IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v147y2006i1p175-19810.1007-s10479-006-0065-0.html
   My bibliography  Save this article

Setup coordination between two stages of a production system: A multi-objective evolutionary approach

Author

Listed:
  • Carlo Meloni
  • David Naso
  • Biagio Turchiano

Abstract

This paper describes the application of evolutionary algorithms to a typical multi-objective problem of serial production systems, in which two consecutive departments must organize their internal work, each taking into account the requirements of the other department. In particular, the paper compares three approaches based on different combinations of multi-objective evolutionary algorithms and local-search heuristics, using both small-size test instances and larger problems derived from an industrial production process. The analysis of the case-studies confirms the effectiveness of the evolutionary approaches, also enlightening the advantages and shortcomings of each considered algorithm. Copyright Springer Science + Business Media, LCC 2006

Suggested Citation

  • Carlo Meloni & David Naso & Biagio Turchiano, 2006. "Setup coordination between two stages of a production system: A multi-objective evolutionary approach," Annals of Operations Research, Springer, vol. 147(1), pages 175-198, October.
    • Handle: RePEc:spr:annopr:v:147:y:2006:i:1:p:175-198:10.1007/s10479-006-0065-0
    DOI: 10.1007/s10479-006-0065-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10479-006-0065-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10479-006-0065-0?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. A. Agnetis & P. Detti & C. Meloni & D. Pacciarelli, 2001. "Set-Up Coordination between Two Stages of a Supply Chain," Annals of Operations Research, Springer, vol. 107(1), pages 15-32, October.
    2. Hertz, Alain & Kobler, Daniel, 2000. "A framework for the description of evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 126(1), pages 1-12, October.
    3. Al-Haboubi, Mohamad H. & Selim, Shokri Z., 1993. "A sequencing problem in the weaving industry," European Journal of Operational Research, Elsevier, vol. 66(1), pages 65-71, April.
    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. Liao, Ching-Jong & Shyu, Cian-Ci & Tseng, Chao-Tang, 2009. "A least flexibility first heuristic to coordinate setups in a two- or three-stage supply chain," International Journal of Production Economics, Elsevier, vol. 117(1), pages 127-135, January.
    2. Dellino, G. & Laudadio, T. & Mari, R. & Mastronardi, N. & Meloni, C., 2018. "Microforecasting methods for fresh food supply chain management: A computational study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 147(C), pages 100-120.

    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. Ciavotta, Michele & Detti, Paolo & Meloni, Carlo & Pranzo, Marco, 2008. "A bi-objective coordination setup problem in a two-stage production system," European Journal of Operational Research, Elsevier, vol. 189(3), pages 734-745, September.
    2. Liao, Ching-Jong & Shyu, Cian-Ci & Tseng, Chao-Tang, 2009. "A least flexibility first heuristic to coordinate setups in a two- or three-stage supply chain," International Journal of Production Economics, Elsevier, vol. 117(1), pages 127-135, January.
    3. Jiafu Tang & Kai-Leung Yung & Iko Kaku & Jianbo Yang, 2008. "The scheduling of deliveries in a production-distribution system with multiple buyers," Annals of Operations Research, Springer, vol. 161(1), pages 5-23, July.
    4. Jun Pei & Xinbao Liu & Panos M. Pardalos & Wenjuan Fan & Shanlin Yang, 2017. "Scheduling deteriorating jobs on a single serial-batching machine with multiple job types and sequence-dependent setup times," Annals of Operations Research, Springer, vol. 249(1), pages 175-195, February.
    5. Liao, C. J. & Yu, W. C., 1996. "Sequencing heuristics for dependent setups in a continuous process industry," Omega, Elsevier, vol. 24(6), pages 649-659, December.
    6. Mansouri, S. Afshin & Aktas, Emel & Besikci, Umut, 2016. "Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption," European Journal of Operational Research, Elsevier, vol. 248(3), pages 772-788.
    7. Boysen, Nils & Scholl, Armin & Wopperer, Nico, 2012. "Resequencing of mixed-model assembly lines: Survey and research agenda," European Journal of Operational Research, Elsevier, vol. 216(3), pages 594-604.
    8. Gaia Nicosia & Andrea Pacifici & Ulrich Pferschy & Julia Resch & Giovanni Righini, 2021. "Optimally rescheduling jobs with a Last-In-First-Out buffer," Journal of Scheduling, Springer, vol. 24(6), pages 663-680, December.
    9. Sanjoy Kumar Paul & Ruhul Sarker & Daryl Essam, 2018. "A reactive mitigation approach for managing supply disruption in a three-tier supply chain," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1581-1597, October.
    10. Bo Liu & Ling Wang & Ying Liu & Shouyang Wang, 2011. "A unified framework for population-based metaheuristics," Annals of Operations Research, Springer, vol. 186(1), pages 231-262, June.
    11. Afshin Mansouri, S. & Gallear, David & Askariazad, Mohammad H., 2012. "Decision support for build-to-order supply chain management through multiobjective optimization," International Journal of Production Economics, Elsevier, vol. 135(1), pages 24-36.
    12. Huyet, A.L., 2006. "Optimization and analysis aid via data-mining for simulated production systems," European Journal of Operational Research, Elsevier, vol. 173(3), pages 827-838, September.
    13. Ana Iannoni & Reinaldo Morabito & Cem Saydam, 2008. "A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways," Annals of Operations Research, Springer, vol. 157(1), pages 207-224, January.
    14. Lee, Cheng-Hsiung & Liao, Ching-Jong & Chung, Tsui-Ping, 2014. "Scheduling with multi-attribute setup times on two identical parallel machines," International Journal of Production Economics, Elsevier, vol. 153(C), pages 130-138.
    15. Dmitry Ivanov & Maxim Rozhkov, 2020. "Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company," Annals of Operations Research, Springer, vol. 291(1), pages 387-407, August.
    16. Iannoni, Ana Paula & Morabito, Reinaldo & Saydam, Cem, 2009. "An optimization approach for ambulance location and the districting of the response segments on highways," European Journal of Operational Research, Elsevier, vol. 195(2), pages 528-542, June.
    17. Dario Landa-Silva & Edmund K. Burke, 2007. "Asynchronous Cooperative Local Search for the Office-Space-Allocation Problem," INFORMS Journal on Computing, INFORMS, vol. 19(4), pages 575-587, November.
    18. Nsakanda, Aaron Luntala & Price, Wilson L. & Diaby, Moustapha & Gravel, Marc, 2007. "Ensuring population diversity in genetic algorithms: A technical note with application to the cell formation problem," European Journal of Operational Research, Elsevier, vol. 178(2), pages 634-638, April.
    19. Fanrong Kong & Jianhui Jiang & Zhigang Ding & Junjie Hu & Weian Guo & Lei Wang, 2017. "A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm," Energies, MDPI, vol. 10(9), pages 1-18, September.
    20. Paolo Detti & Carlo Meloni & Marco Pranzo, 2007. "Local search algorithms for finding the Hamiltonian completion number of line graphs," Annals of Operations Research, Springer, vol. 156(1), pages 5-24, December.

    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:spr:annopr:v:147:y:2006:i:1:p:175-198:10.1007/s10479-006-0065-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.