Open Access
Issue
E3S Web Conf.
Volume 170, 2020
6th International Conference on Energy and City of the Future (EVF’2019)
Article Number 01006
Number of page(s) 5
Section Energy and Management
DOI https://doi.org/10.1051/e3sconf/202017001006
Published online 28 May 2020
  1. F.A. Rodammer et K. Preston White, A recent survey of production scheduling, IEEE Transaction on Systems, Man and Cybernetics, 6-18, (1999). [Google Scholar]
  2. J. Yan, L. Li, F. Zhao, F. Zhang, Q. Zhao, A multi-level optimization approach for energy-efficient flexible flow shop scheduling, J. Cleaner Prod. 137, 1543–1552, (2016). [CrossRef] [Google Scholar]
  3. H. Luo, B. Du, G.Q. Huang, H. Chen, X. Li, Hybrid flow shop scheduling considering machine electricity consumption cost, Int. J. Prod. Econ. 146, 423–439, (2013). [Google Scholar]
  4. A.A.G. Bruzzone, D. Anghinolfi, M. Paolucci, F. Tonelli, Energy-aware scheduling for improving manufacturing process sustainability: a mathematical model for flexible flow shops, CIRP Ann. - Manuf. Technol. 61, 459–462, (2012). [CrossRef] [Google Scholar]
  5. M. Dai, D.B. Tang, A. Giret, M.A. Salido, W.D. Li, Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm, Robo. Comput.-Int. Manuf. 29 (5),418–429, (2013). [CrossRef] [Google Scholar]
  6. H. Luo, D. Du, G.Q. Huang, H.P. Chen, X.L. Li, Hybrid flow shop scheduling considering machine electricity consumption cost, Int. J. Prod. Econ. 146 (2), 423–439, (2013). [Google Scholar]
  7. D.B. Tang, M. Dai, M.A. Salido, A. Giret, Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization, Comput. Ind. (30) 223–232, (2015). [Google Scholar]
  8. D.M. Lei, L. Gao, Y.L. Zheng, A novel teaching-learning-based optimization algorithm for energy-efficient scheduling in hybrid flow shop, IEEE Trans. Eng. Manag. 65 (2) 330–340, (2018). [Google Scholar]
  9. Jiang, Z., & Le, Z. Study on multi-objective flexible job-shop scheduling problem considering energy consumption. Journal of Industrial Engineering and Management (JIEM), 7(3), 589-604, (2014). [Google Scholar]
  10. Mokhtari, H., & Hasani, A. An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Computers & Chemical Engineering, 104, 339-352, (2017). [Google Scholar]
  11. 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, 147, 470-484, (2017). [Google Scholar]
  12. Li, J. Q., Sang, H. Y., Han, Y. Y., Wang, C. G., & Gao, K. Z. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. Journal of Cleaner Production, 181, 584-598, (2018). [Google Scholar]
  13. Schulz, S. A multi-criteria MILP formulation for energy aware hybrid flow shop scheduling. In Operations Research Proceedings 2016 (pp. 543-549). Springer, Cham, (2018). [CrossRef] [Google Scholar]
  14. Schulz, S., Neufeld, J. S., & Buscher, U. A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. Journal of Cleaner Production, 224, 421-434, (2019). [Google Scholar]
  15. Salido, M. A., Escamilla, J., Barber, F., Giret, A., Tang, D., & Dai, M. Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems. AI EDAM, 30(3), 300-312, (2016). [Google Scholar]
  16. Wang, F., Deng, G., Jiang, T., & Zhang, S. Multi-objective parallel variable neighborhood search for energy consumption scheduling in blocking flow shops. IEEE Access, 6, 68686-68700, (2018). [Google Scholar]
  17. Guo, C., & Lei, D. Multi-objective Flexible Job Shop Scheduling Problem with Energy Consumption Constraint Using Imperialist Competitive Algorithm. In International Conference on Intelligent Computing (pp. 659-669). Springer, Cham, (2018). [Google Scholar]
  18. Zhong, L. C., Qian, B., Hu, R., & Zhang, C. S. The Hybrid Shuffle Frog Leaping Algorithm Based on Cuckoo Search for Flow shop Scheduling with the Consideration of Energy Consumption. In International Conference on Intelligent Computing (pp. 649-658). Springer, Cham, (2018). [Google Scholar]
  19. Lu, C., Gao, L., Pan, Q., Li, X., & Zheng, J. A multi-objective cellular grey wolf optimizer for hybrid flow shop scheduling problem considering noise pollution. Applied Soft Computing, 75, 728-749, (2019). [Google Scholar]
  20. Dai, M., Tang, D., Giret, A., & Salido, M. A. Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing, 59, 143-157, (2019). [Google Scholar]
  21. Li, M., Lei, D., & Cai, J. Two-level imperialist competitive algorithm for energy-efficient hybrid flow shop scheduling problem with relative importance of objectives. Swarm and Evolutionary Computation, (2019). [Google Scholar]
  22. Zhang, B., Pan, Q. K., Gao, L., Li, X. Y., Meng, L. L., & Peng, K. K. A multiobjective evolutionary algorithm based on decomposition for hybrid flow shop green scheduling problem. Computers & Industrial Engineering, 136, 325-344, (2019).. [Google Scholar]
  23. Zhou, B., & Liu, W. Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 0959651819827705, (2019). [Google Scholar]
  24. Jiang, S. L., & Zhang, L. Energy-Oriented Scheduling for Hybrid Flow shop With Limited Buffers Through Efficient Multi-Objective Optimization. IEEE Access, 7, 34477-34487, (2019). [Google Scholar]

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