A genetic algorithm for scheduling jobs and maintenance activities in a permutation flow shop with learning and aging effects

Farid Najari, Mohammad Mohammadi and Hosein Nadi

International Journal of Industrial and Systems Engineering, 2016, vol. 24, issue 1, 32-43

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.

Keywords: permutation flowshops; flowshop scheduling; learning effect; aging effect; maintenance activity; makespan; genetic algorithms; machine maintenance; job sequences. (search for similar items in EconPapers)
Date: 2016
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