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Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

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Abstract

With the increasing concern on greenhouse gas emissions, green scheduling decision in the manufacturing factory is gaining more and more attention. This paper addresses the unrelated parallel machine green scheduling problem (UPMGSP) with criteria of minimizing the makespan and the total carbon emission. To solve the problem, the estimation of distribution evolution memetic algorithm (EDEMA) is proposed. Firstly, based on the minimum machine load first principle, the initialization of the population is proposed. Second, a multi-objective non-dominated sorting approach and the crowding distance are adopted to improve the diversity of individual. Third, to estimate the probability distribution of the solution space, a probability model is presented to enhance the searching ability. Third, five neighbourhood searching operators are designed to handle the job-to-machine assignment. Moreover, the population catastrophe is used to maintain the sustainable diversity of the population. Finally, based on the randomly generated instances of the UPMGSP, extensive computational tests are carried out. The obtained computational results show that the EDEMA has the better searching capability and the better objective value than those of the non-dominated sorting genetic algorithm II and the estimation of distribution evolution algorithm (EDEA) in solving the UPMGSP.

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References

  1. Arnaout J, Musa R, Rabadi G (2014) A two-stage ant colony optimization algorithm to minimize the makespan on unrelated parallel machines-part II: enhancements and experimentations. J Intell Manuf 25(1):43–53

    Article  Google Scholar 

  2. Arroyo JEC, Leung JYT (2017) An effective iterated greedy algorithm for scheduling unrelated parallel batch machines with non-identical capacities and unequal ready times. Comput Ind Eng 105:84–100

    Article  Google Scholar 

  3. Avdeenko TV, Mesentsev YA (2016) Efficient approaches to scheduling for unrelated parallel machines with release dates. IFAC PapersOnLine 49(12):1743–1748

    Article  Google Scholar 

  4. Bampis E, Letsios D, Lucarelli G (2015) Green scheduling, flows and matchings. Theoret Comput Sci 579:126–136

    Article  MathSciNet  Google Scholar 

  5. Che A, Zhang S, Wu X (2017) Energy-conscious unrelated parallel machine scheduling under time-of-use electricity tariffs. J Clean Prod 156:688–697

    Article  Google Scholar 

  6. Fang K, Uhan N, Zhao F, Sutherland JW (2011) A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. J Manuf Syst 30(4):234–240

    Article  Google Scholar 

  7. Fang H, Zhou AM, Zhang H (2018) Information fusion in offspring generation: a case study in DE and EDA. Swarm Evolut Comput 42:99–108

    Article  Google Scholar 

  8. Graham RL (1966) Bounds for certain multiprocessing anomalies. Bell Syst Tech J 45:1563–1581

    Article  Google Scholar 

  9. Li BB, Wang L, Liu B (2008) An effective PSO-based hybrid algorithm for multiobjective permutation flow shop scheduling. IEEE Trans Syst Man Cybern Part A Syst Hum 38(4):818–831

    Article  Google Scholar 

  10. Li ZT, Yanga H, Zhangb S, Liub G (2015) Unrelated parallel machine scheduling problem with energy and tardiness cost. Int J Adv Manuf Technol 84(1–4):1–14

    Google Scholar 

  11. Li K, Zhang X, Leung JYT, Yang SL (2016) Parallel machine scheduling problems in green manufacturing industry. J Manuf Syst 38:98–106

    Article  Google Scholar 

  12. Liu ZC, Guo SS, Wang L (2019) Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. J Clean Prod 211:765–786

    Article  Google Scholar 

  13. Lu SJ, Liu XB, Pei J, Thai MT, Pardalos PM (2018) A hybrid ABC-TS algorithm for the unrelated parallel-batching machines scheduling problem with deteriorating jobs and maintenance activity. Appl Soft Comput 66:168–182

    Article  Google Scholar 

  14. Mansouri SA, Aktas E, Besikci B (2016) Green scheduling of a two-machine flowshop: trade-off between makespan and energy consumption. Eur J Oper Res 248(3):772–788

    Article  MathSciNet  Google Scholar 

  15. Manuel VJ, Oscar G, Juan L, Marta B, Ignacio HJ (2018) Combining data augmentation, edas and grammatical evolution for blood glucose forecasting. Memet Comput 10(3):267–277

    Article  Google Scholar 

  16. Mouzon G, Yildirim MB, Twomey J (2007) Operational methods for minimization of energy consumption of manufacturing equipment. Int J Prod Res 45(18–19):4247–4271

    Article  Google Scholar 

  17. Mühlenbein H, Paass G (1996) From recombination of genes to the estimation of distributions I: binary parameters. Lect Notes Comput Sci 1141(1):178–187

    Article  Google Scholar 

  18. Pan I, Das S (2013) Design of hybrid regrouping PSO-GA based sub-optimal networked control system with random packet losses. Memet Comput 5(2):141–153

    Article  Google Scholar 

  19. Safarzadeh H, Niaki STA (2019) Bi-objective green scheduling in uniform parallel machine environments. J Clean Prod 217:559–572

    Article  Google Scholar 

  20. Sels V, Coelho J, Manuel Dias A, Vanhoucke M (2015) Hybrid tabu search and a truncated branch-and-bound for the unrelated parallel machine scheduling problem. Comput Oper Res 53:107–117

    Article  MathSciNet  Google Scholar 

  21. Shen JN, Wang L, Wang SY (2015) A bi-population EDA for solving the no-idle permutation flow-shop scheduling problem with the total tardiness criterion. Knowl Based Syst 74:167–175

    Article  Google Scholar 

  22. Valdez PSI, Hernández A, Botello S (2014) Repairing normal EDAs with selective repopulation. Appl Math Comput 230:65–77

    MathSciNet  MATH  Google Scholar 

  23. Villa F, Vallada E, Fanjul-Peyro L (2018) Heuristic algorithms for the unrelated parallel machine scheduling problem with one scarce additional resource. Expert Syst Appl 93:28–38

    Article  Google Scholar 

  24. Wang L (2003) Shop scheduling with genetic algorithms. Tsinghua University Press, Beijing

    Google Scholar 

  25. Wang L, Wang SY, Fang C (2017) Estimation of distribution algorithms for scheduling. Tsinghua University Press, Beijing

    Google Scholar 

  26. Wang SY, Wang L (2015) An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. IEEE Trans Syst Man Cybern Syst 46(1):139–149

    Article  Google Scholar 

  27. Wu XL, Sun YJ (2018) A green scheduling algorithm for flexible job shop with energy-saving measures. J Clean Prod 172:3249–3264

    Article  Google Scholar 

  28. Yu JJ (2010) Green scheduling and its solution. Adv Mater Res 139–141:1415–1418

    Article  Google Scholar 

  29. Zheng XL, Wang L (2018) A collaborative multiobjective fruit fly optimization algorithm for the resource constrained unrelated parallel machine green scheduling problem. IEEE Trans Syst Man Cybern Syst 48(5):790–800

    Article  Google Scholar 

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Acknowledgements

The authors would like to express our warmest thanks to the referees for their interest in our work and their valuable comments for improving the paper. This work is supported by the National Nature Science Foundation of China (71501098, 71603135, 71774080,71834003), China Postdoctoral Science Foundation Funded Project (2016M590453, 2018T110501), Science Foundation of Ministry of Education of China (17YJC630205) and the Fundamental Research Funds for the Central Universities (3082018NR2018011).

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Correspondence to Xianyu Yu.

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Xue, Y., Rui, Z., Yu, X. et al. Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem. Memetic Comp. 11, 423–437 (2019). https://doi.org/10.1007/s12293-019-00295-0

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