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

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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