51028.44462209041
|
||||
2021-08-08,40990.419651217926
|
||||
2021-08-09,31432.767517597265
|
||||
2021-08-10,46704.92168243732
|
||||
2021-08-11,67447.45628232439
|
||||
2021-08-12,56060.762852218075
|
||||
2021-08-13,36096.04292632875
|
||||
2021-08-14,82159.16133987988
|
||||
2021-08-15,61384.53830739901
|
||||
2021-08-16,42921.0220131585
|
||||
2021-08-17,57574.80918470686
|
||||
2021-08-18,44223.76579375525
|
||||
2021-08-19,38112.188923509486
|
||||
2021-08-20,43302.275718994686
|
||||
2021-08-21,31995.553894163357
|
||||
2021-08-22,51456.16324022061
|
||||
2021-08-23,51376.41164803253
|
||||
2021-08-24,45624.593753100846
|
||||
2021-08-25,46734.78195159169
|
||||
2021-08-26,37565.07483616892
|
||||
2021-08-27,23554.39766955899
|
||||
2021-08-28,20604.948141803365
|
||||
2021-08-29,66334.25895451049
|
||||
2021-08-30,48047.85418448473
|
||||
2021-08-31,56191.71390404747
|
||||
2021-09-01,44859.282252098456
|
||||
2021-09-02,46030.147501430656
|
||||
2021-09-03,10703.823438653832
|
||||
2021-09-04,70343.60042856235
|
||||
2021-09-05,69607.14131423642
|
||||
2021-09-06,56931.55211394906
|
||||
2021-09-07,54663.61348397007
|
||||
2021-09-08,57725.529008129895
|
||||
2021-09-09,53757.39275518815
|
||||
2021-09-10,53480.74906036454
|
||||
2021-09-11,49602.291868261746
|
||||
2021-09-12,52985.895433602054
|
||||
2021-09-13,37411.73715166042
|
||||
2021-09-14,59155.55398150197
|
||||
2021-09-15,21991.02211112378
|
||||
2021-09-16,59508.78533477017
|
||||
2021-09-17,41544.13170794038
|
||||
2021-09-18,46878.16624464087
|
||||
2021-09-19,32469.82943570702
|
||||
2021-09-20,61806.26905613678
|
||||
2021-09-21,56074.725664414334
|
||||
2021-09-22,53483.80545741505
|
||||
2021-09-23,30077.209266523543
|
||||
2021-09-24,73070.54849698953
|
||||
2021-09-25,44118.377703017635
|
||||
2021-09-26,34134.3360656615
|
||||
2021-09-27,61619.510801440054
|
||||
2021-09-28,29334.959480643633
|
||||
2021-09-29,54840.778405071345
|
||||
2021-09-30,54861.25954092192
|
||||
2021-10-01,37666.69406650353
|
||||
2021-10-02,45514.88974301199
|
||||
2021-10-03,37872.595956602185
|
||||
2021-10-04,54862.49528732663
|
||||
2021-10-05,72168.41067112274
|
||||
2021-10-06,58411.76789552352
|
||||
2021-10-07,64450.64193866483
|
||||
2021-10-08,39846.169995410615
|
||||
2021-10-09,51012.92307031886
|
||||
2021-10-10,56193.97181413436
|
||||
2021-10-11,34065.44429410843
|
||||
2021-10-12,58206.460717550566
|
||||
2021-10-13,46487.945645762295
|
||||
2021-10-14,50076.70184963691
|
||||
2021-10-15,47580.71432500986
|
||||
2021-10-16,53915.82908269834
|
||||
2021-10-17,39606.35607109019
|
||||
2021-10-18,73790.25224218029
|
||||
2021-10-19,38407.621781936425
|
||||
2021-10-20,33404.97538990958
|
||||
2021-10-21,59931.9601178157
|
||||
2021-10-22,74486.16955897454
|
||||
2021-10-23,50195.02837816861
|
||||
2021-10-24,40073.20302847418
|
||||
2021-10-25,64626.796001266266
|
||||
2021-10-26,61727.34307665966
|
||||
2021-10-27,62187.88733591297
|
||||
2021-10-28,54214.87801602549
|
||||
2021-10-29,67142.34221772531
|
||||
2021-10-30,46487.69937914996
|
||||
2021-10-31,31086.740684974324
|
||||
2021-11-01,48278.95337799651
|
||||
2021-11-02,20718.68300716247
|
||||
2021-11-03,58138.40065378947
|
||||
2021-11-04,49722.30296011415
|
||||
2021-11-05,36742.138456983004
|
||||
2021-11-06,42736.48890700623
|
||||
2021-11-07,55356.6885726762
|
||||
2021-11-08,81832.3429551895
|
||||
2021-11-09,62338.173681547836
|
||||
2021-11-10,39202.33687407937
|
||||
2021-11-11,62786.50002194336
|
||||
2021-11-12,37726.689751497914
|
||||
2021-11-13,67033.48460270898
|
||||
2021-11-14,55366.81040522425
|
||||
2021-11-15,53898.24191372635
|
||||
2021-11-16,62845.981914852084
|
||||
2021-11-17,33833.82833106041
|
||||
2021-11-18,61076.99869993116
|
||||
2021-11-19,51228.112090794835
|
||||
2021-11-20,55635.47027518508
|
||||
2021-11-21,71803.01115735975
|
||||
2021-11-22,50315.05762449138
|
||||
2021-11-23,60710.007411381375
|
||||
2021-11-24,73249.01607526309
|
||||
2021-11-25,57699.01149670034
|
||||
2021-11-26,46798.292724322295
|
||||
2021-11-27,36359.18817807891
|
||||
2021-11-28,61511.52093729363
|
||||
2021-11-29,39284.72872960448
|
||||
2021-11-30,28462.06773230841
|
||||
2021-12-01,25809.261932155227
|
||||
2021-12-02,59425.1826389642
|
||||
2021-12-03,43014.05369644615
|
||||
2021-12-04,52769.507877984564
|
||||
2021-12-05,56657.291422193426
|
||||
2021-12-06,59175.14433261302
|
||||
2021-12-07,57450.71229516849
|
||||
2021-12-08,25887.75148158159
|
||||
2021-12-09,21300.79633013303
|
||||
2021-12-10,49686.47609053778
|
||||
2021-12-11,50683.5775985572
|
||||
2021-12-12,43090.41843560319
|
||||
2021-12-13,28815.44447997063
|
||||
2021-12-14,31445.73933682877
|
||||
2021-12-15,50964.200286431944
|
||||
2021-12-16,37689.76522472434
|
||||
2021-12-17,39270.444361100475
|
||||
2021-12-18,63767.92920582164
|
||||
2021-12-19,66245.76864762916
|
||||
2021-12-20,64310.02645239804
|
||||
2021-12-21,20186.46628098661
|
||||
2021-12-22,57829.12348425346
|
||||
2021-12-23,61209.40407684893
|
||||
2021-12-24,60229.294569424455
|
||||
2021-12-25,57790.197713617585
|
||||
2021-12-26,59848.30412950744
|
||||
2021-12-27,48843.47435878844
|
||||
2021-12-28,45483.444566160666
|
||||
2021-12-29,45361.81436223178
|
||||
2021-12-30,57103.88645952773
|
||||
2021-12-31,49479.32345442135
|
||||
|
61
cases/case_1/sourcing_events.csv
Normal file
61
cases/case_1/sourcing_events.csv
Normal file
|
|
@ -0,0 +1,61 @@
|
|||
request_date,delivery_date,amount
|
||||
2021-06-18,2021-06-24,361622.08421162824
|
||||
2021-04-08,2021-04-17,404943.20818378055
|
||||
2021-08-02,2021-08-10,372079.3749313439
|
||||
2021-03-23,2021-03-28,324410.8683704191
|
||||
2021-07-14,2021-07-19,467167.83305448893
|
||||
2021-03-02,2021-03-09,280731.9688885999
|
||||
2021-07-18,2021-07-26,369123.2301230304
|
||||
2021-02-18,2021-02-27,384645.34262920194
|
||||
2021-01-21,2021-01-25,310407.1921732673
|
||||
2021-08-12,2021-08-21,366174.22830054327
|
||||
2021-11-02,2021-11-09,391623.2685507731
|
||||
2021-07-13,2021-07-21,345458.2161390513
|
||||
2021-06-01,2021-06-10,360467.175317622
|
||||
2021-10-20,2021-10-31,336289.0502818366
|
||||
2021-07-21,2021-07-29,381876.17957110034
|
||||
2021-10-06,2021-10-11,314261.8504829489
|
||||
2021-11-15,2021-11-22,308806.6478588278
|
||||
2021-09-05,2021-09-14,330698.694265319
|
||||
2021-12-26,2022-01-04,407311.3676167535
|
||||
2021-01-16,2021-01-25,214247.55937424488
|
||||
2021-04-20,2021-04-26,269445.2778637154
|
||||
2021-01-22,2021-01-27,479246.85314207803
|
||||
2021-02-02,2021-02-11,347470.77377278876
|
||||
2021-11-26,2021-12-01,302533.4097257286
|
||||
2021-08-23,2021-09-01,381700.4232063111
|
||||
2021-03-31,2021-04-09,277635.5674830633
|
||||
2021-06-03,2021-06-10,190836.06099482052
|
||||
2021-06-30,2021-07-07,352714.6964695578
|
||||
2021-08-10,2021-08-18,281208.5043011291
|
||||
2021-08-04,2021-08-10,336022.8514088414
|
||||
2021-01-05,2021-01-10,351041.68228418275
|
||||
2021-04-29,2021-05-06,338705.4533610672
|
||||
2021-06-14,2021-06-22,216774.79936204778
|
||||
2021-06-21,2021-07-01,331075.5096480785
|
||||
2021-12-01,2021-12-07,358629.8535678753
|
||||
2021-12-13,2021-12-23,237124.0787089357
|
||||
2021-12-17,2021-12-22,229278.15299078176
|
||||
2021-10-17,2021-10-22,258928.96346443848
|
||||
2021-11-28,2021-12-05,378532.9798112182
|
||||
2021-06-10,2021-06-16,197717.59182534326
|
||||
2021-02-22,2021-02-27,384000.6974037471
|
||||
2021-11-30,2021-12-11,355168.6944729242
|
||||
2021-02-03,2021-02-10,312383.0537738918
|
||||
2021-04-13,2021-04-25,231816.99643375044
|
||||
2021-06-07,2021-06-13,276881.9915723157
|
||||
2021-12-03,2021-12-07,380847.56715868326
|
||||
2021-11-29,2021-12-09,412123.5063860729
|
||||
2021-03-29,2021-04-04,261409.7021051771
|
||||
2021-01-28,2021-02-05,404557.69495151856
|
||||
2021-07-04,2021-07-09,374522.25600175356
|
||||
2021-01-13,2021-01-20,328894.06129062787
|
||||
2021-09-29,2021-10-04,280742.72595198866
|
||||
2021-10-18,2021-10-25,291048.90802077635
|
||||
2021-04-09,2021-04-16,449418.2981764818
|
||||
2021-04-14,2021-04-22,341098.18303366995
|
||||
2021-05-08,2021-05-17,416941.3633993643
|
||||
2021-05-19,2021-05-27,345255.5746514472
|
||||
2021-07-26,2021-08-03,299525.71274023474
|
||||
2021-10-24,2021-10-31,367817.7882031555
|
||||
2021-01-14,2021-01-21,471478.60787775513
|
||||
|
366
cases/case_1/stock_state.csv
Normal file
366
cases/case_1/stock_state.csv
Normal file
|
|
@ -0,0 +1,366 @@
|
|||
date,amount_in_stock
|
||||
2021-01-01,647479.2516513831
|
||||
2021-01-02,611270.6151648902
|
||||
2021-01-03,533486.4423972561
|
||||
2021-01-04,457004.6287930397
|
||||
2021-01-05,404698.75220385677
|
||||
2021-01-06,347600.1878352536
|
||||
2021-01-07,306034.5007738682
|
||||
2021-01-08,224039.00015402416
|
||||
2021-01-09,152997.08548998268
|
||||
2021-01-10,472251.5969659814
|
||||
2021-01-11,439516.50062731595
|
||||
2021-01-12,406661.0550947753
|
||||
2021-01-13,351240.1210121491
|
||||
2021-01-14,302356.80974864156
|
||||
2021-01-15,253988.21197549443
|
||||
2021-01-16,213762.2171895817
|
||||
2021-01-17,144758.54995178233
|
||||
2021-01-18,77379.61626667128
|
||||
2021-01-19,17935.183637817092
|
||||
2021-01-20,292387.4407724763
|
||||
2021-01-21,711069.23392869
|
||||
2021-01-22,680876.0831249541
|
||||
2021-01-23,618547.5455593257
|
||||
2021-01-24,574857.225400806
|
||||
2021-01-25,710652.8900352417
|
||||
2021-01-26,662858.7507577738
|
||||
2021-01-27,1090650.664424961
|
||||
2021-01-28,1026017.4875181256
|
||||
2021-01-29,958645.8244156246
|
||||
2021-01-30,907508.7560427187
|
||||
2021-01-31,845315.8242601342
|
||||
2021-02-01,782934.5790253138
|
||||
2021-02-02,720190.5475699984
|
||||
2021-02-03,668884.8415464258
|
||||
2021-02-04,616266.1743539482
|
||||
2021-02-05,968862.7606461754
|
||||
2021-02-06,913708.4863026484
|
||||
2021-02-07,873908.8571263258
|
||||
2021-02-08,808421.8782180586
|
||||
2021-02-09,753066.6459285829
|
||||
2021-02-10,1019238.2219733722
|
||||
2021-02-11,1313576.0418210896
|
||||
2021-02-12,1282039.0065675988
|
||||
2021-02-13,1235591.2856686988
|
||||
2021-02-14,1171860.2539031678
|
||||
2021-02-15,1117405.48380467
|
||||
2021-02-16,1076745.9766019788
|
||||
2021-02-17,1013251.97847048
|
||||
2021-02-18,958320.7118255846
|
||||
2021-02-19,930041.9769480445
|
||||
2021-02-20,893662.3380808627
|
||||
2021-02-21,831459.5798203176
|
||||
2021-02-22,776936.3696853134
|
||||
2021-02-23,734540.5173158804
|
||||
2021-02-24,691606.0919001552
|
||||
2021-02-25,640111.3214238406
|
||||
2021-02-26,595891.025376767
|
||||
2021-02-27,934221.6023643162
|
||||
2021-02-28,877114.1085506395
|
||||
2021-03-01,848141.8744925237
|
||||
2021-03-02,805093.1398847107
|
||||
2021-03-03,763587.6058287523
|
||||
2021-03-04,715661.57034632
|
||||
2021-03-05,654382.5748560184
|
||||
2021-03-06,615338.0743317754
|
||||
2021-03-07,578195.4376780196
|
||||
2021-03-08,518810.4274615445
|
||||
2021-03-09,779919.5351500085
|
||||
2021-03-10,737043.7148174229
|
||||
2021-03-11,699745.620588449
|
||||
2021-03-12,646333.721569387
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||||
2021-03-13,600987.7229182889
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||||
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||||
2021-03-15,506801.91593977675
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||||
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||||
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||||
2021-03-18,342353.36305987765
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||||
2021-03-19,313724.5858530795
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||||
2021-03-20,262851.4550763895
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||||
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||||
2021-03-22,170653.85826396095
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||||
2021-03-23,97808.41041784057
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||||
2021-03-24,61151.12686222342
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||||
2021-03-25,25218.502460950258
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||||
2021-03-26,-65584.03503789403
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||||
2021-03-27,-119734.39702784433
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||||
2021-03-28,146950.75604798403
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||||
2021-03-29,97153.21441905304
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||||
2021-03-30,57310.64009173471
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||||
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||||
2021-04-01,-27588.618743333966
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||||
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||||
2021-04-03,-121780.90689560089
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||||
2021-04-04,84432.07206712157
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||||
2021-04-05,24238.10583310143
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||||
2021-04-06,-83552.86652671939
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||||
2021-04-07,-142480.2219082731
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||||
2021-04-08,-195050.74612612266
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||||
2021-04-09,41588.074514322594
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||||
2021-04-10,-11364.844023714257
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||||
2021-04-11,-66482.1236459639
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||||
2021-04-12,-128813.02604588126
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||||
2021-04-13,-189165.18592154793
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||||
2021-04-14,-235646.37892092072
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||||
2021-04-15,-262386.42745492875
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||||
2021-04-16,128207.1126242844
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||||
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||||
2021-04-18,415065.4991949436
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||||
2021-04-19,350012.5057265632
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||||
2021-04-20,263063.87403928395
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||||
2021-04-21,215462.95198873535
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||||
2021-04-22,502931.7009489148
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||||
2021-04-23,444128.84454191074
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||||
2021-04-24,416851.5529121987
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||||
2021-04-25,575677.4656509105
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||||
2021-04-26,798803.565254669
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||||
2021-04-27,756329.9209084371
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||||
2021-04-28,693343.5929958853
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||||
2021-04-29,633572.7242262983
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||||
2021-04-30,586605.6140128067
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||||
2021-05-01,555322.3617422794
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||||
2021-05-02,510051.40041188453
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||||
2021-05-03,469851.3389004902
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||||
2021-05-04,441081.9000312464
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||||
2021-05-05,399484.615634201
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||||
2021-05-06,696136.4720517738
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||||
2021-05-07,664079.5714129838
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||||
2021-05-08,639953.3389006793
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||||
2021-05-09,578078.3984912352
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||||
2021-05-10,508496.2163839203
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||||
2021-05-11,453782.5063949912
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||||
2021-05-12,375795.88872781984
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||||
2021-05-13,324748.85745296953
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||||
2021-05-14,265033.5293814591
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||||
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||||
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||||
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||||
2021-05-18,433044.6008993777
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||||
2021-05-19,379181.34503853624
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||||
2021-05-20,336733.4798502792
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||||
2021-05-21,302099.29447029356
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||||
2021-05-22,220713.48533501168
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||||
2021-05-23,167038.98676838083
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||||
2021-05-24,134908.53922642054
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||||
2021-05-25,99766.58410338087
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||||
2021-05-26,64386.80915679069
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||||
2021-05-27,361919.1602337716
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||||
2021-05-28,311015.7070846562
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||||
2021-05-29,258417.9931968885
|
||||
2021-05-30,220655.1474713701
|
||||
2021-05-31,188967.8022209354
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||||
2021-06-01,187586.8123219715
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||||
2021-06-02,148201.8543062532
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||||
2021-06-03,85794.10557071284
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||||
2021-06-04,19937.2721774291
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||||
2021-06-05,-21222.25646840773
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2021-06-06,-99515.04498656568
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2021-06-07,-144600.11278759915
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||||
2021-06-08,-208569.31457434213
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||||
2021-06-09,-270434.79377998784
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||||
2021-06-10,-146080.38556497518
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||||
2021-06-11,-197744.22441062317
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||||
2021-06-12,-247206.8338239739
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||||
2021-06-13,-19232.408561805147
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||||
2021-06-14,-47054.57870629374
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||||
2021-06-15,-82331.94894057448
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||||
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2021-06-17,-5380.359315638307
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2021-06-18,-64742.15657142063
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||||
2021-06-19,-102359.69861953295
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||||
2021-06-20,-187079.5771196356
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||||
2021-06-21,-237233.0730349294
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||||
2021-06-22,-60781.477353804774
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||||
2021-06-23,-95743.53688423763
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||||
2021-06-24,219265.19183468865
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||||
2021-06-25,166018.31299095904
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||||
2021-06-26,127324.35545632138
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||||
2021-06-27,62794.68059832805
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2021-06-28,21668.2514308655
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||||
2021-06-29,-50316.4801029578
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||||
2021-06-30,-79204.52348730947
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||||
2021-07-01,191577.08330515135
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||||
2021-07-02,141760.78489785508
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||||
2021-07-03,98948.39846553442
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||||
2021-07-04,64038.65918802995
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||||
2021-07-05,26447.1233413148
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||||
2021-07-06,-13022.080250524916
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||||
2021-07-07,285296.5291195527
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||||
2021-07-08,257249.25334153447
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||||
2021-07-09,589937.2502111658
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||||
2021-07-10,547332.2642310483
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||||
2021-07-11,489625.47496736515
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||||
2021-07-12,451659.6340056894
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||||
2021-07-13,408940.087223126
|
||||
2021-07-14,372398.90279541345
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||||
2021-07-15,327219.6904202084
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||||
2021-07-16,278112.5707611354
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||||
2021-07-17,249564.69143054486
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||||
2021-07-18,199849.93454908518
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||||
2021-07-19,633081.1550744908
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||||
2021-07-20,560950.8118202476
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||||
2021-07-21,885190.5961887846
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||||
2021-07-22,838076.0106605014
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2021-07-23,775211.1163124711
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||||
2021-07-24,726945.8405482947
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||||
2021-07-25,644089.006161145
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||||
2021-07-26,941593.1369481836
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||||
2021-07-27,910279.2186288635
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||||
2021-07-28,858576.4584500947
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||||
2021-07-29,1188692.7272715634
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||||
2021-07-30,1145293.3945720182
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||||
2021-07-31,1089868.8541963038
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||||
2021-08-01,1047642.9074704085
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||||
2021-08-02,1012835.3742754249
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||||
2021-08-03,1238891.4321784496
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||||
2021-08-04,1211681.9816677598
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||||
2021-08-05,1154444.895439112
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||||
2021-08-06,1109275.8181821972
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||||
2021-08-07,1058247.3735601068
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||||
2021-08-08,1017256.953908889
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||||
2021-08-09,985824.1863912917
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||||
2021-08-10,1275142.1161176958
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||||
2021-08-11,1207694.6598353714
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||||
2021-08-12,1151633.8969831534
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||||
2021-08-13,1115537.8540568247
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||||
2021-08-14,1033378.6927169448
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||||
2021-08-15,971994.1544095458
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||||
2021-08-16,929073.1323963873
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||||
2021-08-17,871498.3232116804
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||||
2021-08-18,1108483.0617190541
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||||
2021-08-19,1070370.8727955446
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||||
2021-08-20,1027068.5970765499
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||||
2021-08-21,1361247.2714829298
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||||
2021-08-22,1309791.1082427092
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||||
2021-08-23,1258414.6965946767
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||||
2021-08-24,1212790.1028415759
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||||
2021-08-25,1166055.3208899843
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||||
2021-08-26,1128490.2460538154
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||||
2021-08-27,1104935.8483842565
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||||
2021-08-28,1084330.9002424532
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||||
2021-08-29,1017996.6412879428
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||||
2021-08-30,969948.7871034581
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||||
2021-08-31,913757.0731994106
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||||
2021-09-01,1250598.2141536232
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||||
2021-09-02,1204568.0666521925
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||||
2021-09-03,1193864.2432135388
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||||
2021-09-04,1123520.6427849764
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||||
2021-09-05,1053913.50147074
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||||
2021-09-06,996981.9493567909
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||||
2021-09-07,942318.3358728208
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||||
2021-09-08,884592.806864691
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||||
2021-09-09,830835.4141095028
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||||
2021-09-10,777354.6650491382
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||||
2021-09-11,727752.3731808765
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2021-09-12,674766.4777472744
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||||
2021-09-13,637354.7405956141
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||||
2021-09-14,908897.880879431
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||||
2021-09-15,886906.8587683073
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||||
2021-09-16,827398.073433537
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||||
2021-09-17,785853.9417255967
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||||
2021-09-18,738975.7754809558
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||||
2021-09-19,706505.9460452488
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||||
2021-09-20,644699.676989112
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||||
2021-09-21,588624.9513246977
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||||
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||||
2021-09-23,505063.9366007591
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||||
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||||
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||||
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||||
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||||
2021-09-28,262786.2040530068
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||||
2021-09-30,153084.1661070135
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2021-10-07,62879.39650024672
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||||
2021-10-08,23033.226504836108
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||||
2021-10-09,-27979.69656548275
|
||||
2021-10-10,-84173.66837961711
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||||
2021-10-11,196022.73780922336
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||||
2021-10-12,137816.2770916728
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||||
2021-10-13,91328.3314459105
|
||||
2021-10-14,41251.62959627359
|
||||
2021-10-15,-6329.08472873627
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||||
2021-10-16,-60244.91381143461
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||||
2021-10-17,-99851.2698825248
|
||||
2021-10-18,-173641.52212470508
|
||||
2021-10-19,-212049.14390664152
|
||||
2021-10-20,-245454.1192965511
|
||||
2021-10-21,-305386.0794143668
|
||||
2021-10-22,-120943.28550890283
|
||||
2021-10-23,-171138.31388707145
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||||
2021-10-24,-211211.51691554562
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||||
2021-10-25,15210.595103964442
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||||
2021-10-26,-46516.747972695215
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||||
2021-10-27,-108704.63530860818
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||||
2021-10-28,-162919.51332463368
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||||
2021-10-29,-230061.855542359
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||||
2021-10-30,-276549.55492150894
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||||
2021-10-31,60181.492596672266
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||||
2021-11-01,11902.539218675753
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||||
2021-11-02,-8816.14378848672
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||||
2021-11-03,-66954.54444227618
|
||||
2021-11-04,-116676.84740239033
|
||||
2021-11-05,-153418.98585937332
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||||
2021-11-06,-196155.47476637954
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||||
2021-11-07,-251512.16333905573
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||||
2021-11-08,-333344.50629424525
|
||||
2021-11-09,-4059.411425020022
|
||||
2021-11-10,-43261.74829909939
|
||||
2021-11-11,-106048.24832104275
|
||||
2021-11-12,-143774.93807254065
|
||||
2021-11-13,-210808.42267524963
|
||||
2021-11-14,-266175.2330804739
|
||||
2021-11-15,-320073.4749942003
|
||||
2021-11-16,-382919.4569090524
|
||||
2021-11-17,-416753.2852401128
|
||||
2021-11-18,-477830.28394004394
|
||||
2021-11-19,-529058.3960308388
|
||||
2021-11-20,-584693.8663060239
|
||||
2021-11-21,-656496.8774633836
|
||||
2021-11-22,-398005.2872290472
|
||||
2021-11-23,-458715.2946404286
|
||||
2021-11-24,-531964.3107156917
|
||||
2021-11-25,-589663.322212392
|
||||
2021-11-26,-636461.6149367143
|
||||
2021-11-27,-672820.8031147933
|
||||
2021-11-28,-734332.3240520869
|
||||
2021-11-29,-773617.0527816914
|
||||
2021-11-30,-802079.1205139998
|
||||
2021-12-01,-525354.9727204265
|
||||
2021-12-02,-584780.1553593907
|
||||
2021-12-03,-627794.2090558368
|
||||
2021-12-04,-680563.7169338213
|
||||
2021-12-05,-358688.0285447965
|
||||
2021-12-06,-417863.17287740955
|
||||
2021-12-07,-94466.31801389478
|
||||
2021-12-08,-120354.06949547637
|
||||
2021-12-09,270468.6405604635
|
||||
2021-12-10,220782.1644699257
|
||||
2021-12-11,525267.2813442927
|
||||
2021-12-12,482176.8629086895
|
||||
2021-12-13,453361.41842871887
|
||||
2021-12-14,421915.6790918901
|
||||
2021-12-15,370951.4788054582
|
||||
2021-12-16,333261.71358073386
|
||||
2021-12-17,293991.26921963337
|
||||
2021-12-18,230223.3400138117
|
||||
2021-12-19,163977.57136618256
|
||||
2021-12-20,99667.54491378451
|
||||
2021-12-21,79481.0786327979
|
||||
2021-12-22,250930.1081393262
|
||||
2021-12-23,426844.782771413
|
||||
2021-12-24,366615.4882019885
|
||||
2021-12-25,308825.2904883709
|
||||
2021-12-26,248976.98635886348
|
||||
2021-12-27,200133.51200007505
|
||||
2021-12-28,154650.06743391437
|
||||
2021-12-29,109288.25307168259
|
||||
2021-12-30,52184.36661215486
|
||||
2021-12-31,2705.043157733511
|
||||
|
10
cases/case_1/summary_first_sweep.csv
Normal file
10
cases/case_1/summary_first_sweep.csv
Normal file
|
|
@ -0,0 +1,10 @@
|
|||
reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
|
||||
0.8,0.8,0.0684931506849315,0.0726027397260274,0.0222201370969798,10,-1594714.1742160032,-1530433.4046675265,285066.2951297739,10,50.2,50.0,1.3984117975602024,10
|
||||
0.8,1.0,0.5816438356164383,0.6520547945205479,0.25980427056171707,10,-20433.232547621035,66493.16878082216,231209.05072598366,10,49.4,49.5,0.9660917830792962,10
|
||||
0.8,1.2,0.9356164383561645,0.9534246575342465,0.040805672352052756,10,260408.40724531026,264591.4202889103,34451.38595202987,10,42.5,42.5,0.5270462766947288,10
|
||||
1.0,0.8,0.11863013698630136,0.10547945205479452,0.06135803865213693,10,-1360077.266559611,-1421026.0243578043,341686.4813035888,10,50.7,50.5,1.3374935098492577,10
|
||||
1.0,1.0,0.6550684931506849,0.7821917808219179,0.2787601486044031,10,63898.06455496148,158892.80360797068,215553.80705682412,10,49.3,49.5,1.1595018087284057,10
|
||||
1.0,1.2,0.9904109589041095,0.9945205479452055,0.011854570763239407,10,360626.1147252074,359297.0984386411,24074.260208145475,10,42.1,42.0,0.5676462121975469,10
|
||||
1.2,0.8,0.14684931506849314,0.11095890410958904,0.1127785478486988,10,-1471541.3243492458,-1590944.9289921233,402072.5562878826,10,50.1,50.5,1.7919573407620821,10
|
||||
1.2,1.0,0.8868493150684932,0.963013698630137,0.15021679044253444,10,289885.33637142787,340960.57915301056,116064.12202102688,10,49.9,50.0,0.7378647873726214,10
|
||||
1.2,1.2,0.996986301369863,1.0,0.007126799274932135,10,464072.69404238986,464252.8407907331,35729.24312964791,10,42.0,42.0,0.6666666666666669,10
|
||||
|
26
cases/case_1/summary_second_sweep.csv
Normal file
26
cases/case_1/summary_second_sweep.csv
Normal file
|
|
@ -0,0 +1,26 @@
|
|||
reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
|
||||
0.8,1.0,0.5629041095890411,0.5630136986301371,0.25173917949278124,100,-17208.65752949769,31032.43630715287,200730.55535889775,100,49.05,49.0,1.166666666666668,100
|
||||
0.8,1.05,0.804986301369863,0.8452054794520547,0.14385248604678155,100,155614.19545368513,176960.00812669177,89250.77498164216,100,47.39,47.0,0.7900262110470866,100
|
||||
0.8,1.1,0.8659726027397261,0.8876712328767123,0.09160908657556802,100,198680.3804320387,207071.20423920936,54616.27896318188,100,45.45,45.0,0.7017294652672376,100
|
||||
0.8,1.15,0.924082191780822,0.9342465753424658,0.042773373946112477,100,240782.04918482568,243459.64652624974,29223.097332241305,100,43.46,43.5,0.7305733185227711,100
|
||||
0.8,1.2,0.9414794520547946,0.9493150684931506,0.03451593530365504,100,261676.29869938738,264285.581551831,25131.022316170343,100,41.83,42.0,0.6971080231639825,100
|
||||
0.9,1.0,0.6648493150684932,0.6945205479452055,0.21570939170830844,100,73024.72198996953,104935.78104756039,163647.97407003478,100,49.32,49.0,0.8862587350511948,100
|
||||
0.9,1.05,0.8186575342465754,0.8753424657534246,0.16907143140798406,100,182114.7427397742,214236.05435841338,108978.10420990313,100,47.46,47.0,0.9147500624584167,100
|
||||
0.9,1.1,0.9226849315068493,0.9397260273972603,0.06860053019119008,100,257549.83941332222,264802.2368256551,46594.05059506942,100,45.65,46.0,0.7833494518006421,100
|
||||
0.9,1.15,0.9611506849315069,0.9726027397260274,0.040083238669095175,100,297073.0555063267,302993.9769790715,33827.59431750586,100,43.63,44.0,0.7057484028184599,100
|
||||
0.9,1.2,0.9742739726027397,0.9808219178082191,0.020594090521446923,100,316243.2068669456,319849.0246099904,23997.41545045925,100,42.05,42.0,0.6871842709362761,100
|
||||
1.0,1.0,0.6921643835616439,0.7698630136986302,0.2448481301495943,100,101882.22346645949,160026.78900577017,180014.0847194441,100,49.41,49.0,1.0739806066379143,100
|
||||
1.0,1.05,0.8867945205479453,0.936986301369863,0.1266759950750295,100,246218.68552190147,273469.8906422792,85390.22913704212,100,47.72,48.0,0.7923880286064316,100
|
||||
1.0,1.1,0.9432054794520548,0.9698630136986301,0.07141874718689835,100,301924.2700638766,316299.9013534653,59963.75704883238,100,45.76,46.0,0.7123726184201268,100
|
||||
1.0,1.15,0.9764109589041096,0.9863013698630136,0.02872101571594946,100,340290.7186400273,343600.27244723897,34849.39399657999,100,43.88,44.0,0.7286350876186868,100
|
||||
1.0,1.2,0.9906575342465753,0.9945205479452055,0.0113001726894409,100,369036.41037286026,371895.01996879186,24001.498220542777,100,42.05,42.0,0.6256309946079575,100
|
||||
1.1,1.0,0.7282465753424657,0.7479452054794521,0.2260275046018253,100,139083.56089769275,162758.51029021217,168922.45751711642,100,49.45,49.5,1.0576799462440758,100
|
||||
1.1,1.05,0.9035068493150685,0.9671232876712329,0.16213843415295967,100,289746.11535166804,320941.33405487105,114866.00516221055,100,47.71,48.0,0.7951240294584386,100
|
||||
1.1,1.1,0.9613698630136986,0.989041095890411,0.0655565291501118,100,350031.4173621522,367119.16888596804,60281.0758667702,100,45.9,46.0,0.7719841941125448,100
|
||||
1.1,1.15,0.9912054794520548,0.9972602739726028,0.017645347308402025,100,396603.9442134802,400346.3145094304,29483.57515994371,100,44.0,44.0,0.7247430753394793,100
|
||||
1.1,1.2,0.9956164383561643,1.0,0.007052473418545561,100,417138.91165137023,418615.9689806167,23055.605401908946,100,42.1,42.0,0.6741998624632427,100
|
||||
1.2,1.0,0.7846575342465754,0.8698630136986301,0.2252867428579435,100,193695.8595228242,242047.6119066357,194075.09374543617,100,49.79,50.0,1.121822110891404,100
|
||||
1.2,1.05,0.9479178082191781,0.9780821917808219,0.09026491930718351,100,346443.9102815047,358689.5454693798,87392.79621129965,100,48.05,48.0,0.7436600722307887,100
|
||||
1.2,1.1,0.9872602739726026,0.9972602739726028,0.025089991771959474,100,411297.4581000472,416474.64406344807,44380.80426082012,100,46.0,46.0,0.7106690545187017,100
|
||||
1.2,1.15,0.9916438356164383,1.0,0.022282747753160106,100,441406.6727818038,448100.49807167007,38234.435371301166,100,44.19,44.0,0.7063206700139026,100
|
||||
1.2,1.2,0.9969589041095891,1.0,0.007179214412726819,100,468382.008571895,474174.00471737224,28487.211871335898,100,42.35,42.0,0.6723244767373897,100
|
||||
|
71
cases/case_1/summary_third_sweep.csv
Normal file
71
cases/case_1/summary_third_sweep.csv
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
reorder_point_factor,purchase_size_factor,service_level_mean,service_level_median,service_level_std,service_level_count,mean_stock_level_mean,mean_stock_level_median,mean_stock_level_std,mean_stock_level_count,purchase_order_count_mean,purchase_order_count_median,purchase_order_count_std,purchase_order_count_count
|
||||
0.8,1.05,0.7859726027397261,0.8260273972602741,0.1598362225708639,100,144208.47823315178,169318.30356595665,100514.74254469664,100,47.4,47.0,0.8164965809277261,100
|
||||
0.8,1.06,0.794958904109589,0.8301369863013699,0.1403919914831539,100,153586.2551041631,173418.410864591,78290.88655784576,100,47.11,47.0,0.7371114795831993,100
|
||||
0.8,1.07,0.8067671232876712,0.8315068493150686,0.1339955549137507,100,161441.590865957,172098.93199610294,72690.59543201819,100,46.56,47.0,0.7291893935852132,100
|
||||
0.8,1.08,0.8381917808219178,0.8493150684931507,0.10243407881742274,100,180757.48462696798,185912.87023764107,63117.92391789514,100,46.47,46.0,0.7843803447642761,100
|
||||
0.8,1.09,0.8667671232876712,0.8808219178082193,0.08505385831720369,100,198896.08671044532,205127.32601251808,47198.21828806646,100,45.77,46.0,0.664466064983872,100
|
||||
0.8,1.1,0.878027397260274,0.9054794520547945,0.09679462977911307,100,207399.79024280803,225251.01385664742,55542.82909581238,100,45.52,46.0,0.7032392583932264,100
|
||||
0.8,1.11,0.8961643835616439,0.9123287671232877,0.06811908370060707,100,218048.13332393923,226635.2932288471,41083.817034265245,100,45.0,45.0,0.7654139963827331,100
|
||||
0.8,1.12,0.9054246575342466,0.9178082191780822,0.05473703917338135,100,226233.79870989014,228435.7464824344,33790.75003378518,100,44.75,45.0,0.770346898114313,100
|
||||
0.8,1.13,0.9113972602739726,0.9219178082191781,0.057722017942900286,100,229820.05176361586,234205.64538325084,37180.29105911812,100,44.41,44.0,0.6371495868001452,100
|
||||
0.8,1.14,0.9096164383561645,0.9260273972602739,0.06476061395141362,100,229451.0061609741,236898.16887785456,39996.703715484575,100,43.98,44.0,0.6192207550093444,100
|
||||
0.9,1.05,0.799068493150685,0.8589041095890411,0.17478733730560359,100,170856.8688515813,195706.0585029618,107559.81482477198,100,47.66,48.0,0.7278028371042323,100
|
||||
0.9,1.06,0.8728493150684932,0.9178082191780822,0.14035816656132336,100,214583.50564701803,237766.20853748685,91537.75933622228,100,47.16,47.0,0.9180567971690441,100
|
||||
0.9,1.07,0.8863835616438357,0.9246575342465753,0.13202268131162476,100,225962.8131575521,248956.4255873827,87091.59551249178,100,46.85,47.0,0.7703468981143126,100
|
||||
0.9,1.08,0.8878356164383561,0.9109589041095891,0.1034915126220675,100,231793.74733914566,241230.59100630687,65364.238707012075,100,46.37,46.0,0.7608474807008903,100
|
||||
0.9,1.09,0.910958904109589,0.9342465753424658,0.0848895391251729,100,247414.19854095648,259646.62831328082,56859.363211731616,100,46.14,46.0,0.829019135649309,100
|
||||
0.9,1.1,0.9097808219178083,0.936986301369863,0.09263505453625975,100,248051.95584330414,261613.48309493947,62298.32416064076,100,45.61,46.0,0.8027100562117265,100
|
||||
0.9,1.11,0.9249041095890411,0.9493150684931506,0.06777942231931827,100,259875.5154301237,265650.987470857,44837.543279018144,100,45.24,45.0,0.7123726184201262,100
|
||||
0.9,1.12,0.9426575342465753,0.958904109589041,0.054991006506983194,100,276332.38956547884,283931.61956175056,40395.41216420654,100,44.78,45.0,0.7327821620165825,100
|
||||
0.9,1.13,0.9434794520547944,0.9657534246575343,0.05636688915712585,100,277312.20724561514,285598.0109710443,42114.061861274546,100,44.54,44.0,0.6422812876966388,100
|
||||
0.9,1.14,0.9461369863013698,0.9671232876712329,0.05949943242463646,100,281586.1900728949,292772.94372766616,44568.637852341606,100,44.05,44.0,0.7436600722307894,100
|
||||
0.95,1.05,0.8504657534246576,0.8876712328767123,0.1426216962196151,100,209502.91100042395,231229.60137442837,98473.14510338863,100,47.62,48.0,0.7885544888073256,100
|
||||
0.95,1.06,0.8763561643835617,0.9205479452054794,0.13371982345233996,100,232132.86769214022,253984.80407355353,85226.62571623079,100,47.18,47.0,0.7571877794400369,100
|
||||
0.95,1.07,0.8897534246575343,0.9232876712328767,0.11000276133429772,100,244444.98080390875,258917.87933101476,70323.6063035861,100,46.75,47.0,0.7833494518006393,100
|
||||
0.95,1.08,0.9206301369863015,0.958904109589041,0.09976094450434589,100,265926.47402984084,283736.9000909083,66614.08688632757,100,46.37,46.0,0.812217316792107,100
|
||||
0.95,1.09,0.9294794520547945,0.958904109589041,0.08543716778698167,100,274030.97554696375,285147.23292353324,56892.76092575346,100,46.01,46.0,0.7452882297839849,100
|
||||
0.95,1.1,0.9287123287671233,0.9547945205479451,0.08003250087101145,100,274078.3527874601,283856.4572754771,56690.76457159778,100,45.64,46.0,0.6744994402884967,100
|
||||
0.95,1.11,0.9364383561643835,0.9561643835616438,0.06526268605058627,100,280881.6829739677,296677.6782650106,49506.88718738129,100,45.34,45.0,0.6391281940932124,100
|
||||
0.95,1.12,0.9556438356164384,0.973972602739726,0.07386818268433056,100,298727.4002688783,311051.6643517049,55230.95325025695,100,44.94,45.0,0.6327748733981471,100
|
||||
0.95,1.13,0.9625479452054795,0.9753424657534246,0.03853770121887277,100,303624.8432092603,305964.4471615079,33997.45186113061,100,44.56,45.0,0.6562827673397108,100
|
||||
0.95,1.14,0.965041095890411,0.9808219178082191,0.04503533313503058,100,309887.30047959444,316649.4679522059,37629.163123648126,100,44.27,44.0,0.6942039987778751,100
|
||||
1.0,1.05,0.878054794520548,0.9232876712328767,0.12456329754626162,100,239357.93264631205,263694.2998236382,80089.22781996857,100,47.79,48.0,0.8680059581790576,100
|
||||
1.0,1.06,0.8947123287671234,0.941095890410959,0.1355028323290669,100,255749.40900901722,285246.23294684856,94956.97950416044,100,47.34,47.0,0.7683118053909035,100
|
||||
1.0,1.07,0.9047945205479452,0.9438356164383561,0.13250530459875723,100,264039.6231237091,286009.00904912734,90347.19839737378,100,46.86,47.0,0.7787584005740479,100
|
||||
1.0,1.08,0.9243287671232876,0.952054794520548,0.08231970120182112,100,281449.77736003743,293460.12722831377,61529.54372494277,100,46.59,47.0,0.726065917924618,100
|
||||
1.0,1.09,0.9343013698630136,0.9643835616438357,0.08847012172074435,100,291842.87457335443,304680.33844471246,67944.88567969218,100,45.95,46.0,0.74366007223079,100
|
||||
1.0,1.1,0.9562739726027396,0.9753424657534246,0.05972417397215357,100,312685.41726779164,317915.94000983867,49197.89880376956,100,45.67,46.0,0.7792122414154682,100
|
||||
1.0,1.11,0.968958904109589,0.9808219178082191,0.03477533957582285,100,323801.7037949509,331782.3224302352,37926.45459276231,100,45.43,45.0,0.781800563578795,100
|
||||
1.0,1.12,0.9685479452054794,0.9835616438356164,0.04226908937517442,100,325205.9243724567,337201.9597184559,45250.733446382925,100,44.9,45.0,0.6890192121758836,100
|
||||
1.0,1.13,0.9677808219178082,0.9808219178082191,0.04733685887394322,100,330657.6603956923,337075.92965660174,42421.01611114322,100,44.6,45.0,0.7247430753394793,100
|
||||
1.0,1.14,0.9823013698630138,0.9917808219178083,0.02489902195487209,100,346636.5895811218,351643.50356452586,32724.192897340898,100,44.11,44.0,0.6947857747012907,100
|
||||
1.05,1.05,0.9096712328767123,0.9506849315068493,0.1268551724970295,100,275036.8994307617,290241.47330518905,92189.74314580947,100,47.76,48.0,0.8542230210530327,100
|
||||
1.05,1.06,0.9146575342465754,0.9561643835616438,0.12009700618062043,100,283383.00552277366,305048.07351176,96246.65543086598,100,47.27,47.0,0.7365631354644712,100
|
||||
1.05,1.07,0.9413972602739726,0.9698630136986301,0.09003060152571733,100,303722.93403703946,313331.6029302139,73057.08547382594,100,46.91,47.0,0.7533762390570582,100
|
||||
1.05,1.08,0.9469315068493152,0.9780821917808219,0.0793003279699756,100,317055.4136440441,329838.54958045797,63485.26929871185,100,46.59,47.0,0.7666666666666667,100
|
||||
1.05,1.09,0.9626575342465753,0.9835616438356164,0.050483002706923574,100,327783.50728198123,337811.29494201415,48868.03042293043,100,46.26,46.0,0.7603826787755086,100
|
||||
1.05,1.1,0.9624109589041097,0.9794520547945205,0.05435562429151819,100,331620.04862504784,339715.6067690905,51905.17082289278,100,45.78,46.0,0.7464393593398702,100
|
||||
1.05,1.11,0.9697534246575343,0.989041095890411,0.04971307496436453,100,340227.99533744523,351120.098823712,49956.8593361718,100,45.43,45.0,0.7420283421851632,100
|
||||
1.05,1.12,0.9702739726027397,0.9863013698630136,0.05158788657894017,100,343659.58516506077,353342.32752993377,44689.97958762292,100,45.05,45.0,0.7299508769967222,100
|
||||
1.05,1.13,0.9841643835616438,0.9917808219178083,0.021220325968317567,100,362923.71636342397,367782.8510066926,30511.492738523157,100,44.68,45.0,0.7089613971340485,100
|
||||
1.05,1.14,0.9803287671232878,0.989041095890411,0.03439019008566572,100,358818.66979266173,366030.73615185026,38779.97603060032,100,44.42,44.0,0.6693883835866954,100
|
||||
1.1,1.05,0.8774520547945205,0.9493150684931506,0.16332896109997266,100,262116.4533938322,307218.83635365096,137001.0438185493,100,47.65,48.0,0.845367650579594,100
|
||||
1.1,1.06,0.93,0.9575342465753425,0.08998299067660721,100,308184.9288191942,315262.4431269691,76531.77678714157,100,47.33,47.0,0.7114503609961922,100
|
||||
1.1,1.07,0.9332054794520548,0.9767123287671233,0.1133241844881452,100,316339.8582983121,343388.79801796563,88723.83399772066,100,47.18,47.0,0.7704124567628391,100
|
||||
1.1,1.08,0.9466849315068494,0.9767123287671233,0.09839032697603153,100,331184.26416084,349136.8427766626,81927.62325661813,100,46.52,47.0,0.8346050901867643,100
|
||||
1.1,1.09,0.9556164383561644,0.989041095890411,0.10547415073320952,100,344293.7967364825,363724.13265618257,85256.01192642892,100,46.26,46.0,0.7333333333333325,100
|
||||
1.1,1.1,0.9733150684931507,0.9945205479452055,0.06351837655542099,100,363147.94176298834,374326.82103307196,57145.65533432398,100,45.75,46.0,0.6256309946079563,100
|
||||
1.1,1.11,0.9746849315068493,0.9917808219178083,0.04402911492630584,100,363388.1927891669,373491.37867364136,49274.54501585131,100,45.5,45.0,0.6435381994422805,100
|
||||
1.1,1.12,0.9793972602739726,0.9917808219178083,0.03915141511936552,100,370890.05170730536,376338.38252097333,44302.65600471223,100,45.1,45.0,0.7035264706814484,100
|
||||
1.1,1.13,0.9804109589041096,0.9945205479452055,0.03243412017637617,100,377327.82974791917,381348.09184570936,38488.17279855358,100,44.76,45.0,0.7123726184201263,100
|
||||
1.1,1.14,0.9886849315068492,0.9945205479452055,0.017334789000443024,100,384809.10717216856,390844.7511152257,31093.991487524258,100,44.51,44.0,0.6589707309451781,100
|
||||
1.2,1.05,0.9171780821917808,0.9726027397260274,0.13067274741673798,100,322060.45665888296,340538.9200925699,105710.6766863121,100,47.92,48.0,0.8490041700769674,100
|
||||
1.2,1.06,0.9489041095890411,0.9917808219178083,0.09929085079019878,100,355803.9539068448,384192.0333909353,97019.18401447838,100,47.61,48.0,0.7771353768420244,100
|
||||
1.2,1.07,0.9564109589041097,0.9917808219178083,0.07656699649287621,100,364057.84548111144,389141.36283888033,75902.53849836365,100,47.31,47.0,0.6918720031095832,100
|
||||
1.2,1.08,0.9602465753424657,0.989041095890411,0.08505995544913891,100,373446.1741382272,386689.89061583555,83449.85958080231,100,46.81,47.0,0.6918720031095821,100
|
||||
1.2,1.09,0.9672054794520548,0.9945205479452055,0.08413140089715565,100,390837.72881569655,407689.56891719927,79174.94776761712,100,46.26,46.0,0.7194273817250479,100
|
||||
1.2,1.1,0.9738356164383561,0.9972602739726028,0.060117001280751294,100,398871.1618030684,414081.79698469874,60975.215674990715,100,46.23,46.0,0.6794977348874467,100
|
||||
1.2,1.11,0.9765205479452055,0.9972602739726028,0.05166994864627592,100,404329.81311479496,423147.71118130337,66661.79450110722,100,45.64,46.0,0.6744994402884964,100
|
||||
1.2,1.12,0.9849315068493151,0.9972602739726028,0.04728618617716756,100,424054.9128070699,431696.0971162426,53638.952963032265,100,45.25,45.0,0.7436600722307911,100
|
||||
1.2,1.13,0.991041095890411,0.9986301369863013,0.018392476408138413,100,428392.03453004366,434494.4380003613,39705.52165661862,100,44.91,45.0,0.6528105515090998,100
|
||||
1.2,1.14,0.992986301369863,1.0,0.01664151932365369,100,438627.18168542744,445798.6518808934,35926.80443305188,100,44.45,44.0,0.6871842709362769,100
|
||||
|
BIN
contrato.pdf
Normal file
BIN
contrato.pdf
Normal file
Binary file not shown.
258
course_syllabus.md
Normal file
258
course_syllabus.md
Normal file
|
|
@ -0,0 +1,258 @@
|
|||
# Applied Optimization Techniques
|
||||
|
||||
## Course goals
|
||||
|
||||
The goal of this course is to provide an introduction to simulation,
|
||||
optimization and machine learning techniques to students with a background in
|
||||
social sciences, with an approach biased towards practical work. The expected
|
||||
outcome is that students that have passed this course know a variety of modern
|
||||
and useful techniques that can be applied in real-life business contexts. With
|
||||
this knowledge and experience, the students understand what are the right
|
||||
techniques for different problems, which are the main steps and requirements to
|
||||
apply each of these techniques and how to judge the successful application of
|
||||
them.
|
||||
|
||||
Many of the techniques taught in this course are usually taught to engineering
|
||||
and technical profiles. This course does not aim to bring students to the same
|
||||
level of technical expertise as their engineering counterparts, but rather to
|
||||
provide enough background so that the students can successfully interact with
|
||||
such profiles. Having said that, this course can also be a first introduction
|
||||
for students that are willing to pursue a more thorough learning of the
|
||||
techniques discussed in the course, after or during itself.
|
||||
|
||||
With the knowledge and skills obtained in this course, students become fit for
|
||||
tasks such as:
|
||||
|
||||
- Applying simulation, optimization and machine learning techniques to simple
|
||||
cases.
|
||||
- Planning and designing simulation, optimization and machine learning
|
||||
initiatives.
|
||||
- Leading simulation, optimization and machine learning projects from a
|
||||
managerial point of view.
|
||||
- Acting as a liaison between management and technical profiles in business
|
||||
contexts.
|
||||
|
||||
## Pre-requisities
|
||||
|
||||
The course assumes the student has covered Mathematics I, II, III courses and
|
||||
the Probability & Statistics course. Passing this course is not impossible if
|
||||
that is not the case, but the student should expect a non-trivial challenge
|
||||
ahead.
|
||||
|
||||
Knowledge of the following topics will help students better leverage this
|
||||
course, but is not strictly required:
|
||||
|
||||
- Basic programming, specially in data oriented languages such as Python or R.
|
||||
- Operations research
|
||||
|
||||
## Teaching method and contents
|
||||
|
||||
The course will have lecture classes and practical seminars. Classes start on
|
||||
April 7th.
|
||||
|
||||
There will be 20 lecture classes and 6 practical seminars. For the practical
|
||||
seminars, students will be divided into two groups with independent sessions to
|
||||
reduce the class size. The practical seminars will be used to assist students
|
||||
in their work in the three mandatory case assignments that students will do
|
||||
throughout the course.
|
||||
|
||||
Students are expected to attend all the activities in the course. Beyond
|
||||
lectures and practical seminars, additional reading resources will be provided
|
||||
to students. For students that need to level up their Python skills, self-paced
|
||||
materials will be suggested.
|
||||
|
||||
Lectures will have the following contents:
|
||||
|
||||
| Week | Classes | Student work |
|
||||
|------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| 1 | - L1: Introduction and motivation of the course<br/> - L2: Simulation, Optimization and Machine Learning in companies | - Python prep |
|
||||
| 2 | - L3: Introduction to simulation: What is it, When do we use it, Types of simulation<br/> - L4: Simulation examples in Python. Introduction to case 1. | - Python prep<br/> - View [Primer: Simulating a pandemic](https://www.youtube.com/watch?v=7OLpKqTriio) <br/>- Read [Agent-based modeling: Methods and techniques for simulating human systems](https://www.pnas.org/content/99/suppl_3/7280) <br/> - Read case 1. |
|
||||
| 3 | - L5: Simulation methodology. <br/> - L6: Simulation-based optimization I. Challenges and issues with simulation. Where to go from here<br/> - S1: Workshop for case 1 | - Work on case 1 <br/> - Review [HASH model market simulation](https://hash.ai/@hash/model-market-python) <br/>- Review [HASH warehouse simulation](https://hash.ai/@hash/warehouse-logistics) |
|
||||
| 4 | - L7: Introduction to optimization<br/> - L8: Modeling optimization problems<br/> - S2: Workshop for case 1 | - Work on case 1 <br/> - Read Gurobi's [Modelling Basics](https://www.gurobi.com/resource/modeling-basics/) <br/> - Read Neos [taxonomy of optimization problems](https://neos-guide.org/optimization-tree) <br/> - View this video on the [Simplex algorithm](https://www.youtube.com/watch?v=RO5477EKlXE) |
|
||||
| 5 | - L9: Taxonomy of optimization techniques <br/> - L10: Simulation-based optimization II. Introduction to case 2 | - Deliver case 1 <br/> - Read case 2 <br/> - Enjoy watching [simulation-based race car training](https://www.youtube.com/watch?v=-sg-GgoFCP0) <br/> - Read how the [4th most popular database software in the world uses GAs to access data faster.](https://www.postgresql.org/docs/8.0/geqo-intro2.html) |
|
||||
| 6 | - L11: Challenges in real-world usage. Simulation vs Optimization <br/> - L12: Introduction to Machine Learning <br/> - S3: Workshop for case 2 | - Work on case 2 <br/> - Read this [review on simulation optimization techniques and softwares](https://arxiv.org/pdf/1706.08591.pdf) |
|
||||
| 7 | - L13: Supervised Machine Learning (SML): NIPS<br/> - L14: Typical SML workflow. Introduction to case 3<br/> - S4: Workshop for case 2 | - Work on case 2 <br/> - Read case 3 |
|
||||
| 8 | - L15: Algorithm deep dive: Decision trees<br/> - L16: Feature Engineering and Model Evaluation<br/> - S5: Workshop for case 3 | - Deliver case 2 <br/> - View this [intro to neural networks](https://www.youtube.com/watch?v=aircAruvnKk&t=10s) and this [intro to random forests](https://www.youtube.com/watch?v=J4Wdy0Wc_xQ) |
|
||||
| 9 | - L17: Deployment of Models <br/> - L18: Stories from the trenches: applying all of this in the real world<br/> - S6: Workshop for case 3 | - Work on case 3 <br/> - View this video on [why businesses fail at ML](https://www.youtube.com/watch?v=dRJGyhS6gA0) |
|
||||
| 10 | - L19: Where to go from here: further learning and carreer advice<br/> - L20: Final Q&A, exam preparation | - Work on case 3 |
|
||||
| 11 | - Exam | - Deliver case 3 | | |
|
||||
|
||||
- Lecture 1 INTRO
|
||||
- Introduction to the course
|
||||
- Citizenship rules
|
||||
- Won't force you to come, but I advice you to.
|
||||
- I'll always try to start 5min late, finish 5min late, and stop
|
||||
for 5min.
|
||||
- You can come and go, just please be respectful.
|
||||
|
||||
- Calendar
|
||||
- Contents
|
||||
- Expectations
|
||||
- The teacher
|
||||
- Evaluation
|
||||
- Contact
|
||||
- Questions?
|
||||
- The relevance of math and computers in management
|
||||
- Examples: pricing, logistics, staffing.
|
||||
- The skills and profiles required
|
||||
- The tools used
|
||||
- Lecture 2 INTRO
|
||||
- The techniques we will see in the course
|
||||
- Simulation
|
||||
- Optimization
|
||||
- Supervised machine learning (aka "prediction")
|
||||
- Why this stuff is important
|
||||
- Lecture 3 SIM
|
||||
- A humbling example
|
||||
- What is simulation and when do we use it
|
||||
- Different types of simulations
|
||||
- Lecture 4 SIM
|
||||
- Toy simulations in Python
|
||||
- How to approach simulation in practical terms
|
||||
- Tools in industry
|
||||
- Lecture 5 SIM
|
||||
- Theoretical background on simulation
|
||||
- Present case 1
|
||||
- Lecture 6 SIM
|
||||
- Simulation-based optimization
|
||||
- Where to go from here
|
||||
- Lecture 7 OPT
|
||||
- What is optimization
|
||||
- A trivial example
|
||||
- Lecture 8 OPT
|
||||
- Different optimization techniques
|
||||
- Present case 3
|
||||
- Lecture 9 OPT
|
||||
- How to model optimization problems (target functions, decision variables
|
||||
and constraints)
|
||||
- Lecture 10 OPT
|
||||
- Simulation-based optimization: Genetic algorithms
|
||||
- Lecture 11 OPT
|
||||
- Real world challenges and optimization deployment
|
||||
- Lecture 12 ML
|
||||
- Good news, you already know Machine Learning
|
||||
- Different branches of Machine Learning
|
||||
- Real world examples of applications
|
||||
- Lecture 13 ML
|
||||
- How does Supervised Machine Learning work?
|
||||
- Present case 2
|
||||
- Lecture 14 ML
|
||||
- The Machine Learning workflow (EDA, Feature Engineering, Model
|
||||
Evaluation, Deployment)
|
||||
- Lecture 15 ML
|
||||
- Feature Engineering
|
||||
- Lecture 16 ML
|
||||
- Model evaluation
|
||||
- Lecture 17 ML
|
||||
- Deployment and real world challenges
|
||||
- Lecture 18 Real life stories from the trenches
|
||||
- Lecture 19 Real life stories from the trenches
|
||||
- Lecture 20
|
||||
- Q&A pre-exam
|
||||
- Feedback on the course
|
||||
|
||||
## Case details
|
||||
|
||||
Case 1
|
||||
|
||||
- Title: Choosing stock policies under uncertainty
|
||||
- Description: Students role-play their participation as consultants in a
|
||||
project for Beanie Limited, a coffee beans roasting company. Elisa, the
|
||||
regional manager for the italian region, is not happy with their inventory
|
||||
policies for raw beans. The students are asked to analyse the problems posed
|
||||
by Elisa and apply simulation techniques, together with real data, to
|
||||
recommend a stock policy for the company's warehouse in the italian region.
|
||||
Python notebooks with some helpful prepared functions are provided to the
|
||||
students. The final delivery is a report with their recommendation to the
|
||||
client company, along with the used code.
|
||||
|
||||
Case 2 candidate
|
||||
|
||||
- Title: ?
|
||||
- Description: ?
|
||||
- Sample idea: https://www.gurobi.com/resource/facility-location-problem/
|
||||
|
||||
Case 3
|
||||
|
||||
- Title: Improving last-mile scheduling with Machine Learning
|
||||
- Description: Students role-play their participation as consultants in a
|
||||
project for Beanie Limited, a coffee beans roasting company. Pieter, the
|
||||
director of secondary transportation, has requested help from the student
|
||||
consultants. One of the key activities in Pieter's team is the daily
|
||||
scheduling, where the different trucks get assigned which deliveries and
|
||||
routes will perform. The students are asked to develop a machine-learning
|
||||
algorithm to predict the drop-time for each delivery (the drop-time is the
|
||||
time a driver takes in unloading the goods in a a client location. More
|
||||
informally, the time that passes since he removes the key from the truck
|
||||
until he starts the engine again). The goal is to provide more advanced
|
||||
information for Pieter's schedulers so they can better plan the routes of
|
||||
their drivers. The students are asked to build and deliver a Machine Learning
|
||||
algorithm that predicts this time. The students will be provided a labelled
|
||||
dataset. The final delivery is the working prediction model, along with a
|
||||
report explaining their methodology in building it, and answering some
|
||||
business questions to the client company.
|
||||
|
||||
## Grading
|
||||
|
||||
The following items compose the final grade:
|
||||
|
||||
- Case assignments: 50% of the grade. There will be three assignments, each
|
||||
with the same weight. The average grade of the assignments must be of 5 or
|
||||
more to pass the course.
|
||||
- Final exam: 40% of the grade. There will be a final exam at the end of the
|
||||
course. The grade must be of 5 or more to pass the course.
|
||||
- Something else? 10%.
|
||||
|
||||
A final grade is calculated as:
|
||||
|
||||
<!-- @formatter:off -->
|
||||
|
||||
```python
|
||||
if avg(case1_grade, case2_grade, case3_grade) < 5:
|
||||
passed_course = False
|
||||
if final_exam_grade < 5:
|
||||
passed course = False
|
||||
|
||||
passed_course = True
|
||||
final_grade = (avg(case1_grade, case2_grade, case3_grade) + final_exam_grade) / 2
|
||||
```
|
||||
|
||||
<!-- @formatter:on -->
|
||||
|
||||
## Bibliography
|
||||
|
||||
All compulsory and required materials will be provided during the course.
|
||||
|
||||
A good book that follows the approach of this course is "Guttag, John.
|
||||
Introduction to Computation and Programming Using Python: With Application to
|
||||
Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624", used in the
|
||||
homonymous course at MIT. It is not compulsory to use this book, but some
|
||||
students might find it helpful.
|
||||
|
||||
Additional specific readings will be provided throughout the course. Students
|
||||
will be requested to read some of these materials in advance of some sessions.
|
||||
|
||||
For students that want to dive deeper in the topics covered in the course, the
|
||||
following books are recommended:
|
||||
|
||||
- On simulation: Louis G. Birta Gilbert Arbez, Modelling and Simulation.
|
||||
Springer 2019 ISBN: 978-3-030-18869-6 or Law A., Kelton D., Simulation and
|
||||
Modelling Analysis, Second Edition, McGraw-Hill, ISBN: 978-0071165372
|
||||
- On optimization: pedir recomendación a Helena.
|
||||
- On machine learning: Hastie T., Tibshirani R., Friedman J., The Elements Of
|
||||
Statistical Learning: Data Mining, Inference, And Prediction, Second Edition
|
||||
ISBN: 978-0387848570
|
||||
|
||||
## Cool ideas & notes
|
||||
|
||||
- Hold a Kaggle competition with the students. Winners come to spend a morning
|
||||
in Accenture.
|
||||
- Start every lecture with a fun fact.
|
||||
- Let them choose the challenge for one of the practical labs.
|
||||
- Should I have office hours?
|
||||
- Will the classes be recorded?
|
||||
- What's are the policies on:
|
||||
- Late deliveries
|
||||
- Not attending exam
|
||||
- Re-takes
|
||||
- https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016
|
||||
19
random_notes.md
Normal file
19
random_notes.md
Normal file
|
|
@ -0,0 +1,19 @@
|
|||
Classroom for lectures -> 20.047
|
||||
Classroom for seminars -> 40.501
|
||||
Despachos asociados -> 40.171 i 40.173
|
||||
|
||||
# Before starting doubts
|
||||
|
||||
- How do I build the course plan? How do I upload it?
|
||||
- What should I expect from Sira?
|
||||
- Office hours?
|
||||
- Metodos para evaluarme a mi?
|
||||
- Sobre las clases
|
||||
- Portatil propio o usar el PC del aula?
|
||||
- Microfono?
|
||||
- Grabar?
|
||||
- Mascarilla?
|
||||
|
||||
Case 1
|
||||
Python colab resources
|
||||
Primer deck
|
||||
6
upf_systems.md
Normal file
6
upf_systems.md
Normal file
|
|
@ -0,0 +1,6 @@
|
|||
u208070
|
||||
el telefono con la colilla corporativa
|
||||
pablo.martinc@upf.edu
|
||||
|
||||
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue