Multiprocessor task scheduling in multistage hybrid flow-shops: a genetic algorithm approach

Gündüz Ulusoy Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey. [email protected] ABSTRACT This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, i.e. the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprised of 400 problems with up to 100 jobs, 10 stages, and with up to 5 processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-hfsp