Ant colony optimization algorithm for total weighted completion time minimization on non-identical batch machines

• Published in: Computers & operations research Vol. 117; pp. 104889 - 19
• Main Authors: Zhang, Han, Jia, Zhao-hong, Li, Kai
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2020; Pergamon Press Inc
• Subjects: Algorithms; Ant colony optimization; Ant colony optimization (ACO); Arbitrary capacities; Batch processing; Completion time; Lower bounds; Operations research; Optimization; Parallel batch machines; Ratios; Scheduling; The total weighted completion time; Parallel batch machines; Scheduling; Arbitrary capacities; Ant colony optimization (ACO); The total weighted completion time
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2020.104889

•A new strategy of first job selection is designed to construct solutions.•Two algorithms based on ACO are presented to address the studied problem.•A job-swap based local optimization strategy is proposed to improve solution quality. This paper aims at the problem of scheduling a set of jobs with arbitrary sizes on parallel batch processing machines with arbitrary capacities. The optimization objective is to minimize the total weighted completion time of jobs. After describing the studied problem, we analyze its complexity and present a lower bound of the problem. A heuristic is provided to solve the problem firstly. Then, with the proposed first job selection strategy based on the weights of jobs, two algorithms based on the ant system and the max-min ant system, respectively, are designed to address the problem. Through extensive experiments, the performance of the proposed algorithms is compared with several state-of-the-art algorithms. The comparative results verify effectiveness and efficiency of the proposed algorithms.