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Production Switching Heuristics for the Aggregate Planning Problem

Author

Listed:
  • Joseph M. Mellichamp

    (University of Alabama)

  • Robert M. Love

    (Army Logistics Management Center, Fort Lee)

Abstract

A number of approaches to the aggregate planning problem have been proposed in the literature, yet experience suggests that industrial concerns seldom use these models in actual planning situations. This paper describes a modified random walk production-inventory heuristic for the problem which should appeal to managers on the basis of simplicity as well as efficiency. The proposed approach is contrasted with linear programming, parametric programming, and linear decision rule optimal and near optimal solutions for several well known production situations. The simple production switching heuristic produces schedules which exceed optimal schedules by only 1 to 2 percent of total production costs in all cases.

Suggested Citation

  • Joseph M. Mellichamp & Robert M. Love, 1978. "Production Switching Heuristics for the Aggregate Planning Problem," Management Science, INFORMS, vol. 24(12), pages 1242-1251, August.
    • Handle: RePEc:inm:ormnsc:v:24:y:1978:i:12:p:1242-1251
    DOI: 10.1287/mnsc.24.12.1242
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    Cited by:

    1. E J Levin & Y Ma & R E Wright, 2004. "Profit maximization in a multi-product firm with impatient customers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(3), pages 211-218, March.
    2. Buxey, Geoff, 2003. "Strategy not tactics drives aggregate planning," International Journal of Production Economics, Elsevier, vol. 85(3), pages 331-346, September.
    3. Wu, Chia-Chin & Chang, Ni-Bin, 2004. "Corporate optimal production planning with varying environmental costs: A grey compromise programming approach," European Journal of Operational Research, Elsevier, vol. 155(1), pages 68-95, May.
    4. Gomes da Silva, Carlos & Figueira, José & Lisboa, João & Barman, Samir, 2006. "An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming," Omega, Elsevier, vol. 34(2), pages 167-177, April.

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