Title: An optimal path in a bi-criteria AGV-based flexible jobshop manufacturing system having uncertain parameters

Authors: Hamed Fazlollahtabar; Nezam Mahdavi-Amiri

Addresses: Department of Industrial Engineering, Mazandaran University of Science and Technology, P.O. Box 734, Babol, Iran. ' Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran

Abstract: We propose an approach for finding an optimal path in a flexible jobshop manufacturing system considering two criteria of time and cost. With rise in demands, advancement in technology and increase in production capacity, the need for more shops persists. Therefore, a flexible jobshop system has more than one shop with the same duty. The difference among shops with the same duty is in their machines with various specifications. A network is configured in which the nodes are considered to be the shops with arcs representing the paths among the shops. An automated guided vehicle functions as a material handling device through the manufacturing network. To account for uncertainty, we consider time to be a triangular fuzzy number and apply an expert system to infer cost. The objective is to find a path minimising both the time and cost criteria, aggregately. Since time and cost have different scales, a normalisation procedure is proposed to remove the scales. The model being biobjective, the analytical hierarchy process weighing method is applied to construct a single objective. Finally, a dynamic programming approach is presented for computing a shortest path in the network. The efficiency of the proposed approach is illustrated by a numerical example.

Keywords: flexible job shops; AGVs; automated guided vehicles; flexible manufacturing systems; FMS; fuzzy numbers; fuzzy logic; uncertainty; optimisation; materials handling; expert systems; analytical hierarchy process; AHP; dynamic programming; time; cost.

DOI: 10.1504/IJISE.2013.050544

International Journal of Industrial and Systems Engineering, 2013 Vol.13 No.1, pp.27 - 55

Published online: 27 Dec 2013 *

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