Hybrid Taguchi-Based Particle Swarm Optimization for Flowshop Scheduling Problem

2013 ◽  
Vol 39 (3) ◽  
pp. 2393-2412 ◽  
Author(s):  
Ching-I Yang ◽  
Jyh-Horng Chou ◽  
Ching-Kao Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Swarm Optimization ◽  
Flowshop Scheduling Problem

scholarly journals Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem

2016 ◽  
Vol 3 (4) ◽  
pp. 295-311 ◽  
Author(s):  
Mansour Eddaly ◽  
Bassem Jarboui ◽  
Patrick Siarry
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Iterated Local Search ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Swarm Optimization ◽  
Blocking Flowshop ◽  
Flowshop Scheduling Problem

Abstract This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem. Highlights The blocking flowshop scheduling problem is very attractive problem in engineering. A particle swarm optimization approach is proposed. Extensive experiments are conducted to choose the initialization way. Improvement procedure is introduced based on iterated local search algorithm. A real-world case is solved by the proposed algorithm.

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