IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i10p1769-d113672.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/10/1769/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/10/1769/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rosario Domingo & Beatriz De Agustina & Marta M. Marín, 2018. "A Multi-Response Optimization of Thrust Forces, Torques, and the Power of Tapping Operations by Cooling Air in Reinforced and Unreinforced Polyamide PA66," Sustainability, MDPI, vol. 10(3), pages 1-14, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    2. Zhu, Xuedong & Son, Junbo & Zhang, Xi & Wu, Jianguo, 2023. "Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 117(C).
    3. Wu, Xueqi & Che, Ada, 2019. "A memetic differential evolution algorithm for energy-efficient parallel machine scheduling," Omega, Elsevier, vol. 82(C), pages 155-165.
    4. Barzanji, Ramin & Naderi, Bahman & Begen, Mehmet A., 2020. "Decomposition algorithms for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 93(C).
    5. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    6. Jun Hyeok Kang & Jinil Han, 2019. "Optimizing the Operation of Animal Shelters to Minimize Unnecessary Euthanasia: A Case Study in the Seoul Capital Area," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    7. Yongmao Xiao & Renqing Zhao & Wei Yan & Xiaoyong Zhu, 2022. "Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept," Sustainability, MDPI, vol. 14(13), pages 1-18, June.
    8. Fei Luan & Zongyan Cai & Shuqiang Wu & Shi Qiang Liu & Yixin He, 2019. "Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem with Discrete Whale Optimization Algorithm," Mathematics, MDPI, vol. 7(8), pages 1-17, August.
    9. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    10. Xu Zhang & Hua Zhang & Jin Yao, 2020. "Multi-Objective Optimization of Integrated Process Planning and Scheduling Considering Energy Savings," Energies, MDPI, vol. 13(23), pages 1-31, November.
    11. Wenzhu Liao & Tong Wang, 2019. "A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    12. Hasani, Ali & Hosseini, Seyed Mohammad Hassan, 2020. "A bi-objective flexible flow shop scheduling problem with machine-dependent processing stages: Trade-off between production costs and energy consumption," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    13. Weiwei Wang & Biao Zhang & Baoxian Jia, 2023. "A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots," Sustainability, MDPI, vol. 15(3), pages 1-29, February.
    14. Rujapa Nanthapodej & Cheng-Hsiang Liu & Krisanarach Nitisiri & Sirorat Pattanapairoj, 2021. "Hybrid Differential Evolution Algorithm and Adaptive Large Neighborhood Search to Solve Parallel Machine Scheduling to Minimize Energy Consumption in Consideration of Machine-Load Balance Problems," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    15. Li, Wei & Nault, Barrie R. & Ye, Honghan, 2019. "Trade-off balancing in scheduling for flow shop production and perioperative processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 817-830.
    16. Zhang, Haowei & Xie, Junwei & Ge, Jiaang & Zhang, Zhaojian & Zong, Binfeng, 2019. "A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar," European Journal of Operational Research, Elsevier, vol. 272(3), pages 868-878.
    17. Adrian Kampa & Iwona Paprocka, 2021. "Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times," Energies, MDPI, vol. 14(21), pages 1-25, November.
    18. Alexander Hübner & Fabian Schäfer & Kai N. Schaal, 2020. "Maximizing Profit via Assortment and Shelf‐Space Optimization for Two‐Dimensional Shelves," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 547-570, March.
    19. 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.
    20. Zhou, Shengchao & Jin, Mingzhou & Du, Ni, 2020. "Energy-efficient scheduling of a single batch processing machine with dynamic job arrival times," Energy, Elsevier, vol. 209(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:10:p:1769-:d:113672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.