Reinforcement Learning-Based Estimation of Distribution Algorithm for Energy-Efficient Distributed Heterogeneous Flexible Job Shop Scheduling Problem
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5578-3_15
Reference19 articles.
1. Okwudire, C.E., Madhyastha, H.V.: Distributed manufacturing for and by the masses. Science 372(6540), 341–342 (2021)
2. Dauzère-Pérès, S., Ding, J., Shen, L., Tamssaouet, K.: The flexible job shop scheduling problem: A review. Eur. J. Oper. Res. 314(2), 409–432 (2024)
3. Luo, Q., Deng, Q.W., Gong, G.L., Guo, X., Liu, X.H.: A distributed flexible job shop scheduling problem considering worker arrangement using an improved memetic algorithm. Expert Syst. Appl. 207, 117984 (2022)
4. Luo, C., Gong, W.Y., Li, R., Lu, C.: Problem-specific knowledge MOEA/D for energy-efficient scheduling of distributed permutation flow shop in heterogeneous factories. Eng. Appl. Artif. Intell. 123, 106454 (2023)
5. Shao, W.S., Shao, Z.S., Pi, D.C.: A network memetic algorithm for energy and labor-aware distributed heterogeneous hybrid flow shop scheduling problem. Swarm Evol. Comput. 75, 101190 (2022)
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