A sample average approximation method for disassembly line balancing problem under uncertainty

Abstract : This paper considers a Disassembly Line Balancing Problem (DLBP) under uncertainty. Disassembly task times are assumed to be random variables with known probability distributions. To deal with this uncertainty, a stochastic program is developed. It both chooses the best disassembly alternative for an end of life product and assigns the corresponding disassembly tasks to the workstations of the line with the aim to minimize the line cost. The latter includes the operation costs for workstations as well as penalty costs generated by the cycle time constraint violations. AND/OR precedence constraints among tasks are observed. A proposed solution algorithm is capable of providing high quality solutions even for large scale problem instances. It integrates Monte Carlo sampling techniques with the L-shaped algorithm.
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Article dans une revue
Computers and Operations Research, Elsevier, 2014, Volume 51, pp.Pages 111-122. 〈10.1016/j.cor.2014.05.006〉
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Soumis le : lundi 17 novembre 2014 - 10:16:36
Dernière modification le : dimanche 28 janvier 2018 - 15:22:05

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Mohand Lounes Bentaha, Olga Battaïa, Alexandre Dolgui. A sample average approximation method for disassembly line balancing problem under uncertainty. Computers and Operations Research, Elsevier, 2014, Volume 51, pp.Pages 111-122. 〈10.1016/j.cor.2014.05.006〉. 〈emse-01083333〉

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