1. Aprender
    2. /
  2. Cursos
    3. /
  3. Supply Chain Analytics in Python
  • 1

    Basics of supply chain optimization and PuLP

    Gratuito

    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.

  • 2

    Modeling in PuLP

    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.

  • 3

    Solve and evaluate model

    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.

  • 4

    Sensitivity and simulation testing of model

    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.

Initializing

Exercicio

Logistics planning problem

You are consulting for kitchen oven manufacturer helping to plan their logistics for next month. There are two warehouse locations (New York, and Atlanta), and four regional customer locations (East, South, Midwest, West). The expected demand next month for East it is 1,800, for South it is 1,200, for the Midwest it is 1,100, and for West it is 1000. The cost for shipping each of the warehouse locations to the regional customer's is listed in the table below. Your goal is to fulfill the regional demand at the lowest price.

Customer New York Atlanta
East $211 $232
South $232 $212
Midwest $240 $230
West $300 $280

Two Python dictionaries costs and var_dict have been created for you containing the costs and decision variables of the model. You can explore them in the console.

Instruções 1/4

undefined XP
    1
    2
    3
    4
  • Initialize the model.