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

Authors: Farid Najari; Mohammad Mohammadi; Hosein Nadi

Addresses: Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, 15719 – 14911 Tehran, Iran ' Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, 15719 – 14911 Tehran, Iran ' Department of Industrial Engineering, Faculty of Engineering, Amirkabir University of Technology, Tehran, Iran

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.

DOI: 10.1504/IJISE.2016.078001

International Journal of Industrial and Systems Engineering, 2016 Vol.24 No.1, pp.32 - 43

Received: 30 Jul 2014
Accepted: 19 Oct 2014
Published online: 31 Jul 2016 *

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