Improved NSGA-II for the job-shop multi-objective scheduling problem

被引：1

作者：

Jiang X. ^{[1
]}

Li Y. ^{[1
]}

机构：

[1] School of Economics and Management, Xiamen University of Technology, Xiamen

来源：

International Journal of Performability Engineering | 2018年 / 14卷 / 05期

关键词：

Job-shop scheduling; Multi-objective optimization; NSGA-II;

D O I：

10.23940/ijpe.18.05.p7.891898

中图分类号：

学科分类号：

摘要：

Job-shop scheduling is essential to advanced manufacturing and modern management. In light of the difficulty of obtaining the optimal solution using simple genetic algorithms in the process of solving multi-objective job-shop scheduling problems, with maximum customer satisfaction and minimum makespan in mind, we constructed a multi-objective job-shop scheduling model with factory capacity constraints and propose an improved NSGA-II algorithm. This algorithm not only uses an improved elitism strategy to dynamically update the elite solution set, but also enhances the Pareto sorting algorithm to make density computations more accurate, thereby ensuring population diversity. An example is given to verify that this algorithm can effectively enhance global search capabilities, save computing resources, and lead to a better optimal solution. Using this algorithm for job-shop scheduling optimization oriented towards multi-objective decision-making can provide corporate executives with a scientific quantitative basis for management and decision-making, thereby enhancing their companies’ competitiveness. © 2018 Totem Publisher, Inc. All rights reserved.

引用

页码：891 / 898

页数：7

共 24 条

[1]

Chatterjee S., Abhishek K., Mahapatra S.S., Nsga-ii approach of optimization to study the effects of drilling parameters in aisi-304 stainless steel, Procedia Engineering, 97, pp. 78-84, (2014)

[2]

Deb K., Multi-Objective Optimization Using Evolutionary Algorithms, (2001)

[3]

Deb K., Pratap A., Agarwal S., A fast and elitist multiobjective genetic algorithm: Nsga-ii, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)

[4]

Fonseca C.M., Fleming P.J., Genetic algorithms f or multiobjective optimization: Formulation discussion and generalization, Proceedings of The International Conference on Genetic Algorithms, pp. 416-423, (1993)

[5]

Gao J., Gen M., Sun L., Zhao X., A hybrid of genetic algorithm and bottleneck shifting for multi-objective flexible job shop scheduling problems, Computers and Industrial Engineering, 53, 1, pp. 149-162, (2007)

[6]

Horn J., Nafpliotis N., Goldberg D.E., Multiobjective optimization using the niched pareto genetic algorithm, Proceedings of The International Conference on Evolutionary Computation, pp. 82-87, (1994)

[7]

Huang R.F., Luo X.W., Ji B., Multi-objective optimization of a mixed-flow pump impeller using modified nsga-ii algorithm, Science China Technological Sciences, 58, 12, pp. 2122-2130, (2015)

[8]

Jiang X.Y., Chen W.C., An effective approach for the optimisation of cutting parameters, International Journal of Computer Applications in Technology, 50, 3-4, pp. 180-185, (2014)

[9]

Jiang X.Y., Wu H.H., Optimization of setup frequency for toc supply chain replenishment system with capacity constraints, Neural Computing and Applications, 23, 6, pp. 1831-1838, (2013)

[10]

Kaplanoglu V., An object-oriented approach for multi-objective flexible job-shop scheduling problem, Expert Systems with Applications, 45, pp. 71-84, (2016)

← 1 2 3 →