Solving the energy-efficient job shop scheduling problem: A multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption

Journal of Cleaner Production

Volumn 112, Issue , 2016, Pages 3361-3375

  Zhang, Rui ^a   Chiong, Raymond ^b  

^a XIAMEN UNIVERSITY OF TECHNOLOGY   (China)

^b UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

Energy efficiency; Genetic algorithm; Job shop scheduling; Multi objective optimization

Indexed keywords

CARBON DIOXIDE; ENERGY EFFICIENCY; ENERGY UTILIZATION; ENVIRONMENTAL IMPACT; GENETIC ALGORITHMS; LOCAL SEARCH (OPTIMIZATION); MANUFACTURE; MULTIOBJECTIVE OPTIMIZATION; PRODUCTION CONTROL; SCHEDULING; ALGORITHMS; CARBON; OPTIMIZATION;

BI-OBJECTIVE OPTIMIZATION; COMPUTATIONAL EXPERIMENT; IMPROVEMENT STRATEGIES; JOB SHOP SCHEDULING PROBLEMS; MULTI-OBJECTIVE GENETIC ALGORITHM; TOTAL ENERGY CONSUMPTION; TOTAL-WEIGHTED TARDINESS; TRADITIONAL MANUFACTURING;

JOB SHOP SCHEDULING;

EID: 84958619818     PISSN: 09596526     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jclepro.2015.09.097     Document Type: Article

References (40)

1. Adams, J., Balas, E., Zawack, D., 1988. The shifting bottleneck procedure for job shop scheduling. Manag. Sci. 34 (3), 391-401.
2. Balas, E., Vazacopoulos, A., 1998. Guided local search with shifting bottleneck for job shop scheduling. Manag. Sci. 44 (2), 262-275.
3. Blum, C., Chiong, R., Clerc, M., De Jong, K., Michalewicz, Z., Neri, F., Weise, T., 2012. Evolutionary optimization. In:Chiong, R., Weise, T., Michalewicz, Z. (Eds.), Variants of Evolutionary Algorithms for Real-world Applications. Springer Berlin Heidelberg, pp. 1-29.
4. Croce, F.D., Tadei, R., Volta, G., 1995. A genetic algorithm for the job-shop problem. Comput. Op. Res. 22 (1), 15-24.
5. Dai, M., Tang, D., Giret, A., Salido, M.A., Li, W.D., 2013. Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robot. Comp. Integr. Manuf. 29 (5), 418-429.
6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2), 182-197.
7. Essafi, I., Mati, Y., Dauzère-Pérès, S., 2008. A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Comput. Op. Res. 35 (8), 2599-2616.
8. Fang, K., Uhan, N.A., Zhao, F., Sutherland, J.W., 2013. Flow shop scheduling with peak power consumption constraints. Ann. Op. Res. 206 (1), 115-145.
9. Gonçalves, J.F., Resende, M.G., 2014. An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling. Int. Trans. Op. Res. 21 (2), 215-246.
10. Holtsclaw, H.H., Uzsoy, R., 1996. Machine criticality measures and subproblem solution procedures in shifting bottleneck methods: a computational study. J. Op. Res. Soc. 47 (5), 666-677.
11. Jain, A.S., Meeran, S., 1999. Deterministic job-shop scheduling: past, present and future. Eur. J. Op. Res. 113 (2), 390-434.
12. Jensen, J.B., Philipoom, P.R., Malhotra, M.K.,1995. Evaluation of scheduling rules with commensurate customer priorities in job shops. J. Op. Manag. 13 (3), 213-228.
13. Lenstra, J.K., Kan, A.H.G.R., Brucker, P., 1977. Complexity of machine scheduling problems. Ann. Discrete Math. 1, 343-362.
14. Lin, W., Yu, D.Y., Zhang, C., Liu, X., Zhang, S., Tian, Y., Liu, S., Xie, Z., 2015. A multiobjective teaching-learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint. J. Clean. Prod. 101, 337-347.
15. Liu, C.-H., Huang, D.-H., 2014. Reduction of power consumption and carbon footprints by applying multi-objective optimisation via genetic algorithms. Int. J. Prod. Res. 52 (2), 337-352.
16. Liu, S.Q., Kozan, E., 2012. A hybrid shifting bottleneck procedure algorithm for the parallel-machine job-shop scheduling problem. J. Op. Res. Soc. 63 (2), 168-182.
17. Liu, Y., Dong, H., Lohse, N., Petrovic, S., Gindy, N., 2014. An investigation into minimising total energy consumption and total weighted tardiness in job shops. J. Clean. Prod. 65, 87-96.
18. Liu, Y., Dong, H., Lohse, N., Petrovic, S., 2015. Reducing environmental impact of production during a rolling blackout policy - a multi-objective schedule optimisation approach. J. Clean. Prod. 102, 418-427.
19. Luo, H., Du, B., Huang, G.Q., Chen, H., Li, X., 2013. Hybrid flow shop scheduling considering machine electricity consumption cost. Int. J. Prod. Econ. 146 (2), 423-439.
20. May, G., Stahl, B., Taisch, M., Prabhu, V., 2015. Multi-objective genetic algorithm for energy-efficient job shop scheduling. Int. J. Prod. Res. http://dx.doi.org/10.1080/00207543.2015.1005248.
21. Montgomery, D.C., 2012. Design and Analysis of Experiments, eighth ed. John Wiley & Sons.
22. Moon, J.-Y., Shin, K., Park, J., 2013. Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency. Int. J. Adv. Manuf. Technol. 68 (1-4), 523-535.
23. Mori, M., Fujishima, M., Inamasu, Y., Oda, Y., 2011. A study on energy efficiency improvement for machine tools. CIRP Ann. Manuf. Technol. 60 (1), 145-148.
24. Mouzon, G., Yildirim, M.B., 2008. A framework tominimise total energy consumption and total tardiness on a single machine. Int. J. Sustain. Eng. 1 (2), 105-116.
25. Mouzon, G., Yildirim, M.B., Twomey, J., 2007. Operational methods for minimization of energy consumption of manufacturing equipment. Int. J. Prod. Res. 45 (18-19), 4247-4271.
26. Nilakantan, J.M., Huang, G.Q., Ponnambalam, S., 2015. An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. J. Clean. Prod. 90, 311-325.
27. Nowicki, E., Smutnicki, C., 1996. A fast taboo search algorithm for the job shop problem. Manag. Sci. 42 (6), 797-813.
28. Shrouf, F., Ordieres-Merè, J., García-Sánchez, A., Ortega-Mier, M., 2014. Optimizing the production scheduling of a single machine to minimize total energy consumption costs. J. Clean. Prod. 67, 197-207.
29. Singer, M., Pinedo, M., 1998. A computational study of branch and bound techniques for minimizing the total weighted tardiness in job shops. IIE Trans. 30 (2), 109-118.
30. Spanos, A.C., Ponis, S.T., Tatsiopoulos, I.P., Christou, I.T., Rokou, E., 2014. A new hybrid parallel genetic algorithm for the job-shop scheduling problem. Int. Trans. Op. Res. 21 (3), 479-499.
31. Tan, K.C., Goh, C.K., Yang, Y.J., Lee, T.H., 2006. Evolving better population distribution and exploration in evolutionary multi-objective optimization. Eur. J. Op. Res. 171 (2), 463-495.
32. Tavakkoli-Moghaddam, R., Safaei, N., Kah, M.M.O., 2008. Accessing feasible space in a generalized job shop scheduling problem with the fuzzy processing times: a fuzzy-neural approach. J. Op. Res. Soc. 59 (4), 431-442.
33. Vepsalainen, A.P., Morton, T.E., 1987. Priority rules for job shops with weighted tardiness costs. Manag. Sci. 33 (8), 1035-1047.
34. Wang, S., Lu, X., Li, X.X., Li, W.D., 2015. A systematic approach of process planning and scheduling optimization for sustainable machining. J. Clean. Prod. 87, 914-929.
35. Wolpert, D.H., Macready, W.G., 1997. No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1 (1), 67-82.
36. Wolsey, L.A., 1998. Integer Programming. Wiley, New York.
37. Zhang, R., Wu, C., 2013. A neighbourhood property for the job shop scheduling problem with application to hybrid particle swarm optimization. IMA J. Manag. Math. 24 (1), 111-134.
38. Zhang, H., Zhao, F., Fang, K., Sutherland, J.W., 2014. Energy-conscious flow shop scheduling under time-of-use electricity tariffs. CIRP Ann. Manuf. Technol. 63 (1), 37-40.
39. Zhu, Z.C., Ng, K.M., Ong, H.L., 2010. A modified tabu search algorithm for cost-based job shop problem. J. Op. Res. Soc. 61 (4), 611-619.
40. Zobolas, G.I., Tarantilis, C.D., Ioannou, G., 2009. A hybrid evolutionary algorithm for the job shop scheduling problem. J. Op. Res. Soc. 60 (2), 221-235.