DOI: 10.1093/comjnl/bxag111 ISSN: 0010-4620

Flow shop scheduling with makespan objective and general position weighted learning effects

Li-Han Zhang, Dan-Yang Lv, Ji-Bo Wang
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Abstract

This article addresses the permutation flow shop problem of minimizing the makespan of all jobs with general position weighted learning effects. Since this problem is NP-hard, we propose two simple heuristics and analyze their worst-case bounds. The lower and upper bounds are given, and a branch-and-bound algorithm and two complex heuristics (including simulated annealing and tabu search) are proposed to solve this problem. Computational results show that our algorithms can generate effective and efficient schedules.

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