An improved genetic algorithm for robust permutation flowshop scheduling

2011 ◽  
Vol 56 (1-4) ◽  
pp. 345-354 ◽  
Author(s):  
Qiong Liu ◽  
Saif Ullah ◽  
Chaoyong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Flowshop Scheduling ◽  
Improved Genetic Algorithm ◽  
Permutation Flowshop

A Self-guided Genetic Algorithm for permutation flowshop scheduling problems

2012 ◽  
Vol 39 (7) ◽  
pp. 1450-1457 ◽  
Author(s):  
Shih-Hsin Chen ◽  
Pei-Chann Chang ◽  
T.C.E. Cheng ◽  
Qingfu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Scheduling Problems ◽  
Flowshop Scheduling ◽  
Permutation Flowshop

scholarly journals A Constructive Genetic Algorithm for permutation flowshop scheduling

2008 ◽  
Vol 55 (1) ◽  
pp. 195-207 ◽  
Author(s):  
Marcelo Seido Nagano ◽  
Rubén Ruiz ◽  
Luiz Antonio Nogueira Lorena
Keyword(s):  
Genetic Algorithm ◽  
Flowshop Scheduling ◽  
Permutation Flowshop

scholarly journals Synergy of Genetic Algorithm with Extensive Neighborhood Search for the Permutation Flowshop Scheduling Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Rong-Chang Chen ◽  
Jeanne Chen ◽  
Tung-Shou Chen ◽  
Chien-Che Huang ◽  
Li-Chiu Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Flowshop Scheduling ◽  
Permutation Flowshop ◽  
Hybrid Genetic Algorithms ◽  
Combinatorial Optimization Problems ◽  
Flowshop Scheduling Problem

The permutation flowshop scheduling problem (PFSP) is an important issue in the manufacturing industry. The objective of this study is to minimize the total completion time of scheduling for minimum makespan. Although the hybrid genetic algorithms are popular for resolving PFSP, their local search methods were compromised by the local optimum which has poorer solutions. This study proposed a new hybrid genetic algorithm for PFSP which makes use of the extensive neighborhood search method. For evaluating the performance, results of this study were compared against other state-of-the-art hybrid genetic algorithms. The comparisons showed that the proposed algorithm outperformed the other algorithms. A significant 50% test instances achieved the known optimal solutions. The proposed algorithm is simple and easy to implement. It can be extended easily to apply to similar combinatorial optimization problems.

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