A genetic algorithm for scheduling jobs and maintenance activities in a permutation flow shop with learning and aging effects
by Farid Najari; Mohammad Mohammadi; Hosein Nadi
International Journal of Industrial and Systems Engineering (IJISE), Vol. 24, No. 1, 2016

Abstract: In manufacturing environment, machine maintenance is implemented to prevent untimely machine fails and preserve production efficiency. This paper deals with a permutation flow shop scheduling problem with learning and aging effects and maintenance activity simultaneously. It is assumed that each of the machines may be subject to at most one maintenance activity over the scheduling horizon. The objective is defined as obtaining, concurrently, the optimal or near optimal job sequences, maintenance iterations and positions of the maintenance activities such that makespan is minimised. The problem is non-deterministic polynomial-time hard (NP-hard), thus, an integer linear programming formulation and a genetic algorithm are proposed to solve the problem efficiently in small and large sizes respectively.

Online publication date: Sun, 31-Jul-2016

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