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Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

Author

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  • Jin Huang

    (School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
    School of Mechanical Engineering, Hubei University of Technology, Wuhan 430074, China)

  • Liangliang Jin

    (Department of Mechanical Engineering, Shaoxing University, Shaoxing 312000, China)

  • Chaoyong Zhang

    (School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan 430074, China)

Abstract

Process planning is an important function in a manufacturing system; it specifies the manufacturing requirements and details for the shop floor to convert a part from raw material to the finished form. However, considering only economical criterion with technological constraints is not enough in sustainable manufacturing practice; formerly, criteria about low carbon emission awareness have seldom been taken into account in process planning optimization. In this paper, a mathematical model that considers both machining costs reduction as well as carbon emission reduction is established for the process planning problem. However, due to various flexibilities together with complex precedence constraints between operations, the process planning problem is a non-deterministic polynomial-time (NP) hard problem. Aiming at the distinctive feature of the multi-objectives process planning optimization, we then developed a hybrid non-dominated sorting genetic algorithm (NSGA)-II to tackle this problem. A local search method that considers both the total cost criterion and the carbon emission criterion are introduced into the proposed algorithm to avoid being trapped into local optima. Moreover, the technique for order preference by similarity to an ideal solution (TOPSIS) method is also adopted to determine the best solution from the Pareto front. Experiments have been conducted using Kim’s benchmark. Computational results show that process plan schemes with low carbon emission can be captured, and, more importantly, the proposed hybrid NSGA-II algorithm can obtain more promising optimal Pareto front than the plain NSGA-II algorithm. Meanwhile, according to the computational results of Kim’s benchmark, we find that both of the total machining cost and carbon emission are roughly proportional to the number of operations, and a process plan with less operation may be more satisfactory. This study will draw references for the further research on green manufacturing in the process planning level.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1769-:d:113672
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    References listed on IDEAS

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    1. Seung-Jun Shin & Suk-Hwan Suh & Ian Stroud, 2015. "A green productivity based process planning system for a machining process," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5085-5105, September.
    2. Zhang, Luping & Wong, T.N., 2015. "An object-coding genetic algorithm for integrated process planning and scheduling," European Journal of Operational Research, Elsevier, vol. 244(2), pages 434-444.
    3. Zewen Sun & Xingsheng Gu, 2017. "Hybrid Algorithm Based on an Estimation of Distribution Algorithm and Cuckoo Search for the No Idle Permutation Flow Shop Scheduling Problem with the Total Tardiness Criterion Minimization," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    4. Fang Wang & Yunqing Rao & Chaoyong Zhang & Qiuhua Tang & Liping Zhang, 2016. "Estimation of Distribution Algorithm for Energy-Efficient Scheduling in Turning Processes," Sustainability, MDPI, vol. 8(8), pages 1-20, August.
    5. Liangliang Jin & Qiuhua Tang & Chaoyong Zhang & Xinyu Shao & Guangdong Tian, 2016. "More MILP models for integrated process planning and scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4387-4402, July.
    6. 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.
    7. Jingjing Xu & Lei Wang, 2017. "A Feedback Control Method for Addressing the Production Scheduling Problem by Considering Energy Consumption and Makespan," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    8. Ding, Jian-Ya & Song, Shiji & Wu, Cheng, 2016. "Carbon-efficient scheduling of flow shops by multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 758-771.
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