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A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance

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  • Liu, Ying
  • Dong, Haibo
  • Lohse, Niels
  • Petrovic, Sanja

Abstract

Increasing energy price and requirements to reduce emission are new challenges faced by manufacturing enterprises. A considerable amount of energy is wasted by machines due to their underutilisation. Consequently, energy saving can be achieved by turning off the machines when they lay idle for a comparatively long period. Otherwise, turning the machine off and back on will consume more energy than leave it stay idle. Thus, an effective way to reduce energy consumption at the system level is by employing intelligent scheduling techniques which are capable of integrating fragmented short idle periods on the machines into large ones. Such scheduling will create opportunities for switching off underutilised resources while at the same time maintaining the production performance. This paper introduces a model for the bi-objective optimisation problem that minimises the total non-processing electricity consumption and total weighted tardiness in a job shop. The Turn off/Turn on is applied as one of the electricity saving approaches. A novel multi-objective genetic algorithm based on NSGA-II is developed. Two new steps are introduced for the purpose of expanding the solution pool and then selecting the elite solutions. The research presented in this paper is focused on the classical job shop environment, which is widely used in the manufacturing industry and provides considerable opportunities for energy saving. The algorithm is validated on job shop problem instances to show its effectiveness.

Suggested Citation

  • Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    • Handle: RePEc:eee:proeco:v:179:y:2016:i:c:p:259-272
    DOI: 10.1016/j.ijpe.2016.06.019
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    Cited by:

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    2. Wichmann, Matthias Gerhard & Johannes, Christoph & Spengler, Thomas Stefan, 2019. "Energy-oriented Lot-Sizing and Scheduling considering energy storages," International Journal of Production Economics, Elsevier, vol. 216(C), pages 204-214.
    3. Anghinolfi, Davide & Paolucci, Massimo & Ronco, Roberto, 2021. "A bi-objective heuristic approach for green identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 289(2), pages 416-434.
    4. Ivan Ferretti & Matteo Camparada & Lucio Enrico Zavanella, 2022. "Queuing Theory-Based Design Methods for the Definition of Power Requirements in Manufacturing Systems," Energies, MDPI, vol. 15(20), pages 1-14, October.
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    6. Shun Jia & Shang Wang & Jingxiang Lv & Wei Cai & Na Zhang & Zhongwei Zhang & Shuowei Bai, 2021. "Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption," Sustainability, MDPI, vol. 13(24), pages 1-23, December.
    7. Iqra Asghar & Biswajit Sarkar & Sung-jun Kim, 2019. "Economic Analysis of an Integrated Production–Inventory System under Stochastic Production Capacity and Energy Consumption," Energies, MDPI, vol. 12(16), pages 1-27, August.
    8. Feng, Yanling & Li, Guo & Sethi, Suresh P., 2018. "A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing," International Journal of Production Economics, Elsevier, vol. 196(C), pages 269-283.
    9. Park, Myoung-Ju & Ham, Andy, 2022. "Energy-aware flexible job shop scheduling under time-of-use pricing," International Journal of Production Economics, Elsevier, vol. 248(C).
    10. Masmoudi, Oussama & Delorme, Xavier & Gianessi, Paolo, 2019. "Job-shop scheduling problem with energy consideration," International Journal of Production Economics, Elsevier, vol. 216(C), pages 12-22.
    11. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    12. João M. R. C. Fernandes & Seyed Mahdi Homayouni & Dalila B. M. M. Fontes, 2022. "Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review," Sustainability, MDPI, vol. 14(10), pages 1-34, May.
    13. Zhongwei Zhang & Lihui Wu & Tao Peng & Shun Jia, 2018. "An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment," Sustainability, MDPI, vol. 11(1), pages 1-21, December.
    14. Seokgi Lee & Mona Issabakhsh & Hyun Woo Jeon & Seong Wook Hwang & Byung Chung, 2020. "Idle time and capacity control for a single machine scheduling problem with dynamic electricity pricing," Operations Management Research, Springer, vol. 13(3), pages 197-217, December.
    15. Matthias Gerhard Wichmann & Christoph Johannes & Thomas Stefan Spengler, 2019. "An extension of the general lot-sizing and scheduling problem (GLSP) with time-dependent energy prices," Journal of Business Economics, Springer, vol. 89(5), pages 481-514, July.
    16. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    17. Chen Peng & Tao Peng & Yi Zhang & Renzhong Tang & Luoke Hu, 2018. "Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop," Energies, MDPI, vol. 11(12), pages 1-15, December.
    18. Heydar, Mojtaba & Mardaneh, Elham & Loxton, Ryan, 2022. "Approximate dynamic programming for an energy-efficient parallel machine scheduling problem," European Journal of Operational Research, Elsevier, vol. 302(1), pages 363-380.

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