A multi-restart iterated local search algorithm for the permutation flow shop problem minimizing total flow time

Authors:
Xingye Dong;Ping Chen;Houkuan Huang;Maciek Nowak
Affiliations:
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China and Quinlan School of Business, Loyola University, Chicago, IL 60611, USA;Department of Logistics Management, TEDA College, NanKai University, Tianjin 300457, China;School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;Quinlan School of Business, Loyola University, Chicago, IL 60611, USA
Venue:
Computers and Operations Research
Year:
2013

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Abstract

A variety of metaheuristics have been developed to solve the permutation flow shop problem minimizing total flow time. Iterated local search (ILS) is a simple but powerful metaheuristic used to solve this problem. Fundamentally, ILS is a procedure that needs to be restarted from another solution when it is trapped in a local optimum. A new solution is often generated by only slightly perturbing the best known solution, narrowing the search space and leading to a stagnant state. In this paper, a strategy is proposed to allow the restart solution to be generated from a group of solutions drawn from local optima. This allows an extension of the search space, while maintaining the quality of the restart solution. A multi-restart ILS (MRSILS) is proposed, with the performance evaluated on a set of benchmark instances and compared with six state of the art metaheuristics. The results show that the easily implementable MRSILS is significantly better than five of the other metaheuristics and comparable to or slightly better than the remaining one.