Improved simulated annealing algorithm for flexible job shop scheduling problems

2016 ◽  
Author(s):  
Gao Chenyang ◽  
Gao Yuelin ◽  
Lv Shanshan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Annealing Algorithm

scholarly journals Cooperative Threads with Effective-Address in Simulated Annealing Algorithm to Job Shop Scheduling Problems

2019 ◽  
Vol 9 (16) ◽  
pp. 3360 ◽  
Author(s):  
Marco Antonio Cruz-Chávez ◽  
Jesús del C. Peralta-Abarca ◽  
Martín H. Cruz-Rosales
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Distributed Processing ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Parallel And Distributed Processing ◽  
Annealing Algorithm ◽  
Cooperative Algorithm

This paper presents a parallel algorithm applied to the job shop scheduling problem (JSSP). The algorithm generates a set of threads, which work in parallel. Each generated thread, executes a procedure of simulated annealing which obtains one solution for the problem. Each solution is directed towards the best solution found by the system at the present, through a procedure called effective-address. The cooperative algorithm evaluates the makespan for various benchmarks of different sizes, small, medium, and large. A statistical analysis of the results of the algorithm is presented and a comparison of performance with other (sequential, parallel, and distributed processing) algorithms that are found in the literature is presented. The obtained results show that the cooperation of threads carried out by means of effective-address procedure permits to simulated annealing to work with increased efficacy and efficiency for problems of JSSP.

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.

Sign in / Sign up

Export Citation Format

Share Document