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Energy-efficient distributed heterogeneous blocking flowshop scheduling problem using a knowledge-based iterated Pareto greedy algorithm

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Abstract

In recent years, both distributed scheduling and energy-efficient scheduling have attracted great attention in production systems. This paper studies an energy-efficient distributed blocking flowshop scheduling problem where several heterogeneous factories cooperate to process jobs. A knowledge-based iterated Pareto greedy algorithm (KBIPG) is proposed to minimize simultaneously the makespan and total energy consumption. Based on a speed scaling framework that allows machines to process different jobs at different speed levels or remain in the standby mode, the KBIPG has two stages, where the difference lies in whether to adjust the processing speed. First, two multi-objective insertion procedures are proposed to form construction procedures. Second, we presented an efficient destruction procedure for each stage separately. Third, two local intensification methods are designed based on adjusting machine speeds, including the energy-saving procedure that optimizes the total energy consumption and the speedup-based local search procedure that optimizes the makespan. The KBIPG algorithm starts with generating solutions under various initial machine speed matrixes with different levels and then goes through a two-stage loop based on the proposed procedures. Computational experiments and comparisons with five algorithms demonstrate the effectiveness of the proposed KBIPG algorithm.

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Data availability

The data generated and analyzed during this study are available in the GitHub repository: https://github.com/ShuaiChen-lgtm/NCAA_Data.git.

References

  1. Gielen D, Bennaceur K, Kerr T, Tam C, Tanaka K, Taylor M, Taylor P (2007) IEA, tracking industrial energy efficiency and CO2 emissions

  2. Gao KZ, Huang Y, Sadollah A, Wang L (2020) A review of energy-efficient scheduling in intelligent production systems. Complex Intell Syst 6(2):237–249. https://doi.org/10.1007/s40747-019-00122-6

    Article  Google Scholar 

  3. Öztop H, Tasgetiren MF, Eliiyi DT, Pan Q-K, Kandiller L (2020) An energy-efficient permutation flowshop scheduling problem. Expert Syst Appl 150:113279. https://doi.org/10.1016/j.eswa.2020.113279

    Article  Google Scholar 

  4. Zhang B, Pan Q-K, Gao L, Meng L-L, Li X-Y, Peng K-K (2020) A three-stage multiobjective approach based on decomposition for an energy-efficient hybrid flow shop scheduling problem. IEEE Trans Syst Man Cybern-Syst 50(12):4984–4999. https://doi.org/10.1109/tsmc.2019.2916088

    Article  Google Scholar 

  5. Ding J-Y, Song S, Wu C (2016) Carbon-efficient scheduling of flow shops by multi-objective optimization. Eur J Oper Res 248(3):758–771. https://doi.org/10.1016/j.ejor.2015.05.019

    Article  MathSciNet  MATH  Google Scholar 

  6. Lei D, Gao L, Zheng Y (2018) A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop. IEEE Trans Eng Manag 65(2):330–340. https://doi.org/10.1109/tem.2017.2774281

    Article  Google Scholar 

  7. Luo S, Zhang L, Fan Y (2019) Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J Clean Prod 234:1365–1384. https://doi.org/10.1016/j.jclepro.2019.06.151

    Article  Google Scholar 

  8. Ruiz R, Pan Q-K, Naderi B (2019) Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega-Int J Manag Sci 83:213–222. https://doi.org/10.1016/j.omega.2018.03.004

    Article  Google Scholar 

  9. Meng T, Pan Q-K (2020) A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2020.100804

    Article  Google Scholar 

  10. Huang J-P, Pan Q-K, Miao Z-H, Gao L (2021) Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times. Eng Appl Artif Intell 97:104016. https://doi.org/10.1016/j.engappai.2020.104016

    Article  Google Scholar 

  11. Naderi B, Ruiz R (2010) The distributed permutation flowshop scheduling problem. Comput Oper Res 37(4):754–768. https://doi.org/10.1016/j.cor.2009.06.019

    Article  MathSciNet  MATH  Google Scholar 

  12. Ribas I, Companys R, Tort-Martorell X (2017) Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Syst Appl 74:41–54. https://doi.org/10.1016/j.eswa.2017.01.006

    Article  Google Scholar 

  13. Ying K-C, Lin S-W, Cheng C-Y, He C-D (2017) Iterated reference greedy algorithm for solving distributed no-idle permutation flowshop scheduling problems. Comput Ind Eng 110:413–423. https://doi.org/10.1016/j.cie.2017.06.025

    Article  Google Scholar 

  14. Mao JY, Pan QK, Miao ZH, Gao L (2021) An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance. Expert Syst Appl 169:114495. https://doi.org/10.1016/j.eswa.2020.114495

    Article  Google Scholar 

  15. Huang J-P, Pan Q-K, Gao L (2020) An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times. Swarm Evol Comput 59:100742. https://doi.org/10.1016/j.swevo.2020.100742

    Article  Google Scholar 

  16. Chen J, Wang L, He X, Huang D (2019) A probability model-based memetic algorithm for distributed heterogeneous flow-shop scheduling. In: 2019 IEEE congress on evolutionary computation (CEC). pp 411–418

  17. Li H, Li X, Gao L (2021) A discrete artificial bee colony algorithm for the distributed heterogeneous no-wait flowshop scheduling problem. Appl Soft Comput 100:106946. https://doi.org/10.1016/j.asoc.2020.106946

    Article  Google Scholar 

  18. Ronconi DP (2004) A note on constructive heuristics for the flowshop problem with blocking. Int J Prod Econ 87(1):39–48. https://doi.org/10.1016/S0925-5273(03)00065-3

    Article  Google Scholar 

  19. Gong H, Tang L, Duin CW (2010) A two-stage flow shop scheduling problem on a batching machine and a discrete machine with blocking and shared setup times. Comput Oper Res 37(5):960–969. https://doi.org/10.1016/j.cor.2009.08.001

    Article  MATH  Google Scholar 

  20. Grabowski J, Pempera J (2000) Sequencing of jobs in some production system. Eur J Oper Res 125(3):535–550. https://doi.org/10.1016/S0377-2217(99)00224-6

    Article  MathSciNet  MATH  Google Scholar 

  21. Ying K-C, Lin S-W (2017) Minimizing makespan in distributed blocking flowshops using hybrid iterated greedy algorithms. IEEE Access 5:15694–15705. https://doi.org/10.1109/access.2017.2732738

    Article  Google Scholar 

  22. Zhang G, Xing K, Cao F (2018) Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion. Eng Appl Artif Intell 76:96–107. https://doi.org/10.1016/j.engappai.2018.09.005

    Article  Google Scholar 

  23. Shao Z, Pi D, Shao W (2020) Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment. Expert Syst Appl 145:113147. https://doi.org/10.1016/j.eswa.2019.113147

    Article  Google Scholar 

  24. Zhao F, Zhao L, Wang L, Song H (2020) An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113678

    Article  Google Scholar 

  25. Chen S, Pan Q-K, Gao L (2021) Production scheduling for blocking flowshop in distributed environment using effective heuristics and iterated greedy algorithm. Robot Comput-Integr Manuf 71:102155. https://doi.org/10.1016/j.rcim.2021.102155

    Article  Google Scholar 

  26. Ribas I, Companys R, Tort-Martorell X (2019) An iterated greedy algorithm for solving the total tardiness parallel blocking flow shop scheduling problem. Expert Syst Appl 121:347–361. https://doi.org/10.1016/j.eswa.2018.12.039

    Article  Google Scholar 

  27. Miyata HH, Nagano MS (2022) An iterated greedy algorithm for distributed blocking flow shop with setup times and maintenance operations to minimize makespan. Comput Ind Eng 171:108366. https://doi.org/10.1016/j.cie.2022.108366

    Article  Google Scholar 

  28. Han X, Han Y, Zhang B, Qin H, Li J, Liu Y, Gong D (2022) An effective iterative greedy algorithm for distributed blocking flowshop scheduling problem with balanced energy costs criterion. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2022.109502

    Article  Google Scholar 

  29. Shao Z, Shao W, Pi D (2022) LS-HH: a learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2022.3174915

    Article  Google Scholar 

  30. Deng J, Wang L, Wu C, Wang J, Zheng X (2016) A competitive memetic algorithm for carbon-efficient scheduling of distributed flow-shop. In: 2016 international conference on intelligent computing. pp 476–488

  31. Wang J-J, Wang L (2020) A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop. IEEE Trans Syst Man Cybern-Syst 50(5):1805–1819. https://doi.org/10.1109/tsmc.2017.2788879

    Article  Google Scholar 

  32. Chen J-F, Wang L, Peng Z-P (2019) A collaborative optimization algorithm for energy-efficient multi-objective distributed no-idle flow-shop scheduling. Swarm Evol Comput 50:100557. https://doi.org/10.1016/j.swevo.2019.100557

    Article  Google Scholar 

  33. Pan Z, Lei D, Wang L (2020) A knowledge-based two-population optimization algorithm for distributed energy-efficient parallel machines scheduling. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3026571

    Article  Google Scholar 

  34. Jiang E-D, Wang L, Peng Z-P (2020) Solving energy-efficient distributed job shop scheduling via multi-objective evolutionary algorithm with decomposition. Swarm Evol Comput 58:100745. https://doi.org/10.1016/j.swevo.2020.100745

    Article  Google Scholar 

  35. Wang G, Li X, Gao L, Li P (2021) An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop. Ann Oper Res 310:223–255. https://doi.org/10.1007/s10479-021-03952-1

    Article  Google Scholar 

  36. Framinan JM, Leisten R (2008) A multi-objective iterated greedy search for flowshop scheduling with makespan and flowtime criteria. Or Spectr 30(4):787–804. https://doi.org/10.1007/s00291-007-0098-z

    Article  MathSciNet  MATH  Google Scholar 

  37. Minella G, Ruiz R, Ciavotta M (2011) Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems. Comput Oper Res 38(11):1521–1533. https://doi.org/10.1016/j.cor.2011.01.010

    Article  MathSciNet  Google Scholar 

  38. Li W, Zhou X, Yang C, Fan Y, Wang Z, Liu Y (2022) Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery. Inf Fusion 79:110–123. https://doi.org/10.1016/j.inffus.2021.10.002

    Article  Google Scholar 

  39. Nawaz M, Enscore EE, Ham I (1983) A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1):91–95. https://doi.org/10.1016/0305-0483(83)90088-9

    Article  Google Scholar 

  40. Garcia S, Molina D, Lozano M, Herrera F (2009) A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J Heuristics 15(6):617–644. https://doi.org/10.1007/s10732-008-9080-4

    Article  MATH  Google Scholar 

  41. Goh CK, Ong Y, Tan K (2009) Multi-objective memetic algorithms

  42. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30. https://doi.org/10.5555/1248547.1248548

    Article  MathSciNet  MATH  Google Scholar 

  43. Cai S, Yang K, Liu K (2018) Multi-objective optimization of the distributed permutation flow shop scheduling problem with transportation and eligibility constraints. J Oper Res Soc China 6(3):391–416. https://doi.org/10.1007/s40305-017-0165-3

    Article  MathSciNet  MATH  Google Scholar 

  44. Jiang ED, Wang L (2019) An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time. Int J Prod Res 57(6):1756–1771. https://doi.org/10.1080/00207543.2018.1504251

    Article  Google Scholar 

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Acknowledgements

This research is partially supported by the National Key Research and Development Program 2020YFB1708200, the National Natural Science Fund for Distinguished Young Scholars of China under Grant 51825502, the National Science Foundation of China 62273221 and 61973203, the Program of Shanghai Academic/Technolgical Research Learder 21XD1401000 and Shanghai Key Laboratory of Power station Automation Technology.

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, People’s Republic of China

    Shuai Chen, Quan-Ke Pan, Zhong-Hua Miao & Chen Peng

  2. School of Computer Science, Liaocheng University, Liaocheng, 252000, People’s Republic of China

    Quan-Ke Pan

  3. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, People’s Republic of China

    Liang Gao

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Chen, S., Pan, QK., Gao, L. et al. Energy-efficient distributed heterogeneous blocking flowshop scheduling problem using a knowledge-based iterated Pareto greedy algorithm. Neural Comput & Applic 35, 6361–6381 (2023). https://doi.org/10.1007/s00521-022-08012-8

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