Title: Optimisation of makespan of a flow shop problem using multi layer neural network

Authors: Harendra Kumar; Shailendra Giri

Addresses: Department of Mathematics and Statistics, Gurukula Kangri University, Haridwar 249404, Uttarakhand, India ' Department of Mathematics and Statistics, Gurukula Kangri University, Haridwar 249404, Uttarakhand, India

Abstract: This paper presents an approach based on a multi layer neural network algorithm (MLNNA) to find a sequence of jobs for flow shop scheduling problems with the objective of minimise the makespan. The purpose of this paper is to develop an artificial intelligence and trained a neural network model for solving the flow shop scheduling problem which gives a best jobs sequence with the objective of minimise the makespan. The effectiveness of the proposed MLNNA method is compared with many problems selected from different papers. A large number of problems are solved with the present MLNNA model and it is found suitable and workable in all the cases.

Keywords: artificial neural network; flow shop problem; scheduling; multi layer network; makespan; job sequencing.

DOI: 10.1504/IJCSM.2020.106389

International Journal of Computing Science and Mathematics, 2020 Vol.11 No.2, pp.107 - 122

Received: 24 Aug 2017
Accepted: 28 Sep 2017

Published online: 31 Mar 2020 *

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