Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Journal of Industrial Engineering and Management

Volumn 7, Issue 3, 2014, Pages 589-604

  Jiang, Zengqiang ^a   Zuo, Le ^a   Mingcheng, E ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Blood variation; Energy consumption; Flexible job shop scheduling; Multi objective scheduling; NSGA II

Indexed keywords

BLOOD; CHROMOSOMES; COSTS; ENERGY UTILIZATION; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE; SCHEDULING; SCHEDULING ALGORITHMS; SITE SELECTION;

CROSSOVER AND MUTATION; DESIGN/METHODOLOGY/APPROACH; ENVIRONMENTAL EMISSIONS; FLEXIBLE JOB-SHOP SCHEDULING; FLEXIBLE JOB-SHOP SCHEDULING PROBLEM; MULTI-OBJECTIVE SCHEDULING; NON-DOMINATED SORTING GENETIC ALGORITHMS; NSGA-II;

MATHEMATICAL MODELS;

EID: 84903772987     PISSN: 20138423     EISSN: 20130953     Source Type: Journal    
DOI: 10.3926/jiem.1075     Document Type: Article

References (22)

1. Corne, D.W. (2000). The Pareto envelope based selection algorithm for multi-objective optimization. Lecture Notes in Computer Science, 839-848. http://dx.doi.org/10.1007/3-540-45356-3_82
2. Dai, M., Tang, D.B., Giret, A., Salido, M.A. & Li, W.D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing. 29(5), 418-429. http://dx.doi.org/10.1016/j.rcim.2013.04.001
3. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2): 182-197. http://dx.doi.org/10.1109/4235.996017
4. Fang, K., Uhana, N., Zhao, F., & Sutherland, J.W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30, 234-240. http://dx.doi.org/10.1016/j.jmsy.2011.08.004
5. Fonseca, C.M., & Fleming, P.J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms, 416-423.
6. Ghasem, M., & Mehdi, M. (2011). 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), 14-22. http://dx.doi.org/10.1016/j.ijpe.2010.08.004
7. He, Y., Liu, F., Cao, H.J., & Liu, C. (2007). Job Scheduling Model of Machining System for Green Manufacturing. Journal of Mechanical Engineering, 43(4), 27-33. http://dx.doi.org/10.3901/JME.2007.04.027
8. Horn, J., Nafpliotis, N., & Goldberg, D.E. (1994). A niched Pareto genetic algorithm for multi-objective optimization. Proceedings of the 1st IEEE Congress on Evolutionary Computation. Piscataway: IEEE, 82-87. http://dx.doi.org/10.1109/ICEC.1994.350037
9. Jensen, M.T. (2003). Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. IEEE Transactions on Evolutionary Computation, 7(5), 503-515. http://dx.doi.org/10.1109/TEVC.2003.817234
10. Knowles, J., & Corne, D. (1999). The Pareto archived evolution strategy: a new baseline algorithm for multi-objective optimization. Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 98-105. http://dx.doi.org/10.1109/CEC.1999.781913
11. Li, J.Q., Pan, Q.K., & Wang, Y.T. (2010). Hybrid Pareto-Based Tabu Search Algorithm for Solving the Multi-Objective Flexible Job Shop Scheduling Problem. Computer Integrated Manufacturing Systems, 16(7), 1419-1426.
12. Liu, A.J., Yang, Y., Cheng, W.M., Xing, Q.S., Lu, H., Zhao, X.H. et al. (2012). Improved NSGA for Complex Manufacturing Environment. Computer Integrated Manufacturing Systems, 18(11), 2447-2458.
13. Liu, M.Z., Zhang, M.W., Jiang, Z.Q., Ge, M.G., & Zhang, M.X. (2008). Multi-objective and Flexible Job-Shop Problems Study Based on Hybrid Particle Swarm Optimization. Transactions of The Chinese Society For Agricultural Machinery, 39(5), 122-127.
14. Luo, H., Du, B., Huang, G.Q., Chen, H. & Li, X. (2013). Hybrid flow shop scheduling considering machine electricity consumption cost. International Journal of Production Economics. 146(2), 423-439. http://dx.doi.org/10.1016/j.ijpe.2013.01.028
15. Srinivas, N., & Deb, K. (1995). Multi-objective function optimization using non-dominated sorting genetic algorithm. Evolutionary Computation, 2(3), 221-248. http://dx.doi.org/10.1162/evco.1994.2.3.221
16. Xing, L.N., Chen, Y.W., & Yang, K.W. (2009). Multi-objective flexible job shop schedule: design and evaluation by simulation modeling. Applied Soft Computing, 9(1), 362-376. http://dx.doi.org/10.1016/j.asoc.2008.04.013
17. Xu, X.L., Ying, S.Y., Wang, W.L. (2010). Fuzzy Flexible Job-Shop Scheduling Method Based on Multi-Agent Immune Algorithm. Control and Decision, 25(2), 171-184. (in Chinese).
18. Zhang, C.Y., Dong, X., Wang, X.J., Li, X.Y., & Liu, Q. (2010). Improved NSGA-II for the Multi-objective Flexible Job-shop Scheduling Problem. Journal of Mechanical Engineering, 46(11), 156-164. http://dx.doi.org/10.3901/JME.2010.11.156
19. Zhang, G.H., Shao, X.Y., Li, P.G., Gao, L. (2009). An effective hybrid particles warm optimization algorithm for multi-objective flexible job shop scheduling problem. Computers & Industrial Engineering, 56(2), 1309-1318. http://dx.doi.org/10.1016/j.cie.2008.07.021
20. Zheng, J.H. (2010). Multi-objective Evolutionary Algorithm and Application. Beijing: Science press.
21. Zitzler, E., & Thile, L. (1999). Multi-objective evolutionary algorithm: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. http://dx.doi.org/10.1109/4235.797969
22. Zeng, S.Y., Li, H., Ding, L.X., & Yao, S.Z. (2004). A Fast Algorithm for Finding Non-Dominated Set Based on Sorting. Journal Of Computer Research And Development, 41(9), 1565-1571.