The multi-objective constrained shortest path problem is one of the most significant and well-known problems in the field of network optimization which due to its many applications in routing, telecommunication, transportation, scheduling, etc., has attracted the attention of many researchers. In this paper, the mathematical model of the constrained shortest path problem with three objectives of cost, time and risk is formulated, where the constraint is on the path length. The aim is to find the most desirable path to move commodities from origin to destination based on three factors of cost, time, and risk which the length of path does not exceed a predetermined value. The approach proposed for solving the problem under investigation is to use fuzzy inference system which finds optimal solution in comparison to linear programming and genetic algorithm approaches in less time. The proposed algorithm is implemented on a network of 27 nodes and 52 arcs. The implementation results of the proposed algorithm show that it is capable of finding the optimal solution.
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