Scheduling Unrelated Parallel Machine to Minimize Total Weighted Tardiness Using Ant Colony Optimization

• Published in: 2007 IEEE International Conference on Automation and Logistics pp. 132 - 136
• Main Authors: Hong Zhou, Zhengdao Li, Xuejing Wu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2007
• Subjects: Ant colony optimization; Automation; Conference management; Environmental economics; heuristics and ant colony optimization; Job shop scheduling; Logistics; Parallel machines; Processor scheduling; Production; scheduling; Single machine scheduling; total weighted tardiness; unrelated parallel machine
• ISBN: 1424415314; 9781424415311; 1424415306; 9781424415304
• ISSN: 2161-8151
• DOI: 10.1109/ICAL.2007.4338544

Parallel machine problem is a typical scheduling problem with wide applications in practice. As for the scheduling criteria, the total weighted tardiness is always regarded as one of the most important criteria in real situations. The problem of scheduling a given set of independent jobs on unrelated parallel machines to minimize the total weighted tardiness is studied in this paper, which is known to be NP-hard in strong sense. An ant colony optimization (ACO) algorithm is presented with the following features: (1) extending the use of VMDD heuristic rule from single machine situation to unrelated parallel machine environment; (2) incorporating PGA gene transfer operator in local search. The computational experiment shows that the proposed ACO algorithm strongly outperforms the traditional heuristic rule-VMDD and the general ACO algorithm without gene transfer operator.