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Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

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  • Lvjiang Yin

    (The State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    School of Economics and Management, Hubei University of Automotive Technology, Shiyan 442002, China)

  • Xinyu Li

    (The State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chao Lu

    (The State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Liang Gao

    (The State Key Lab of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

Suggested Citation

  • Lvjiang Yin & Xinyu Li & Chao Lu & Liang Gao, 2016. "Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm," Sustainability, MDPI, vol. 8(12), pages 1-33, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1268-:d:84649
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    References listed on IDEAS

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    1. Gahm, Christian & Denz, Florian & Dirr, Martin & Tuma, Axel, 2016. "Energy-efficient scheduling in manufacturing companies: A review and research framework," European Journal of Operational Research, Elsevier, vol. 248(3), pages 744-757.
    2. Dvir Shabtay & George Steiner, 2008. "The single-machine earliness-tardiness scheduling problem with due date assignment and resource-dependent processing times," Annals of Operations Research, Springer, vol. 159(1), pages 25-40, March.
    3. J M S Valente, 2010. "Beam search heuristics for quadratic earliness and tardiness scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 620-631, April.
    4. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
    5. Yano, Candace Arai & Kim, Yeong-Dae, 1991. "Algorithms for a class of single-machine weighted tardiness and earliness problems," European Journal of Operational Research, Elsevier, vol. 52(2), pages 167-178, May.
    6. Kan Fang & Nelson Uhan & Fu Zhao & John Sutherland, 2013. "Flow shop scheduling with peak power consumption constraints," Annals of Operations Research, Springer, vol. 206(1), pages 115-145, July.
    7. Baker, Kenneth R., 2014. "Minimizing earliness and tardiness costs in stochastic scheduling," European Journal of Operational Research, Elsevier, vol. 236(2), pages 445-452.
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    7. Jin Huang & Liangliang Jin & Chaoyong Zhang, 2017. "Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    8. Radosław Winiczenko & Krzysztof Górnicki & Agnieszka Kaleta & Monika Janaszek-Mańkowska & Aneta Choińska & Jędrzej Trajer, 2018. "Apple Cubes Drying and Rehydration. Multiobjective Optimization of the Processes," Sustainability, MDPI, vol. 10(11), pages 1-12, November.
    9. 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.

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