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Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level

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Abstract

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW · h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.

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References

  1. Du Y, Li C. Implementing energy-saving and environmental-benign paradigm: Machine tool remanufacturing by OEMs in China. Journal of Cleaner Production, 2014, 66: 272–279

    Article  Google Scholar 

  2. Xiang W, Ming C. Implementing extended producer responsibility: Vehicle remanufacturing in China. Journal of Cleaner Production, 2011, 19(6–7): 680–686

    Article  Google Scholar 

  3. International Energy Agency. World Energy Outlook 2016. Paris: Organization for Economic Co-operation and Development, 2016, 284–285

    Book  Google Scholar 

  4. Central People’s Government of China. Notification of energy conservation and emission reduction strategy for “13th Five-Year Plan” delivered by the State Council. Available at the State Council of the People’s Republic of China website, 2018-11-20

  5. Chu H, Cao Q, Fei R. MAS-based production scheduling system for manufacturing cell-based workshop. Frontiers of Mechanical Engineering in China, 2006, 1(4): 375–380

    Article  Google Scholar 

  6. Jiang Z, Jiang Y, Wang Y, et al. A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. Journal of Intelligent Manufacturing, 2019, 30(1): 19–32

    Article  Google Scholar 

  7. Gutowski T G, Sahni S, Boustani A, et al. Remanufacturing and energy savings. Environmental Science & Technology, 2011, 45(10): 4540–4547

    Article  Google Scholar 

  8. Sutherland J W, Adler D P, Haapala K R, et al. A comparison of manufacturing and remanufacturing energy intensities with application to diesel engine production. CIRP Annals-Manufacturing Technology, 2008, 57(1): 5–8

    Article  Google Scholar 

  9. Yang S S, Ngiam H Y, Ong S K, et al. The impact of automotive product remanufacturing on environmental performance. Procedia CIRP, 2015, 29: 774–779

    Article  Google Scholar 

  10. Lage Junior M, Godinho Filho M. Master disassembly scheduling in a remanufacturing system with stochastic routings. Central European Journal of Operations Research, 2017, 25(1): 123–138

    Article  Google Scholar 

  11. Sun H, Chen W, Liu B, et al. Economic lot scheduling problem in a remanufacturing system with returns at different quality grades. Journal of Cleaner Production, 2018, 170: 559–569

    Article  Google Scholar 

  12. Guide V D R Jr, Srivastava R, Kraus R E. Product structure complexity and scheduling of operations in recoverable manufacturing. International Journal of Production Research, 1997, 35(11): 3179–3200

    Article  Google Scholar 

  13. Zhang R, Ong S K, Nee A Y C. A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Applied Soft Computing, 2015, 37: 521–532

    Article  Google Scholar 

  14. Wen H, Liu M, Liu C, et al. Remanufacturing production planning with compensation function approximation method. Applied Mathematics and Computation, 2015, 256: 742–753

    Article  MathSciNet  Google Scholar 

  15. Singh P, Khan B, Vidyarthi A, et al. Energy-aware online non-clairvoyant scheduling using speed scaling with arbitrary power function. Applied Sciences, 2019, 9(7): 1467

    Article  Google Scholar 

  16. Liu G S, Zhou Y, Yang H D. Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time. Journal of Cleaner Production, 2017, 147: 470–484

    Article  Google Scholar 

  17. Shrouf F, Ordieres-Meré J, García-Sánchez A, et al. Optimizing the production scheduling of a single machine to minimize total energy consumption costs. Journal of Cleaner Production, 2014, 67: 197–207

    Article  Google Scholar 

  18. Tang D, Dai M, Salido M A, et al. Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 2016, 81: 82–95

    Article  Google Scholar 

  19. Abdullah S, Abdolrazzagh-Nezhad M. Fuzzy job-shop scheduling problems: A review. Information Sciences, 2014, 278: 380–407

    Article  MathSciNet  Google Scholar 

  20. Lei D. Fuzzy job shop scheduling problem with availability constraints. Computers & Industrial Engineering, 2010, 58(4): 610–617

    Article  Google Scholar 

  21. Sakawa M, Kubota R. Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. European Journal of Operational Research, 2000, 120(2): 393–407

    Article  MathSciNet  Google Scholar 

  22. Wang S, Wang L, Xu Y, et al. An effective estimation of distribution algorithm for the flexible job-shop scheduling problem with fuzzy processing time. International Journal of Production Research, 2013, 51(12): 3778–3793

    Article  Google Scholar 

  23. Lei D. A genetic algorithm for flexible job shop scheduling with fuzzy processing time. International Journal of Production Research, 2010, 48(10): 2995–3013

    Article  Google Scholar 

  24. Gao K Z, Suganthan P N, Pan Q K, et al. An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 2016, 65: 52–67

    Article  Google Scholar 

  25. Geng Z, Zou Y. Study on job shop fuzzy scheduling problem based on genetic algorithm. Computer Integrated Manufacturing Systems, 2002, 8: 616–620 (in Chinese)

    Google Scholar 

  26. Sakawa M, Mori T. Efficient genetic algorithm for job-shop scheduling problems with fuzzy processing time and fuzzy duedate. Computers & Industrial Engineering, 1999, 36(2): 325–341

    Article  Google Scholar 

  27. Lei D. Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling. Applied Soft Computing, 2012, 12(8): 2237–2245

    Article  Google Scholar 

  28. Wang X, Chan H K. An integrated fuzzy approach for evaluating remanufacturing alternatives of a product design. Journal of Remanufacturing, 2013, 3(1): 10

    Article  Google Scholar 

  29. Lin F T. Fuzzy job-shop scheduling based on ranking level (λ, 1) interval-valued fuzzy numbers. IEEE Transactions on Fuzzy Systems, 2002, 10(4): 510–522

    Article  Google Scholar 

  30. Balin S. Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Information Sciences, 2011, 181(17): 3551–3569

    Article  Google Scholar 

  31. Zhu Q, Sarkis J, Lai K. Supply chain-based barriers for truck-engine remanufacturing in China. Transportation Research Part E: Logistics and Transportation Review, 2015, 74: 94–108

    Article  Google Scholar 

  32. Wang X, Cao L. Genetic Algorithm and Implementation of Software Applications. Xi’an: Xi’an Jiaotong University Press, 2002, 25–39 (in Chinese)

    Google Scholar 

  33. Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 1994, 24(4): 656–667

    Article  Google Scholar 

  34. Farhy L S, Straume M, Johnson M L, et al. A construct of interactive feedback control of the GH axis in the male. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 2001, 281(1): R38–R51

    Google Scholar 

  35. Wei L, Li X, Li Y, et al. Optimization of tandem cold rolling schedule based on improved adaptive genetic algorithm. Journal of Mechanical Engineering, 2010, 46(16): 136–141 (in Chinese)

    Article  Google Scholar 

  36. Gonçalves J F, Resende M G C. Random-key genetic algorithms. In: Martí R, Panos P, Resende M, eds. Handbook of Heuristics. Cham: Springer, 2016, 1–13

    Google Scholar 

  37. Li M. Research on remanufactured flexible job shop scheduling based on damaged parts. Thesis for the Master’s Degree. Dalian: Dalian University of Technology, 2019, 29–30 (in Chinese)

    Google Scholar 

  38. Peng S, Li T, Zhao J, et al. Petri net-based scheduling strategy and energy modeling for the cylinder block remanufacturing under uncertainty. Robotics and Computer-integrated Manufacturing, 2019, 58: 208–219

    Article  Google Scholar 

  39. Gong X, De Pessemier T, Joseph W, et al. A generic method for energy-efficient and energy-cost-effective production at the unit process level. Journal of Cleaner Production, 2016, 113: 508–522

    Article  Google Scholar 

  40. Liu C, Dang F, Li W, et al. Production planning of multi-stage multi-option seru production systems with sustainable measures. Journal of Cleaner Production, 2015, 105: 285–299

    Article  Google Scholar 

  41. Peng S, Li T, Tang Z, et al. Comparative life cycle assessment of remanufacturing cleaning technologies. Journal of Cleaner Production, 2016, 137: 475–489

    Article  Google Scholar 

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Acknowledgements

The authors highly appreciate the investigation opportunities provided by SINOTRUK, Jinan Fuqiang Power Co., Ltd. We are also grateful for the financial support from the National Natural Science Foundation of China (Grant Nos. 51775086 and 51605169), and Natural Science Foundation of Guangdong Province China (Grant No. 2014A030310345).

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Authors and Affiliations

  1. School of Mechanical & Electronical Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Jiali Zhao

  2. Institute of Sustainable Design and Manufacturing, Dalian University of Technology, Dalian, 116024, China

    Shitong Peng, Tao Li, Mengyun Li & Hongchao Zhang

  3. College of Engineering, South China Agricultural University, Guangzhou, 510642, China

    Shengping Lv

  4. Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX, 79409, USA

    Hongchao Zhang

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  1. Jiali Zhao
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  2. Shitong Peng
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  3. Tao Li
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  5. Mengyun Li
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Correspondence to Shitong Peng.

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Zhao, J., Peng, S., Li, T. et al. Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level. Front. Mech. Eng. 14, 474–488 (2019). https://doi.org/10.1007/s11465-019-0560-z

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  • DOI: https://doi.org/10.1007/s11465-019-0560-z

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