A novel objective function for job-shop scheduling problem with fuzzy processing time and fuzzy due date using differential evolution algorithm

2011 ◽  
Vol 56 (9-12) ◽  
pp. 1125-1138 ◽  
Author(s):  
Yanmei Hu ◽  
Minghao Yin ◽  
Xiangtao Li
Keyword(s):  
Processing Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Due Date ◽  
Shop Scheduling ◽  
Fuzzy Processing Time ◽  
Evolution Algorithm

scholarly journals An Improved Version of Discrete Particle Swarm Optimization for Flexible Job Shop Scheduling Problem with Fuzzy Processing Time

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Song Huang ◽  
Na Tian ◽  
Yan Wang ◽  
Zhicheng Ji
Keyword(s):  
Processing Time ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Fuzzy Processing Time

The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO) is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF). In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.

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