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Setting inventory levels of CONWIP flow lines via linear programming

Author

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  • Helber, Stefan
  • Schimmelpfeng, Katja
  • Stolletz, Raik

Abstract

This paper treats the problem of setting the inventory level of closed-loop flow lines operating under the constant-work-in-process (CONWIP) protocol. We solve a huge but simple linear program that models an entire simulation run of a closed-loop flow line in discrete time to determine a production rate estimate of the system. This new approach has been introduced in Helber et al. (2008) for open flow lines with limited buffer capacities. In this paper we present numerical results of the method for closed-loop CONWIP flow lines. The first part of the numerical study deals with the accuracy of the method. In the second part, we focus on the relationship between the CONWIP inventory level and the short-term profit. In our numerical investigation we consider both limited and unlimited local buffer capacities between the machines.

Suggested Citation

  • Helber, Stefan & Schimmelpfeng, Katja & Stolletz, Raik, 2009. "Setting inventory levels of CONWIP flow lines via linear programming," Hannover Economic Papers (HEP) dp-436, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-436
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    References listed on IDEAS

    as
    1. Stefan Helber & Katja Schimmelpfeng & Raik Stolletz & Svenja Lagershausen, 2011. "Using linear programming to analyze and optimize stochastic flow lines," Annals of Operations Research, Springer, vol. 182(1), pages 193-211, January.
    2. George Harhalakis & Jean‐Marie Proth, 1993. "Manufacturing systems," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 9(2), pages 85-86, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    CONWIP; flow lines; random processing times; performance evaluation; buffer allocation; linear programming; simulation.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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