Heuristic Solving of NP-Complete Job-Shop Scheduling Problems by Multicriteria Optimisation

1994 ◽  
pp. 243-258
Author(s):  
R. Straubel ◽  
B. Holznagel ◽  
A. Wittmüss ◽  
U. Bärmann
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Multicriteria Optimisation ◽  
Np Complete

Flexible Job-Shop Scheduling Problems

2011 ◽  
pp. 234-263 ◽  
Author(s):  
Imed Kacem ◽  
Slim Hammadi ◽  
Pierre Borne
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Computing Time ◽  
Search Space ◽  
Scheduling Problems ◽  
Problem Complexity ◽  
Exact Methods ◽  
Shop Scheduling ◽  
Wire Routing ◽  
Np Complete

The Job-shop Scheduling Problem (JSP) is one of hardest problems; it is classified NP-complete (Carlier & Chretienne, 1988; Garey & Johnson, 1979). In the most part of cases, the combination of goals and resources can exponentially increase the problem complexity, because we have a very large search space and precedence constraints between tasks. Exact methods such as dynamic programming and branch and bound take considerable computing time (Carlier, 1989; Djerid & Portmann, 1996). Front to this difficulty, meta-heuristic techniques such as evolutionary algorithms can be used to find a good solution. The literature shows that they could be successfully used for combinatorial optimization such as wire routing, transportation problems, scheduling problems, etc. (Banzhaf, Nordin, Keller & Francone, 1998; Dasgupta & Michalewicz, 1997).

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

Multiobjective Optimization for Complex Flexible Job-Shop Scheduling Problems

2021 ◽  
Author(s):  
Karim Tamssaouet ◽  
Stéphane Dauzère-Pérès ◽  
Sebastian Knopp ◽  
Abdel Bitar ◽  
Claude Yugma
Keyword(s):  
Multiobjective Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling
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