Iterated greedy local search methods for unrelated parallel machine scheduling

• Published in: European journal of operational research Vol. 207; no. 1; pp. 55 - 69
• Main Authors: Fanjul-Peyro, Luis, Ruiz, Rubén
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.11.2010; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Applied sciences; Assignment problem; Combinatorial analysis; Completion time; Exact sciences and technology; Flows in networks. Combinatorial problems; Heuristic; Inventory control, production control. Distribution; Iterated greedy; Job shops; Local search; Makespan; Mathematical models; Operational research; Operational research and scientific management; Operational research. Management science; Optimization; Optimization algorithms; Production scheduling; Scheduling; Scheduling, sequencing; State of the art; Studies; Unrelated parallel machines; Unrelated parallel machines Makespan Iterated greedy Local search; Local search; Makespan; Iterated greedy; Unrelated parallel machines; Statistical analysis; Assignment problem; Combinatorial problem; Parallel machines; Minimization; Processing time; Scheduling; Combinatorial optimization; Search algorithm; Workload; Disjunctive programming; Heuristic method; Greedy algorithm; Metamodel; Completion time; Execution time
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2010.03.030

This work deals with the parallel machine scheduling problem which consists in the assignment of n jobs on m parallel machines. The most general variant of this problem is when the processing time depends on the machine to which each job is assigned to. This case is known as the unrelated parallel machine problem. Similarly to most of the literature, this paper deals with the minimization of the maximum completion time of the jobs, commonly referred to as makespan ( C max ) . Many algorithms and methods have been proposed for this hard combinatorial problem, including several highly sophisticated procedures. By contrast, in this paper we propose a set of simple iterated greedy local search based metaheuristics that produce solutions of very good quality in a very short amount of time. Extensive computational campaigns show that these solutions are, most of the time, better than the current state-of-the-art methodologies by a statistically significant margin.