Solving the Job Shop Scheduling Problem Using the Imperialist Competitive Algorithm

2012 ◽  
Vol 430-432 ◽  
pp. 737-740 ◽  
Author(s):  
Jie Zhang ◽  
Peng Zhang ◽  
Jian Xiong Yang ◽  
Ying Huang

This paper deals with the Job Shop Scheduling Problem with the minimization of makespan as the objective. A novel meta-heuristic named imperialist competitive algorithm (ICA) is adopted to solve the problem. Since appropriate design of the parameters has a significant impact on the performance of the algorithm, the parameters were chosen based on orthogonal test. A local search strategy based on critical path and critical block was used to improve the performance of the algorithm. At last the algorithm was tested on a set of standard benchmark instances, and the computational results showed that the algorithm proposed performed well in both convergence rate and better global optima achievement.

2012 ◽  
Vol 2012.51 (0) ◽  
pp. 175-176
Author(s):  
Soichiro YOKOYAMA ◽  
Masahito YAMAMOTO ◽  
Masashi FURUKAWA ◽  
Ikuo SUZUKI

2019 ◽  
Vol 13 (3) ◽  
pp. 389-396 ◽  
Author(s):  
Aya Ishigaki ◽  
◽  
Yuki Matsui

The flexible job shop scheduling problem (FJSSP) is an extension of the classical job shop scheduling problem (JSSP) that allocates jobs to resources while minimizing the maximum completion time of all the jobs. Machine assignment and job sequence are determined in the FJSSP. To efficiently solve the FJSSP, which is a non-deterministic polynomial-time hard problem, a heuristic method must be used. In previous studies, the FJSSP has been solved using neighborhood algorithms that employ various metaheuristic methods. These approaches constrain the neighborhood operation to jobs on a critical path and simultaneously change the machine assignment and job sequence. Branches on the critical path are easily generated in the FJSSP search processes; this branch structure can improve the efficiency of the FJSSP. This study investigates two neighborhood search algorithms used for changing the machine assignment and job sequence via a critical path. The first method changes the machine assignment and job sequence simultaneously, whereas the second method changes them independently. In this study, we propose an efficient neighborhood generating method using a branch block of critical path.


2011 ◽  
Vol 21 (12) ◽  
pp. 3082-3093
Author(s):  
Zhu-Chang XIA ◽  
Fang LIU ◽  
Mao-Guo GONG ◽  
Yu-Tao QI

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