Oefening gevoeligheidsanalyse casestudy

Ga verder met de casestudy van het Capacitated Plant Location-model van een autofabrikant. Het model is al voor je opgelost en de schaduwprijzen en slackwaarden van de restricties zijn naar de console geprint. Let op: de invoerkosten van het model zijn in (duiz. $US) en de productievraag en -capaciteit in (duiz. auto’s). Bekijk de console en kies de ware uitspraak.

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Supply Chain Analytics in Python

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Supply Chain Analytics in Python

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Linear Programming (LP) is a key technique for Supply Chain Optimization. The PuLP framework is an easy to use tool for working with LP problems and allows the programmer to focus on modeling. In this chapter we learn the basics of LP problems and start to learn how to use the PuLP framework to solve them.

Exercise 1: Basics of optimization Exercise 2: To LP, or to not IP?Exercise 3: Choosing exercise routine Exercise 4: Basics of PuLP modeling Exercise 5: Getting started with LpProblem()Exercise 6: Simple resource scheduling exercise Exercise 7: Using lpSum Exercise 8: Trying out lpSum Exercise 9: Logistics planning problem

In this chapter we continue to learn how to model LP and IP problems in PuLP. We touch on how to use PuLP for large scale problems. Additionally, we begin our case study example on how to solve the Capacitated Plant location model.

Exercise 1: LpVariable dictionary function Exercise 2: Logistics planning problem 2 Exercise 3: Traveling salesman problem (TSP)Exercise 4: Example of a scheduling problem Exercise 5: Scheduling workers problem Exercise 6: Preventative maintenance scheduling Exercise 7: Capacitated plant location - case study P1 Exercise 8: Review data for case study Exercise 9: Decision variables of case study Exercise 10: Objective function of case study Exercise 11: Logical constraints Exercise 12: Logical constraint exercise Exercise 13: Logical constraints exercise 2

This chapter reviews some common mistakes made when creating constraints, and step through the process of solving the model. Once we have a solution to our LP model, how do we know if it is correct? In this chapter we also review a process for reasonableness checking or sanity checking the results. Furthermore, we continue working through our case study example on the Capacitated Plant location model by completing all the needed constraints.

Exercise 1: Common constraint mistakes Exercise 2: Dependent demand constraint exercise Exercise 3: Constraint combination exercise Exercise 4: Capacitated plant location - case study P2 Exercise 5: Constraints of case study exercise Exercise 6: Adding logical constraint in case study exercise Exercise 7: Solve the PuLP model Exercise 8: Choose the model status exercise Exercise 9: Solving production plan exercise Exercise 10: Sanity checking the solution Exercise 11: Reviewing model specification exercise Exercise 12: Sanity checking exercise

In our final chapter we review sensitivity analysis of constraints through shadow prices and slack. Additionally, we look at simulation testing our LP models. These different techniques allow us to answer different business-related questions about our models, such as available capacity and incremental costs. Finally, we complete our case study exercise and focus on using sensitivity analysis and simulation testing to answer questions about our model.

Exercise 1: Gevoeligheidsanalyse met schaduwprijzen Exercise 2: Oefening gevoeligheidsanalyse Exercise 3: Oefening met schaduwprijs en speling pt1 Exercise 4: Oefening schaduwprijs en slack, deel 2 Exercise 5: Capacitated plant location - casestudy P3 Exercise 6: De model-casestudy oplossen Exercise 7: Oefening gevoeligheidsanalyse casestudy

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Exercise 8: Oplossing voor simulatie testen Exercise 9: Oefening: oplossing voor simulatie testen Exercise 10: Wat is het risico-oefening Exercise 11: Gefaciliteerde locatiewaardebepaling fabriek - casestudy P4 Exercise 12: Simulatietest van capaciteitsmodel Exercise 13: Resultaten van simulatie interpreteren oefening Exercise 14: Eindoverzicht