Authors:
Xingye Dong
1
;
Maciek Nowak
2
;
Ping Chen
3
and
Youfang Lin
1
Affiliations:
1
Beijing Jiaotong University, China
;
2
Loyola University, United States
;
3
NanKai University, China
Keyword(s):
Scheduling, Permutation Flow Shop, Total Flow Time, Iterated Local Search, Self-adaptive Perturbation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Formal Methods
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Manufacturing Systems Engineering
;
Optimization Algorithms
;
Planning and Scheduling
;
Production Planning, Scheduling and Control
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
Iterated local search (ILS) is a simple, effective and efficient metaheuristic, displaying strong performance on the permutation flow shop scheduling problem minimizing total flow time. Its perturbation method plays an important role in practice. However, in ILS, current methodology does not use an evaluation of the search status to adjust the perturbation strength. In this work, a method is proposed that evaluates the neighborhoods around the local optimum and adjusts the perturbation strength according to this evaluation using a technique
derived from simulated-annealing. Basically, if the neighboring solutions are considerably worse than the best solution found so far, indicating that it is hard to escape from the local optimum, then the perturbation strength is likely to increase. A self-adaptive ILS named SAILS is proposed by incorporating this perturbation strategy. Experimental results on benchmark instances show that the proposed perturbation strategy is effective and SAILS p
erforms better than three state of the art algorithms.
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