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A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants

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Journal of the Operational Research Society

Abstract

The hot metal is produced from the blast furnaces in the iron plant and should be processed as soon as possible in the subsequent steel plant for energy saving. Therefore, the release times of hot metal have an influence on the scheduling of a steel plant. In this paper, the scheduling problem with release times for steel plants is studied. The production objectives and constraints related to the release times are clarified, and a new multi-objective scheduling model is built. For the solving of the multi-objective optimization, a hybrid multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. In the hybrid multi-objective algorithm, an efficient decoding heuristic (DH) and a non-dominated solution construction method (NSCM) are proposed based on the problem-specific characteristics. During the evolutionary process, individuals with different solutions may have a same chromosome because the NSCM constructs non-dominated solutions just based on the solution found by DH. Therefore, three operations in the original NSGA-II process are modified to avoid identical chromosomes in the evolutionary operations. Computational tests show that the proposed hybrid algorithm based on NSGA-II is feasible and effective for the multi-objective scheduling with release times.

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References

  • Atighehchian A, Bijari M and Tarkesh H (2009). A novel hybrid algorithm for scheduling steel-making continuous casting production. Computers & Operations Research 36(8): 2450–2461.

    Article  Google Scholar 

  • Deb K (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley: Chichester, UK.

    Google Scholar 

  • Deb K, Pratap A, Agarwal S and Meyarivan T (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2): 182–197.

    Article  Google Scholar 

  • Falkenauer E and Bouffoix S (1991). A genetic algorithm for job shop. In: Proceedings of the 1991 IEEE international conference on robotics and automation. IEEE, pp 824–829.

  • Goldberg DE (1989). Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley Publishing Company: Boston.

    Google Scholar 

  • Harjunkoski I and Grossmann IE (2001). A decomposition approach for the scheduling of a steel plant production. Computers & Chemical Engineering 25(11–12): 1647–1660.

    Article  Google Scholar 

  • Huegler PA and Vasko FJ (2007). Metaheuristics for meltshop scheduling in the steel industry. Journal of the Operational Research Society 58(6): 791–796.

    Article  Google Scholar 

  • Kumar V, Kumar S, Tiwari MK and Chan FTS (2006). Auction-based approach to resolve the scheduling problem in the steel making process. International Journal of Production Research 44(8): 1503–1522.

    Article  Google Scholar 

  • Li J, Xiao X, Tang QH and Floudas CA (2012). Production scheduling of a large-scale steelmaking continuous casting process via unit-specific event-based continuous-time models: Short-term and medium-term scheduling. Industrial & Engineering Chemistry Research 51(21): 7300–7319.

    Article  Google Scholar 

  • Li JQ, Pan QK, Mao K and Suganthan PN (2014). Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm. Knowledge-Based Systems 72: 28–36.

    Article  Google Scholar 

  • Mao K, Pan QK, Pang XF and Chai TY (2014). A novel lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process. European Journal of Operational Research 236(1): 51–60.

    Article  Google Scholar 

  • Missbauer H, Hauber W and Stadler W (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research 47(15): 4147–4172.

    Article  Google Scholar 

  • Oguz C and Ercan M (2005). A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Journal of Scheduling 8(4): 323–351.

    Article  Google Scholar 

  • Pacciarelli D and Pranzo M (2004). Production scheduling in a steelmaking-continuous casting plant. Computers & Chemical Engineering 28(12): 2823–2835.

    Article  Google Scholar 

  • Pan QK, Wang L, Mao K, Zhao JH and Zhang M (2013). An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process. IEEE Transactions on Automation Science and Engineering 10(2): 307–322.

    Article  Google Scholar 

  • Ruiz R and Vazquez-Rodriguez JA (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research 205(1): 1–18.

    Article  Google Scholar 

  • Tang LX, Liu JY, Rong AY and Yang ZH (2000). A mathematical programming model for scheduling steelmaking- continuous casting production. European Journal of Operational Research 120(2): 423–435.

    Article  Google Scholar 

  • Tang LX, Liu JY, Rong AY and Yang ZH (2001). A review of planning and scheduling systems and methods for integrated steel production. European Journal of Operational Research 133(1): 1–20.

    Article  Google Scholar 

  • Tang LX, Luh PB, Liu JY and Fang L (2002). Steel-making process scheduling using lagrangian relaxation. International Journal of Production Research 40(1): 55–70.

    Article  Google Scholar 

  • Tang LX, Wang GS and Chen ZL (2014). Integrated charge batching and casting width selection at baosteel. Operations Research 62(4): 772–787.

    Article  Google Scholar 

  • Xuan H and Tang LX (2007). Scheduling a hybrid flowshop with batch production at the last stage. Computers & Operations Research 34(9): 2718–2733.

    Article  Google Scholar 

  • Zitzler E, Deb K and Thiele L (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2): 173–195.

    Article  Google Scholar 

  • Zitzler E, Laumanns M and Thiele L (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. In: Proceedings of EUROGEN2001, Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems Conference. Athens, Greece.

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Acknowledgements

This research is partially supported by the High-Tech. R& D Program of China (No. 2007AA04Z161), the National Natural Science Foundation of China (No. 51474044, 50574110, 50174061), the Key Projects of Chongqing Science and Technology Research Projects of China (No. CSTC2011AB3053), and funded by China Scholarship Council.

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Long, J., Zheng, Z., Gao, X. et al. A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants. J Oper Res Soc 67, 1184–1199 (2016). https://doi.org/10.1057/jors.2016.17

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  • DOI: https://doi.org/10.1057/jors.2016.17

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