IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v55y2017i10p2806-2828.html
   My bibliography  Save this article

Assembly sequence planning for processes with heterogeneous reliabilities

Author

Listed:
  • Shraga Shoval
  • Mahmoud Efatmaneshnik
  • Michael J. Ryan

Abstract

Stochasticity in assembly processes is often associated with the processing time and availability of machinery, tools and manpower, however in this paper it is determined by probability of an assembly task successful completion which here is referred to as task reliability. We present a mathematical model for optimising the expected assembly cost, and consider two scenarios: the first a situation where a failure of one assembly task requires rework of that task alone; second a situation in which a failure in the midst of the process requires resumption of previously completed tasks. In the worst case scenario the assembly process must restart from the beginning. We show that the first scenario is insensitive to sequencing unless there are set-up costs. In the second scenario the process is sensitive to tasks’ sequence. We present a heuristic that argues for accomplishing more uncertain tasks (with less reliability) earlier in the process to decrease the expected cost of assembly, and show that in a mutually dependent assembly process, when tasks’ reliabilities are similar, the cheaper tasks should be executed earlier in the process.

Suggested Citation

  • Shraga Shoval & Mahmoud Efatmaneshnik & Michael J. Ryan, 2017. "Assembly sequence planning for processes with heterogeneous reliabilities," International Journal of Production Research, Taylor & Francis Journals, vol. 55(10), pages 2806-2828, May.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:10:p:2806-2828
    DOI: 10.1080/00207543.2016.1213449
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2016.1213449
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2016.1213449?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. Hulett, Maria & Damodaran, Purushothaman, 2011. "Analytical approximations to predict performance measures of markovian type manufacturing systems with job failures and parallel processing," European Journal of Operational Research, Elsevier, vol. 212(1), pages 89-99, July.
    2. Pradhan, Salil & Damodaran, Purushothaman, 2009. "Performance characterization of complex manufacturing systems with general distributions and job failures," European Journal of Operational Research, Elsevier, vol. 197(2), pages 588-598, September.
    3. Kara, Yakup & Paksoy, Turan & Chang, Ching-Ter, 2009. "Binary fuzzy goal programming approach to single model straight and U-shaped assembly line balancing," European Journal of Operational Research, Elsevier, vol. 195(2), pages 335-347, June.
    4. Q. Su, 2007. "Applying case-based reasoning in assembly sequence planning," International Journal of Production Research, Taylor & Francis Journals, vol. 45(1), pages 29-47, January.
    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. Damodaran, Purushothaman & Hulett, Maria, 2012. "Analytical approximations to predict performance measures of manufacturing systems with general distributions, job failures and parallel processing," European Journal of Operational Research, Elsevier, vol. 221(1), pages 74-86.
    2. Hulett, Maria & Damodaran, Purushothaman, 2011. "Analytical approximations to predict performance measures of markovian type manufacturing systems with job failures and parallel processing," European Journal of Operational Research, Elsevier, vol. 212(1), pages 89-99, July.
    3. Wu, Kan & McGinnis, Leon, 2012. "Performance evaluation for general queueing networks in manufacturing systems: Characterizing the trade-off between queue time and utilization," European Journal of Operational Research, Elsevier, vol. 221(2), pages 328-339.
    4. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Jula, Payman & Pirayesh, Amir & Ahmadi, Hadi, 2020. "A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty," European Journal of Operational Research, Elsevier, vol. 285(2), pages 513-537.
    5. Rifat G. Ozdemir & Ugur Cinar & Eren Kalem & Onur Ozcelik, 2016. "Sub-assembly detection and line balancing using fuzzy goal programming approach," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 65-86.
    6. M. H. Alavidoost & M. H. Fazel Zarandi & Mosahar Tarimoradi & Yaser Nemati, 2017. "Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 313-336, February.
    7. Klindworth, Hanne & Otto, Christian & Scholl, Armin, 2012. "On a learning precedence graph concept for the automotive industry," European Journal of Operational Research, Elsevier, vol. 217(2), pages 259-269.
    8. Borodin, Valeria & Dolgui, Alexandre & Hnaien, Faicel & Labadie, Nacima, 2016. "Component replenishment planning for a single-level assembly system under random lead times: A chance constrained programming approach," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 79-86.
    9. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    10. Liu, Jialu & Yang, Sheng & Wu, Aiguo & Hu, S. Jack, 2012. "Multi-state throughput analysis of a two-stage manufacturing system with parallel unreliable machines and a finite buffer," European Journal of Operational Research, Elsevier, vol. 219(2), pages 296-304.
    11. Abolfazl Jafari Asl & Maghsud Solimanpur & Ravi Shankar, 2019. "Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 603-627, September.
    12. Masood Fathi & María Jesús à lvarez & Victoria Rodríguez, 2016. "A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(2), pages 145-169.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:55:y:2017:i:10:p:2806-2828. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.