Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems

Populations of multipopulation genetic algorithms (MPGAs) parallely evolve with some interaction mechanisms. Previous studies have shown that the interaction structures can impact on the performance of MPGAs to some extent. This paper introduces the concept of complex networks such as ring-shaped networks and small-world networks to study how interaction structures and their parameters influence the MPGAs, where subpopulations are regarded as nodes and their interaction or migration of elites between subpopulations as edges. After solving the flexible job-shop scheduling problem (FJSP) by MPGAs with different parameters of interaction structures, simulation results were measured by criteria, such as success rate and average optimal value. The analysis reveals that (1) the smaller the average path length (APL) of the network is, the higher the propagation rate will be; (2) the performance of MPGAs increased first and then decreased along with the decrease of APL, indicating that, for better performance, the networks should have a proper APL, which can be adjusted by changing the structural parameters of networks; and (3) because the edge number of small-world networks remains unchanged with different rewiring possibilities of edges, the change in performance indicates that the MPGA can be improved by a more proper interaction structure of subpopulations as other conditions remain unchanged.

Download Full-text

A Review of The Application of Genetic Algorithms for Solving Flexible Job Shop Scheduling Problems

Proceedings of the 2nd International Conference on Information Technologies and Electrical Engineering ◽

10.1145/3386415.3387092 ◽

2019 ◽

Author(s):

Dongri Shan ◽

Yuwei Wang

Keyword(s):

Genetic Algorithms ◽

Job Shop ◽

Job Shop Scheduling ◽

Flexible Job Shop ◽

Scheduling Problems ◽

Flexible Job Shop Scheduling ◽

Shop Scheduling

Download Full-text

Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

Mathematical and Computational Applications ◽

10.3390/mca24030080 ◽

2019 ◽

Vol 24 (3) ◽

pp. 80 ◽

Cited By ~ 1

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.

Download Full-text