Fuzzy and Evolutionary Algorithms for Transport Logistics Under Uncertainty

Abstract

This paper deals with the analysis of fuzzy and evolutionary approaches for efficiently solving vehicle routing problems (VRP) with constraints on vehicle’s capacity (CVRP) and time-windows (VRPTW). Authors focused their research on CVRP for marine bunkering tankers, in particular, on the planning of tanker’s routes under uncertainty of fuel demands at nodes. Triangular fuzzy numbers (TFNs) are proposed for modeling uncertain demands. In this case, the maximum possible number of customers is calculated, which can be served based on the subtraction operation with TFNs. In the paper, the authors also analyzed the planning of transport routes with time-windows. Currently, there are several methods and algorithms for planning of transport routes with time-windows, in particular: saving and sweeping algorithms, ant colony optimization (ACO) algorithm, artificial bee colony (ABC) algorithm, etc. In this paper, the authors discussed the features of using the ACO algorithm and the ABC algorithm to solve the vehicle routing problems with time-windows and the influence of their application on the results. The modeling results confirm the efficiency of the proposed fuzzy and evolutionary algorithms.