Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 97, Issue -, Pages 170-182Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2016.05.001
Keywords
Parallel machine scheduling; Job splitting; Learning effect; Branch-and-bound; Greedy search
Funding
- National Natural Science Foundation of China [71371106]
- State Key Program of National Natural Science of China
- Ministry of Science and Technology Project, China [2014IM010100]
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This paper examines parallel machine scheduling with the objective of minimizing total completion time considering job splitting and learning. This study is motivated by real situations in labor-intensive industry, where learning effects take place and managers need to make decisions to split and assign orders to parallel production teams. Firstly, some analytical properties which are efficient at reducing complexity of the problem are presented. Utilizing the analytical property of the problem, a branch-and-bound algorithm which is efficient at solving small-sized problems is proposed. For the large-sized problems, several constructive heuristics and meta-heuristics are presented. Among them, the greedy search, which can take both the current profit and future cost after splitting a job into consideration, obtains a near optimal solution for the small sized problems and performs best in all proposed heuristics for the large sized problems. Finally, extensive numerical experiments are conducted to test the performance of the proposed methods. (C) 2016 Elsevier Ltd. All rights reserved.
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