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A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty

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  • Lusa, Amaia
  • Corominas, Albert
  • Muñoz, Norberto

Abstract

The annualisation of working hours (i.e., the irregular distribution of the total number of working hours over the course of a year) makes it possible to adapt production capacity to fluctuations in demand. The required capacity, which is an essential data for the optimal planning of working time, usually depends on several complex factors. Often, it is impossible to reliably predict the required capacity or it is unrealistic to adjust it to a probability distribution. In some cases, it is possible to determine a set of required-capacity scenarios, each with a related probability. This paper presents a multistage stochastic optimisation model that provides a robust solution (i.e., feasible for any possible scenario) and minimises the expected total capacity shortage.

Suggested Citation

  • Lusa, Amaia & Corominas, Albert & Muñoz, Norberto, 2008. "A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 113(2), pages 957-968, June.
    • Handle: RePEc:eee:proeco:v:113:y:2008:i:2:p:957-968
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    References listed on IDEAS

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    1. Beraldi, Patrizia & Ghiani, Gianpaolo & Guerriero, Emanuela & Grieco, Antonio, 2006. "Scenario-based planning for lot-sizing and scheduling with uncertain processing times," International Journal of Production Economics, Elsevier, vol. 101(1), pages 140-149, May.
    2. Azmat, Carlos S. & Widmer, Marino, 2004. "A case study of single shift planning and scheduling under annualized hours: A simple three-step approach," European Journal of Operational Research, Elsevier, vol. 153(1), pages 148-175, February.
    3. L. Escudero & P. Kamesam, 1995. "On solving stochastic production planning problems via scenario modelling," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 3(1), pages 69-95, June.
    4. Albert Corominas & Amaia Lusa & Rafael Pastor, 2004. "Planning Annualised Hours with a Finite Set of Weekly Working Hours and Joint Holidays," Annals of Operations Research, Springer, vol. 128(1), pages 217-233, April.
    5. Carlos Azmat & Tony Hürlimann & Marino Widmer, 2004. "Mixed Integer Programming to Schedule a Single-Shift Workforce under Annualized Hours," Annals of Operations Research, Springer, vol. 128(1), pages 199-215, April.
    6. Mula, J. & Poler, R. & Garcia-Sabater, J.P. & Lario, F.C., 2006. "Models for production planning under uncertainty: A review," International Journal of Production Economics, Elsevier, vol. 103(1), pages 271-285, September.
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    Cited by:

    1. Fernandes, Rui & Gouveia, Borges & Pinho, Carlos, 2010. "Modeling Overstock," MPRA Paper 25126, University Library of Munich, Germany.
    2. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    3. Liu, Dehai & Li, Hongyi & Wang, Weiguo & Dong, Yucheng, 2012. "Constructivism scenario evolutionary analysis of zero emission regional planning: A case of Qaidam Circular Economy Pilot Area in China," International Journal of Production Economics, Elsevier, vol. 140(1), pages 341-356.

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