Journal
COMPUTERS & OPERATIONS RESEARCH
Volume 74, Issue -, Pages 175-186Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2016.04.021
Keywords
Order scheduling; Simulated annealing; Particle swarm optimization method; Tardiness; Learning effect
Categories
Funding
- National Natural Science Foundation of China [71301022, 11561036]
- Ministry of Science Technology (MOST) of Taiwan [NSC 102-2221-E-035-070-MY3, MOST 103-2410-H-035-022-MY2]
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The order scheduling problem is receiving increasing attention in the relatively new but creative area of scheduling research. In order scheduling, several orders are processed on multiple machines, and each order comprises multiple components. The order completion time is defined as the time at which all components in an order are completed. In previous studies, the processing times of all components were fixed in order scheduling problems. This is unreasonable because a steady decline in processing time usually occurs when the same task is performed repeatedly in practical situations. Therefore, we propose a multiple-machine order scheduling problem with a learning effect to minimize the total tardiness. We develop a branch-and-bound algorithm incorporating certain dominance rules and three lower bounds for obtaining the optimal solution. Subsequently, we propose simulated annealing, particle swarm optimization, and order-scheduling MDD algorithms for obtaining a near-optimal solution. In addition, the experimental results of all proposed algorithms are provided. (C) 2016 Elsevier Ltd. All rights reserved.
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