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Solving the response time variability problem by means of the cross-entropy method

Author

Listed:
  • Alberto Garcia-Villoria
  • Albert Corominas
  • Rafael Pastor

Abstract

The response time variability problem (RTVP) is an NP-hard combinatorial scheduling problem that has recently appeared in the literature. The RTVP has a wide range of production line systems applications such as sequencing the models to be produced on a mixed-model assembly line in a just-in-time context. This problem occurs whenever several units of different models need to be sequenced so as to minimise the variability of the distance between any two consecutive units of the same model. A mathematical mixed integer linear programming (MILP) model has been presented by another study, but the practical limit for obtaining optimal solutions is around 40 units to be scheduled. Another study has developed five heuristic algorithms to solve non-small RTVP instances. We propose to solve the RTVP by means of the metaheuristic cross-entropy (CE) method, which has been developed recently. We report on the computational experiments in which the CE method is compared with the five heuristic algorithms proposed in the literature.

Suggested Citation

  • Alberto Garcia-Villoria & Albert Corominas & Rafael Pastor, 2010. "Solving the response time variability problem by means of the cross-entropy method," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 20(1/2/3/4), pages 316-330.
    • Handle: RePEc:ids:ijmtma:v:20:y:2010:i:1/2/3/4:p:316-330
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    Citations

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    Cited by:

    1. Albert Corominas & Alberto García-Villoria & Rafael Pastor, 2013. "Metaheuristic algorithms hybridised with variable neighbourhood search for solving the response time variability problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 296-312, July.
    2. Chenxia Jin & Fachao Li & Marzana Wilamowska‐Korsak & Ling Li & Liuliu Fu, 2014. "BSP‐GA: A new Genetic Algorithm for System Optimization and Excellent Schema Selection," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 337-352, May.
    3. S. Acharyya & A. K. Datta, 2020. "Matching formulation of the Staff Transfer Problem: meta-heuristic approaches," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 629-668, September.
    4. García-Villoria, Alberto & Salhi, Said & Corominas, Albert & Pastor, Rafael, 2011. "Hyper-heuristic approaches for the response time variability problem," European Journal of Operational Research, Elsevier, vol. 211(1), pages 160-169, May.

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