par Sampels, Michaël ;Blum, Christian ;Mastrolilli, Monaldo M.;Rossi-Doria, Olivia O.
Référence Lecture notes in computer science, 2439, page (631-640)
Publication Publié, 2002
Article révisé par les pairs
Résumé : The Group Shop Scheduling Problem (GSP) is a generalization of the classical Job Shop and Open Shop Scheduling Problems. In the GSP there are m machines and n jobs. Each job consists of a set of operations, which must be processed on specified machines without preemption. The operations of each job are partitioned into groups on which a total precedence order is given. The problem is to order the operations on the machines and on the groups such that the maximal completion time (makespan) of all operations is minimized. The main goal of this paper is to provide a fair comparison of five metaheuristic approaches (i.e., Ant Colony Optimization, Evolutionary Algorithm, Iterated Local Search, Simulated Annealing, and Tabu Search) to tackle the GSP. We guarantee a fair comparison by a common definition of neighborhood in the search space, by using the same data structure, programming language and compiler, and by running the algorithms on the same hardware.