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Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming

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  • José Emmanuel Gómez-Rocha
  • Eva Selene Hernández-Gress
  • Héctor Rivera-Gómez

Abstract

In this article two multi-stage stochastic linear programming models are developed, one applying the stochastic programming solver integrated by Lingo 17.0 optimization software that utilizes an approximation using an identical conditional sampling and Latin-hyper-square techniques to reduce the sample variance, associating the probability distributions to normal distributions with defined mean and standard deviation; and a second proposed model with a discrete distribution with 3 values and their respective probabilities of occurrence. In both cases, a scenario tree is generated. The models developed are applied to an aggregate production plan (APP) for a furniture manufacturing company located in the state of Hidalgo, Mexico, which has important clients throughout the country. Production capacity and demand are defined as random variables of the model. The main purpose of this research is to determine a feasible solution to the aggregate production plan in a reasonable computational time. The developed models were compared and analyzed. Moreover, this work was complemented with a sensitivity analysis; varying the percentage of service level, also, varying the stochastic parameters (mean and standard deviation) to test how these variations impact in the solution and decision variables.

Suggested Citation

  • José Emmanuel Gómez-Rocha & Eva Selene Hernández-Gress & Héctor Rivera-Gómez, 2021. "Production planning of a furniture manufacturing company with random demand and production capacity using stochastic programming," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-26, June.
    • Handle: RePEc:plo:pone00:0252801
    DOI: 10.1371/journal.pone.0252801
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    References listed on IDEAS

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    1. Rüdiger Schultz, 1993. "Continuity Properties of Expectation Functions in Stochastic Integer Programming," Mathematics of Operations Research, INFORMS, vol. 18(3), pages 578-589, August.
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