Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources

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Authors

Dalila Fontes¹ , S. Mahdi Homayouni¹ , Joao Fernandes¹

1. INESCTEC, Porto, Portugal

Conference / event

French German Portuguese Conference on Optimization 2022, May 2022 (Porto, Portugal)

Poster summary

This poster summarizes the findings of a paper by Fontes et al. (2022). The work addresses the energy-efficient job shop scheduling problem with transport resources. It introduces two types of speed adjustable resources - machines where jobs are processed and vehicles which transport jobs - and aims to find solutions that balance makespan and total energy consumption. This problem involves determining the processing speed, sequence of operations, allocation of transport tasks to vehicles, vehicle traveling speed, and sequence of tasks for each machine and vehicle. The paper presents a bi-objective mixed-integer linear programming model and a novel multi-objective multi-population biased random key genetic algorithm (mpBRKGA) to solve the problem. Computational experiments demonstrate the effectiveness and efficiency of the algorithm, even for larger problem instances and compared to another frequently used algorithm. An extensive analysis of time and energy trade-offs is included, providing insights for managers facing similar complex problems.

Keywords

Job shop scheduling problem, Transport resources, Energy efficient, Mixed integer linear programming model, BRKGA, Multi-population

Research areas

Mathematics, Energy Engineering, Environmental Engineering

References

1. Dai, M., Tang, D., Giret, A., & Salido, M. A. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing, 59(October 2018), 143–157. https://doi.org/10.1016/j.rcim.2019.04.006
2. Fernandes, J. M. R. C., Homayouni, S. M., & Fontes, D. B. M. M. (2022). Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review. Sustainability, 14(10), 6264. https://doi.org/10.3390/su14106264
3. Gahm, C., Denz, F., Dirr, M., & Tuma, A. (2016). Energy-efficient scheduling in manufacturing companies: A review and research framework. European Journal of Operational Research, 248(3), 744–757. https://doi.org/10.1016/j.ejor.2015.07.017
4. Gonçalves, J. F., & Resende, M. G. C. (2011). Biased random-key genetic algorithms for combinatorial optimization. Journal of Heuristics, 17(5), 487–525. https://doi.org/10.1007/s10732-010-9143-1
5. He, L., Chiong, R., Li, W., Dhakal, S., Cao, Y., & Zhang, Y. (2021). Multiobjective Optimization of Energy-efficient Job-Shop Scheduling with Dynamic Reference Point-based Fuzzy Relative Entropy. IEEE Transactions on Industrial Informatics, 3203(c), 1–1. https://doi.org/10.1109/tii.2021.3056425
6. Para, J., Del Ser, J., & Nebro, A. J. (2022). Energy-Aware Multi-Objective Job Shop Scheduling Optimization with Metaheuristics in Manufacturing Industries: A Critical Survey, Results, and Perspectives. Applied Sciences (Switzerland), 12(3). https://doi.org/10.3390/app12031491
7. Salido, M. A., Escamilla, J., Giret, A., & Barber, F. (2016). A genetic algorithm for energy-efficiency in job-shop scheduling. International Journal of Advanced Manufacturing Technology, 85(5–8), 1303–1314. https://doi.org/10.1007/s00170-015-7987-0
8. Zhou, B., & Lei, Y. (2021). Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem. Applied Soft Computing, 111, 107717. https://doi.org/10.1016/j.asoc.2021.107717

Funding

1. Federação da Ciencia e Tecnologia (No. SFRH/BD/146253/2019)

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available elsewhere (e.g., repository).
fastmanufacturingproject.wordpress.com/problem-instances

Creative Commons license

Copyright © 2023 Fontes et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.