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Nested partitions for the large-scale extended job shop scheduling problem

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  • Hoksung Yau
  • Leyuan Shi

Abstract

This paper addresses the large-scale extended job shop scheduling problem while considering the bill of material and the working shifts constraints. We propose two approaches for the problem. One is based on dispatching rules (DR), and the other is an application of the Nested Partitions (NP) Framework. A sampling approach for the exact feasible subregion is developed to complete the NP method. Furthermore, to efficiently search each subregion, a weighted sampling approach is also presented. Computational experiments show that the NP method with weighted sampling can find good solutions for most large-scale extended job shop scheduling problems. Copyright Springer Science+Business Media, LLC 2009

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  • Hoksung Yau & Leyuan Shi, 2009. "Nested partitions for the large-scale extended job shop scheduling problem," Annals of Operations Research, Springer, vol. 168(1), pages 23-39, April.
    • Handle: RePEc:spr:annopr:v:168:y:2009:i:1:p:23-39:10.1007/s10479-008-0370-x
    DOI: 10.1007/s10479-008-0370-x
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    References listed on IDEAS

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    1. Peter J. M. van Laarhoven & Emile H. L. Aarts & Jan Karel Lenstra, 1992. "Job Shop Scheduling by Simulated Annealing," Operations Research, INFORMS, vol. 40(1), pages 113-125, February.
    2. Egon Balas, 1969. "Machine Sequencing Via Disjunctive Graphs: An Implicit Enumeration Algorithm," Operations Research, INFORMS, vol. 17(6), pages 941-957, December.
    3. Leyuan Shi & Sigurdur Ólafsson, 2000. "Nested Partitions Method for Global Optimization," Operations Research, INFORMS, vol. 48(3), pages 390-407, June.
    4. Joseph Adams & Egon Balas & Daniel Zawack, 1988. "The Shifting Bottleneck Procedure for Job Shop Scheduling," Management Science, INFORMS, vol. 34(3), pages 391-401, March.
    5. J. Carlier & E. Pinson, 1989. "An Algorithm for Solving the Job-Shop Problem," Management Science, INFORMS, vol. 35(2), pages 164-176, February.
    6. R. J. M. Vaessens & E. H. L. Aarts & J. K. Lenstra, 1996. "Job Shop Scheduling by Local Search," INFORMS Journal on Computing, INFORMS, vol. 8(3), pages 302-317, August.
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    Cited by:

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    3. Choi, Hyunhong & Koo, Yoonmo, 2018. "Using Contingent Valuation and Numerical Methods to Determine Optimal Locations for Environmental Facilities: Public Arboretums in South Korea," Ecological Economics, Elsevier, vol. 149(C), pages 184-201.
    4. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Some results of the worst-case analysis for flow shop scheduling with a learning effect," Annals of Operations Research, Springer, vol. 211(1), pages 481-490, December.
    5. Lin-Hui Sun & Kai Cui & Ju-Hong Chen & Jun Wang & Xian-Chen He, 2013. "Research on permutation flow shop scheduling problems with general position-dependent learning effects," Annals of Operations Research, Springer, vol. 211(1), pages 473-480, December.

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