IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1299-d1091097.html
   My bibliography  Save this article

Multi-Objective Optimization for Mixed-Model Two-Sided Disassembly Line Balancing Problem Considering Partial Destructive Mode

Author

Listed:
  • Bao Chao

    (The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Peng Liang

    (The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Chaoyong Zhang

    (The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hongfei Guo

    (School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China)

Abstract

Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products.

Suggested Citation

  • Bao Chao & Peng Liang & Chaoyong Zhang & Hongfei Guo, 2023. "Multi-Objective Optimization for Mixed-Model Two-Sided Disassembly Line Balancing Problem Considering Partial Destructive Mode," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1299-:d:1091097
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1299/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/6/1299/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rehman, Shafique Ur & Kraus, Sascha & Shah, Syed Asim & Khanin, Dmitry & Mahto, Raj V., 2021. "Analyzing the relationship between green innovation and environmental performance in large manufacturing firms," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    2. Yılmaz Delice & Emel Kızılkaya Aydoğan & Uğur Özcan & Mehmet Sıtkı İlkay, 2017. "A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 23-36, January.
    3. Mohand Lounes Bentaha & Olga Battaïa & Alexandre Dolgui, 2015. "An exact solution approach for disassembly line balancing problem under uncertainty of the task processing times," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1807-1818, March.
    Full references (including those not matched with items on IDEAS)

    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. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    2. Kusa, Rafał & Suder, Marcin & Duda, Joanna, 2023. "Impact of greening on performance in the hospitality industry: Moderating effect of flexibility and inter-organizational cooperation," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    3. Yousaf, Umair Bin & Ullah, Irfan & Jiang, Junchen & Wang, Man, 2022. "The role of board capital in driving green innovation: Evidence from China," Journal of Behavioral and Experimental Finance, Elsevier, vol. 35(C).
    4. Ana Labella-Fernández & M. Mar Serrano-Arcos & Belén Payán-Sánchez, 2021. "Firm Growth as a Driver of Sustainable Product Innovation: Mediation and Moderation Analysis. Evidence from Manufacturing Firms," IJERPH, MDPI, vol. 18(5), pages 1-22, March.
    5. Joseph I. Uduji & Elda N. Okolo-Obasi, 2022. "Gender Sensitive Responses to Climate Change in Nigeria: The Role of Multinationals’ Corporate Social Responsibility in Oil Host Communities," Working Papers 22/041, European Xtramile Centre of African Studies (EXCAS).
    6. Biao Hu & Kai Yuan & Tingyun Niu & Liang Zhang & Yuqiong Guan, 2022. "Study on the Spatial and Temporal Evolution Patterns of Green Innovation Efficiency and Driving Factors in Three Major Urban Agglomerations in China—Based on the Perspective of Economic Geography," Sustainability, MDPI, vol. 14(15), pages 1-28, July.
    7. Mohammad Ehteram & Hojat Karami & Sayed Farhad Mousavi & Saaed Farzin & Alcigeimes B. Celeste & Ahmad-El Shafie, 2018. "Reservoir Operation by a New Evolutionary Algorithm: Kidney Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4681-4706, November.
    8. Diefenbach, Johannes & Stolletz, Raik, 2022. "Stochastic assembly line balancing: General bounds and reliability-based branch-and-bound algorithm," European Journal of Operational Research, Elsevier, vol. 302(2), pages 589-605.
    9. Junkai He & Feng Chu & Feifeng Zheng & Ming Liu, 2021. "A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times," Annals of Operations Research, Springer, vol. 296(1), pages 71-93, January.
    10. Joseph I. Uduji & Elda N. Okolo-Obasi, 2022. "Gender Sensitive Responses to Climate Change in Nigeria: The Role of Multinationals’ Corporate Social Responsibility in Oil Host Communities," Working Papers of The Association for Promoting Women in Research and Development in Africa (ASPROWORDA). 22/008, The Association for Promoting Women in Research and Development in Africa (ASPROWORDA).
    11. Alisher Khamdamov & Zhiwei Tang & Muhammad Ali Hussain, 2023. "Unpacking Parallel Mediation Processes between Green HRM Practices and Sustainable Environmental Performance: Evidence from Uzbekistan," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    12. Hermundsdottir, Fanny & Haneberg, Dag Håkon & Aspelund, Arild, 2022. "Analyzing the impact of COVID-19 on environmental innovations in manufacturing firms," Technology in Society, Elsevier, vol. 68(C).
    13. Xuhui Xia & Wei Liu & Zelin Zhang & Lei Wang & Jianhua Cao & Xiang Liu, 2019. "A Balancing Method of Mixed-model Disassembly Line in Random Working Environment," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    14. Xinwei Li & Wenjuan Zeng & Mao Xu, 2022. "The Moderating Role of IT Capability on Green Innovation and Ambidexterity: Towards a Corporate Sustainable Development," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    15. Muhammad Ibrahim & Rosli Mahmood, 2022. "Proactive Environmental Strategy and Environmental Performance of the Manufacturing SMEs of Karachi City in Pakistan: Role of Green Mindfulness as a DCV," Sustainability, MDPI, vol. 14(19), pages 1-14, September.
    16. Rok Črešnar & Marina Dabić & Nebojša Stojčić & Zlatko Nedelko, 2023. "It takes two to tango: technological and non-technological factors of Industry 4.0 implementation in manufacturing firms," Review of Managerial Science, Springer, vol. 17(3), pages 827-853, April.
    17. Li, Zixiang & Kucukkoc, Ibrahim & Zhang, Zikai, 2020. "Branch, bound and remember algorithm for two-sided assembly line balancing problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 896-905.
    18. Chen, Fu & Wang, Liyun & Gu, Qiaojing & Wang, Mingyue & Ding, Xuanwen, 2022. "Nexus between natural resources, financial development, green innovation and environmental sustainability in China: Fresh insight from novel quantile ARDL," Resources Policy, Elsevier, vol. 79(C).
    19. Bentaha, Mohand-Lounes & Voisin, Alexandre & Marangé, Pascale, 2020. "A decision tool for disassembly process planning under end-of-life product quality," International Journal of Production Economics, Elsevier, vol. 219(C), pages 386-401.
    20. Jin Zhu & Fei Huang, 2023. "Transformational Leadership, Organizational Innovation, and ESG Performance: Evidence from SMEs in China," Sustainability, MDPI, vol. 15(7), pages 1-23, March.

    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:jmathe:v:11:y:2023:i:6:p:1299-:d:1091097. 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.