Comparing an ACO algorithm with other heuristics for the single machine scheduling problem with sequence-dependent setup times

• Published in: The Journal of the Operational Research Society Vol. 53; no. 8; pp. 895 - 906
• Main Authors: Gagné, C, Price, W L, Gravel, M
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 01.08.2002; Palgrave Macmillan Press; Palgrave Macmillan UK; Palgrave; Taylor & Francis Ltd
• Subjects: Algorithms; ant colony optimization; Ants; Applied sciences; Business and Management; Exact sciences and technology; Genetic algorithms; Heuristic; Heuristics; Insect colonies; Insect genetics; Job shops; Lateness; Management; metaheuristic; Operational research and scientific management; Operational research. Management science; Operations research; Operations Research/Decision Theory; Optimization; Pheromones; Scheduling; Scheduling, sequencing; sequence-dependent setups; single machine; Studies; Theoretical Paper; Theoretical Papers; total tardiness; scheduling; metaheuristic; sequence-dependent setups; ant colony optimization; total tardiness; single machine; Setup time; Scheduling; Single machine; NP hard problem
• ISSN: 0160-5682; 1476-9360
• DOI: 10.1057/palgrave.jors.2601390

We compare several heuristics for solving a single machine scheduling problem. In the operating situation modelled, setup times are sequence-dependent and the objective is to minimize total tardiness. We describe an Ant Colony Optimization (ACO) algorithm having a new feature using look-ahead information in the transition rule. This feature shows an improvement in performance. A comparison with a genetic algorithm, a simulated annealing approach, a local search method and a branch-and-bound algorithm indicates that the ACO that we describe is competitive and has a certain advantage for larger problems.