Optimal Scheduling and Decision Making Method for Dynamic Flexible Job Shop

Authors

Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China;
Ding Yu, Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China;

Abstract

Abstract: For multi-objective dynamic flexible job-shop scheduling, an improved multi-objective differential evolution algorithm is proposed. The adaptive cross-mutation operator is introduced into the differential evolution algorithm to improve its global search capability. The fast non-dominated sorting method based on immunological principles is introduced to improve the quality of the solution set in the selection and sorting. An improved TOPSIS-G1-EVM comprehensive decision-making method is proposed. The comprehensive weight of G1-EVM is calculated by Nash equilibrium theory. The comprehensive weight and TOPSIS evaluation system are combined to evaluate each dispatching scheme. The experimental results show that the optimal scheduling algorithm is superior in the optimization ability and the effectiveness of the comprehensive decision-making method.

Recommended Citation

Yan, Wang and Yu, Ding (2020) "Optimal Scheduling and Decision Making Method for Dynamic Flexible Job Shop," Journal of System Simulation: Vol. 32: Iss. 11, Article 1.
DOI: 10.16182/j.issn1004731x.joss.20-0732
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss11/1

First Page

2073

Revised Date

2020-10-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0732

Last Page

2083

CLC

TP391.9

Recommended Citation

Wang Yan, Ding Yu. Optimal Scheduling and Decision Making Method for Dynamic Flexible Job Shop[J]. Journal of System Simulation, 2020, 32(11): 2073-2083.

DOI

10.16182/j.issn1004731x.joss.20-0732