Annals of Operations Research

, Volume 43, Issue 6, pp 309–335 | Cite as

Production planning via scenario modelling

  • Laureano F. Escudero
  • Pasumarti V. Kamesam
  • Alan J. King
  • Roger J-B. Wets
Section VI Stochastic Programming


Several Linear Programming (LP) and Mixed Integer Programming (MIP) models for the production and capacity planning problems with uncertainty in demand are proposed. In contrast to traditional mathematical programming approaches, we use scenarios to characterize the uncertainty in demand. Solutions are obtained for each scenario and then these individual scenario solutions are aggregated to yield a nonanticipative or implementable policy. Such an approach makes it possible to model nonstationarity in demand as well as a variety of recourse decision types. Two scenario-based models for formalizing implementable policies are presented. The first model is a LP model for multi-product, multi-period, single-level production planning to determine the production volume and product inventory for each period, such that the expected cost of holding inventory and lost demand is minimized. The second model is a MIP model for multi-product, multi-period, single-level production planning to help in sourcing decisions for raw materials supply. Although these formulations lead to very large scale mathematical programming problems, our computational experience with LP models for real-life instances is very encouraging.


Mathematical Programming Product Inventory Mixed Integer Programming Linear Programming Model Capacity Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© J.C. Baltzer AG, Science Publishers 1993

Authors and Affiliations

  • Laureano F. Escudero
    • 1
  • Pasumarti V. Kamesam
    • 2
  • Alan J. King
    • 2
  • Roger J-B. Wets
    • 2
  1. 1.UITESA and Mathematical Sciences SchoolCompultense University of MadridMadridSpain
  2. 2.IBM T.J. Watson Research CenterYorktown HeightsUSA

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