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Bicriterion Optimization for Flow Shop with a Learning Effect Subject to Release Dates

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  • Ji-Bo Wang
  • Jian Xu
  • Jing Yang

Abstract

This paper investigates a two-machine flow shop problem with release dates in which the job processing times are variable according to a learning effect. The bicriterion is to minimize the weighted sum of makespan and total completion time subject to release dates. We develop a branch-and-bound (B&B) algorithm to solve the problem by using a dominance property, several lower bounds, and an upper bound to speed up the elimination process of the search tree. We further propose a multiobjective memetic algorithm (MOMA), enhanced by an initialization strategy and a global search strategy, to obtain the Pareto front of the problem. Computational experiments are also carried out to examine the effectiveness and the efficiency of the B&B algorithm and the MOMA algorithm.

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  • Ji-Bo Wang & Jian Xu & Jing Yang, 2018. "Bicriterion Optimization for Flow Shop with a Learning Effect Subject to Release Dates," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    • Handle: RePEc:hin:complx:9149510
    DOI: 10.1155/2018/9149510
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    Cited by:

    1. Qian, Jin & Lin, Hexiang & Kong, Yufeng & Wang, Yuansong, 2020. "Tri-criteria single machine scheduling model with release times and learning factor," Applied Mathematics and Computation, Elsevier, vol. 387(C).

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