A bi-criteria AGV-based flexible jobshop manufacturing network having uncertain parameters

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 (AGV) 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 minimizing both the time and cost criteria, aggregately. Since time and cost have different scales, a normalization procedure is proposed to remove the scales. The model being bi-objective, the AHP 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 effectiveness of the proposed approach is illustrated by a numerical example.