Sequential and parallel variable neighborhood search algorithms for job shop scheduling

Abstract

Variable Neighborhood Search (VNS) is a recently invented metaheuristic to use in solving combinatorial optimization problems in which a systematic change of neighborhood with a local search is carried out. However, as happens with other meta-heuristics, it sometimes takes long time to reach useful solutions whilst solving some sort of hard and large scale combinatorial problems such as job shop scheduling. One of the most considerable way out to overcome this shortcoming is to parallelize VNS implementations. In this chapter, firstly, a number of variable neighborhood search algorithms are examined for Job Shop Scheduling (JSS) problems and then four different parallelization policies are tackled as part of efficiency investigation for parallel VNS algorithms. The experimentation reveals the performance of various VNS algorithms and the efficiency of policies to follow in parallelization. In the end, a policy based on unidirectional-ring topology is found most efficient. © 2008 Springer-Verlag Berlin Heidelberg.