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A genetic solution based on lexicographical goal programming for a multiobjective job shop with uncertainty

Autor(es) y otros:
González Rodríguez, InésAutoridad Uniovi; Rodríguez Vela, María del CaminoAutoridad Uniovi; Puente Peinador, JorgeAutoridad Uniovi
Fecha de publicación:
2010
Editorial:

Springer

Versión del editor:
http://dx.doi.org/10.1007/s10845-008-0161-x
Citación:
Journal of Intelligent Manufacturing, 21(1), p. 65-73 (2010); doi:10.1007/s10845-008-0161-x
Descripción física:
p. 65-73
Resumen:

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm

In this work we consider a multiobjective job shop problem with uncertain durations and crisp due dates. Ill-known durations are modelled as fuzzy numbers. We take a fuzzy goal programming approach to propose a generic multiobjective model based on lexicographical minimisation of expected values. To solve the resulting problem, we propose a genetic algorithm searching in the space of possibly active schedules. Experimental results are presented for several problem instances, solved by the GA according to the proposed model, considering three objectives: makespan, tardiness and idleness. The results illustrate the potential of the proposed multiobjective model and genetic algorithm

URI:
http://hdl.handle.net/10651/6167
ISSN:
0956-5515
Identificador local:

20100737

DOI:
10.1007/s10845-008-0161-x
Patrocinado por:

All authors are supported by MEC-FEDER Grant TIN2007- 67466-C02-01

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