Study on Multi-objective Flexible Job-shop Scheduling Problem considering Energy Consumption

被引：45

作者：

Jiang, Zengqiang ^{[1
]}

Zuo, Le ^{[1
]}

Mingcheng E ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Sch Mech Elect & Control Engn, Beijing, Peoples R China

来源：

JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM | 2014年 / 7卷 / 03期

关键词：

multi-objective scheduling; flexible job-shop scheduling; NSGA-II; energy consumption; blood variation;

D O I：

10.3926/jiem.1075

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem( FJSP) optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm ( NSGA-II) based on blood variation for above scheduling model. Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model. Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption. Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II ( BVNSGA-II) is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

引用

页码：589 / 604

页数：16

共 22 条

[1]

Corne D.W., The Pareto envelope based selection algorithm for multi-objective optimization, Lecture Notes in Computer Science, pp. 839-848, (2000)

[2]

Dai M., Tang D.B., Giret A., Salido M.A., Li W.D., Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm, Robotics and Computer-Integrated Manufacturing, 29, 5, pp. 418-429, (2013)

[3]

Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)

[4]

Fang K., Uhana N., Zhao F., Sutherland J.W., A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction, Journal of Manufacturing Systems, 30, pp. 234-240, (2011)

[5]

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

[6]

Ghasem M., Mehdi M., A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search, International Journal of Production Economics, 129, 1, pp. 14-22, (2011)

[7]

He Y., Liu F., Cao H.J., Liu C., Job Scheduling Model of Machining System for Green Manufacturing, Journal of Mechanical Engineering, 43, 4, pp. 27-33, (2007)

[8]

Horn J., Nafpliotis N., Goldberg D.E., A niched Pareto genetic algorithm for multi-objective optimization, Proceedings of the 1st IEEE Congress on Evolutionary Computation, pp. 82-87, (1994)

[9]

Jensen M.T., Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms, IEEE Transactions on Evolutionary Computation, 7, 5, pp. 503-515, (2003)

[10]

Knowles J., Corne D., The Pareto archived evolution strategy: A new baseline algorithm for multi-objective optimization, Proceedings of the 1999 Congress on Evolutionary Computation, pp. 98-105, (1999)

← 1 2 3 →