IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v269y2018i2p647-660.html
   My bibliography  Save this article

An asynchronous parallel disassembly planning based on genetic algorithm

Author

Listed:
  • Ren, Yaping
  • Zhang, Chaoyong
  • Zhao, Fu
  • Xiao, Huajun
  • Tian, Guangdong

Abstract

Disassembly is one of the most crucial remanufacturing activities. Disassembly sequence planning (DSP) is a combinatorial optimization problem and has been studied by many researchers. Conventional DSP techniques focus on sequential disassembly planning (SDP) in which only one manipulator is used to remove a single part or subassembly at a time such that it is inefficient when disassembling large or complex products. Recently, parallel disassembly has attracted some interest as it employs several manipulators to remove multiple components simultaneously. However, most of the work to date focuses on parallel disassembly techniques which require synchronization between manipulators, i.e., they must start their tasks simultaneously. This simplifies the modeling and analysis efforts but fails to fully realize the benefits of parallel disassembly. In this work, we propose asynchronous parallel disassembly planning (aPDP) which eliminates the synchronization requirement. In addition to precedence constraints, aPDP becomes highly operation time-dependent. To deal with this, we design an efficient encoding and decoding strategy for the disassembly process. In this paper, a metaheuristic approach, based on a genetic algorithm, is developed to solve the aPDP problem. The proposed algorithm is applied to four products which require disassembly processes of varying complexity, and the results are compared with two methods reported in literature. It is suggested that the proposed approach can identify faster disassembly processes, especially when solving large-scale problems.

Suggested Citation

  • Ren, Yaping & Zhang, Chaoyong & Zhao, Fu & Xiao, Huajun & Tian, Guangdong, 2018. "An asynchronous parallel disassembly planning based on genetic algorithm," European Journal of Operational Research, Elsevier, vol. 269(2), pages 647-660.
    • Handle: RePEc:eee:ejores:v:269:y:2018:i:2:p:647-660
    DOI: 10.1016/j.ejor.2018.01.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718300912
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.01.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Panwalkar, S.S., 2016. "The proportionate two-machine no-wait job shop scheduling problemAuthor-Name: Koulamas, Christos," European Journal of Operational Research, Elsevier, vol. 252(1), pages 131-135.
    2. Yaping Ren & Daoyuan Yu & Chaoyong Zhang & Guangdong Tian & Leilei Meng & Xiaoqiang Zhou, 2017. "An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7302-7316, December.
    3. Hyung-Won Kim & Dong-Ho Lee, 2017. "An optimal algorithm for selective disassembly sequencing with sequence-dependent set-ups in parallel disassembly environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7317-7333, December.
    4. Goncalves, Jose Fernando & de Magalhaes Mendes, Jorge Jose & Resende, Mauricio G. C., 2005. "A hybrid genetic algorithm for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 167(1), pages 77-95, November.
    5. Moore, Kendra E. & Gungor, Askiner & Gupta, Surendra M., 2001. "Petri net approach to disassembly process planning for products with complex AND/OR precedence relationships," European Journal of Operational Research, Elsevier, vol. 135(2), pages 428-449, December.
    6. Ondemir, Onder & Gupta, Surendra M., 2014. "A multi-criteria decision making model for advanced repair-to-order and disassembly-to-order system," European Journal of Operational Research, Elsevier, vol. 233(2), pages 408-419.
    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. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    2. Yaping Ren & Xinyu Lu & Hongfei Guo & Zhaokang Xie & Haoyang Zhang & Chaoyong Zhang, 2023. "A Review of Combinatorial Optimization Problems in Reverse Logistics and Remanufacturing for End-of-Life Products," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    3. Chang Wook Kang & Muhammad Imran & Muhammad Omair & Waqas Ahmed & Misbah Ullah & Biswajit Sarkar, 2019. "Stochastic-Petri Net Modeling and Optimization for Outdoor Patients in Building Sustainable Healthcare System Considering Staff Absenteeism," Mathematics, MDPI, vol. 7(6), pages 1-26, June.
    4. Lei, Jieyu & Che, Ada & Van Woensel, Tom, 2024. "Collection-disassembly-delivery problem of disassembly centers in a reverse logistics network," European Journal of Operational Research, Elsevier, vol. 313(2), pages 478-493.
    5. Leilei Meng & Biao Zhang & Yaping Ren & Hongyan Sang & Kaizhou Gao & Chaoyong Zhang, 2022. "Mathematical Formulations for Asynchronous Parallel Disassembly Planning of End-of-Life Products," Mathematics, MDPI, vol. 10(20), pages 1-16, October.

    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. Mehmet Talha Dulman & Surendra M. Gupta, 2018. "Evaluation of Maintenance and EOL Operation Performance of Sensor-Embedded Laptops," Logistics, MDPI, vol. 2(1), pages 1-22, January.
    2. Sels, Veronique & Craeymeersch, Kjeld & Vanhoucke, Mario, 2011. "A hybrid single and dual population search procedure for the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 215(3), pages 512-523, December.
    3. Özden Tozanlı & Elif Kongar & Surendra M. Gupta, 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain," Sustainability, MDPI, vol. 12(13), pages 1-33, July.
    4. Rego, César & Duarte, Renato, 2009. "A filter-and-fan approach to the job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 194(3), pages 650-662, May.
    5. Gonçalves, J.F. & Mendes, J.J.M. & Resende, M.G.C., 2008. "A genetic algorithm for the resource constrained multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1171-1190, September.
    6. Gonçalves, José Fernando & Resende, Mauricio G.C., 2015. "A biased random-key genetic algorithm for the unequal area facility layout problem," European Journal of Operational Research, Elsevier, vol. 246(1), pages 86-107.
    7. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    8. Muris Lage Junior & Moacir Godinho Filho, 2017. "Master disassembly scheduling in a remanufacturing system with stochastic routings," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 123-138, March.
    9. José Fernando Gonçalves & Mauricio G. C. Resende, 2011. "A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem," Journal of Combinatorial Optimization, Springer, vol. 22(2), pages 180-201, August.
    10. Liang, Wei & Zhang, Zeqiang & Yin, Tao & Zhang, Yu & Wu, Tengfei, 2023. "Modelling and optimisation of energy consumption and profit-oriented multi-parallel partial disassembly line balancing problem," International Journal of Production Economics, Elsevier, vol. 262(C).
    11. Thi-Kien Dao & Tien-Szu Pan & Trong-The Nguyen & Jeng-Shyang Pan, 2018. "Parallel bat algorithm for optimizing makespan in job shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 451-462, February.
    12. Gabriel H Greve & Kenneth M Hopkinson & Gary B Lamont, 2018. "Evolutionary sensor allocation for the Space Surveillance Network," The Journal of Defense Modeling and Simulation, , vol. 15(3), pages 303-322, July.
    13. Jianhua Cao & Xuhui Xia & Lei Wang & Zelin Zhang & Xiang Liu, 2019. "A Novel Multi-Efficiency Optimization Method for Disassembly Line Balancing Problem," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    14. 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).
    15. Mehmet Hakan Satman & Emre Akadal, 2020. "Machine Coded Compact Genetic Algorithms for Real Parameter Optimization Problems," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(1), pages 43-58, June.
    16. Liu, Kanglin & Zhang, Zhi-Hai, 2018. "Capacitated disassembly scheduling under stochastic yield and demand," European Journal of Operational Research, Elsevier, vol. 269(1), pages 244-257.
    17. Gonçalves, José Fernando & Wäscher, Gerhard, 2020. "A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects," European Journal of Operational Research, Elsevier, vol. 286(3), pages 867-882.
    18. Julliany S. Brandão & Thiago F. Noronha & Celso C. Ribeiro, 2016. "A biased random-key genetic algorithm to maximize the number of accepted lightpaths in WDM optical networks," Journal of Global Optimization, Springer, vol. 65(4), pages 813-835, August.
    19. Jorge M. S. Valente & Maria R. A. Moreira & Alok Singh & Rui A. F. S. Alves, 2009. "Genetic algorithms for single machine scheduling with quadratic earliness and tardiness costs," FEP Working Papers 312, Universidade do Porto, Faculdade de Economia do Porto.
    20. Pisut Pongchairerks, 2019. "A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem," Complexity, Hindawi, vol. 2019, pages 1-11, 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:eee:ejores:v:269:y:2018:i:2:p:647-660. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.