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A Search Decision Rule for the Aggregate Scheduling Problem

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  • William H. Taubert

    (University of California, Los Angeles)

Abstract

This paper proposes a search decision rule (SDR) approach to the aggregate scheduling problem. The approach is tested by converting the objective function of the classic Holt, Modigliani, Muth and Simon paint factory scheduling problem into a 20 dimension response surface which is then explored by the SDR. Conjugate gradient, variable metric and pattern search methods are tested. All three methods demonstrate the ability to consistently descend to the neighborhood of the minimum. The pattern search technique is selected for incorporation into the SDR heuristic because of its excellent performance in terms of reducing the computation time required. Production and work force decisions made up by the SDR are comparable to those made by the linear decision rule and total costs are within 0.1%. By means of the search decision rule approach it should be possible to eliminate the restrictions imposed by linear and quadratic cost models and thereby pursue a more general and realistic approach to the aggregate scheduling problem.

Suggested Citation

  • William H. Taubert, 1968. "A Search Decision Rule for the Aggregate Scheduling Problem," Management Science, INFORMS, vol. 14(6), pages 343-359, February.
    • Handle: RePEc:inm:ormnsc:v:14:y:1968:i:6:p:b343-b359
    DOI: 10.1287/mnsc.14.6.B343
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    Cited by:

    1. 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.
    2. Yongjian Li & Jian Chen & Xiaoqiang Cai, 2007. "An integrated staff-sizing approach considering feasibility of scheduling decision," Annals of Operations Research, Springer, vol. 155(1), pages 361-390, November.
    3. Smitabhindu, R. & Janjai, S. & Chankong, V., 2008. "Optimization of a solar-assisted drying system for drying bananas," Renewable Energy, Elsevier, vol. 33(7), pages 1523-1531.
    4. Yasser A. Davizón & César Martínez-Olvera & Rogelio Soto & Carlos Hinojosa & Piero Espino-Román, 2015. "Optimal Control Approaches to the Aggregate Production Planning Problem," Sustainability, MDPI, vol. 7(12), pages 1-16, December.
    5. Buxey, Geoff, 2003. "Strategy not tactics drives aggregate planning," International Journal of Production Economics, Elsevier, vol. 85(3), pages 331-346, September.
    6. 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.
    7. Wang, Reay-Chen & Liang, Tien-Fu, 2005. "Applying possibilistic linear programming to aggregate production planning," International Journal of Production Economics, Elsevier, vol. 98(3), pages 328-341, December.
    8. Logan, Samuel H., 1984. "An Annual Planning Model for Food Processing: An Example of the Tomato Industry," Research Reports 251942, University of California, Davis, Giannini Foundation.

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