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Using a Monte Carlo Simulation Exercise to Teach Principles of Distribution: An Enhanced Version of the Classic Transportation Problem

Author

Listed:
  • David Weltman

    (Information Systems and Supply Chain Management, Neeley School of Business, Texas Christian University, Fort Worth, Texas 76129)

  • Travis Tokar

    (Information Systems and Supply Chain Management, Neeley School of Business, Texas Christian University, Fort Worth, Texas 76129)

Abstract

This paper explains a Monte Carlo simulation workshop applied to an extended version of the classic transportation problem. It is designed to be conducted in a classroom or laboratory where students have access to a Monte Carlo simulation tool, such as Oracle Crystal Ball. The hands-on exercise builds on the classic transportation problem by allowing students to develop cost-efficient solutions when demands are uncertain and follow multiple types of patterns. Students develop a distribution plan by considering transportation, inventory-holding, and stock-out costs. Through simulation, students are able to see the consequences of their proposed policies and revise them until reaching a satisfactory solution. The Monte Carlo method is deployed because traditional deterministic optimization models do not exist for our scenario that we believe to be realistic and widely applicable. Students gain valuable experience using an important modeling tool applied to a classic operations-management problem.

Suggested Citation

  • David Weltman & Travis Tokar, 2019. "Using a Monte Carlo Simulation Exercise to Teach Principles of Distribution: An Enhanced Version of the Classic Transportation Problem," INFORMS Transactions on Education, INFORMS, vol. 19(3), pages 111-120, May.
    • Handle: RePEc:inm:orited:v:19:y:2019:i:3:p:111-120
    DOI: 10.1287/ited.2018.0200
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    References listed on IDEAS

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    3. Engle, R. F. & Granger, C. W. J. & Hallman, J. J., 1989. "Merging short-and long-run forecasts : An application of seasonal cointegration to monthly electricity sales forecasting," Journal of Econometrics, Elsevier, vol. 40(1), pages 45-62, January.
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