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Variable Neighbourhood Search and Mathematical Programming for Just-in-Time Job-Shop Scheduling Problem

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  • Sunxin Wang
  • Yan Li

Abstract

This paper presents a combination of variable neighbourhood search and mathematical programming to minimize the sum of earliness and tardiness penalty costs of all operations for just-in-time job-shop scheduling problem (JITJSSP). Unlike classical E/T scheduling problem with each job having its earliness or tardiness penalty cost, each operation in this paper has its earliness and tardiness penalties, which are paid if the operation is completed before or after its due date. Our hybrid algorithm combines (i) a variable neighbourhood search procedure to explore the huge feasible solution spaces efficiently by alternating the swap and insertion neighbourhood structures and (ii) a mathematical programming model to optimize the completion times of the operations for a given solution in each iteration procedure. Additionally, a threshold accepting mechanism is proposed to diversify the local search of variable neighbourhood search. Computational results on the 72 benchmark instances show that our algorithm can obtain the best known solution for 40 problems, and the best known solutions for 33 problems are updated.

Suggested Citation

  • Sunxin Wang & Yan Li, 2014. "Variable Neighbourhood Search and Mathematical Programming for Just-in-Time Job-Shop Scheduling Problem," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-9, April.
    • Handle: RePEc:hin:jnlmpe:431325
    DOI: 10.1155/2014/431325
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    Cited by:

    1. Ahmadian, Mohammad Mahdi & Salehipour, Amir & Cheng, T.C.E., 2021. "A meta-heuristic to solve the just-in-time job-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 288(1), pages 14-29.

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