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Annals of Operations Research

, Volume 182, Issue 1, pp 193–211 | Cite as

Using linear programming to analyze and optimize stochastic flow lines

  • Stefan HelberEmail author
  • Katja Schimmelpfeng
  • Raik Stolletz
  • Svenja Lagershausen
Article

Abstract

This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines.

Keywords

Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line 
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

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Stefan Helber
    • 1
    Email author
  • Katja Schimmelpfeng
    • 2
  • Raik Stolletz
    • 3
  • Svenja Lagershausen
    • 4
  1. 1.Institut für ProduktionswirtschaftLeibniz Universität HannoverHannoverGermany
  2. 2.Lehrstuhl ABWL und Besondere des Rechnungswesens und ControllingBrandenburgische Technische Universität CottbusCottbusGermany
  3. 3.Department of Management Engineering/Operations ManagementTechnical University of DenmarkKgs. LyngbyDenmark
  4. 4.Seminar für Supply Chain Management und ProduktionUniversität zu KölnKölnGermany

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