<b>An Ant Colony Optimization Method for Fuzzy Vehicle Routing Problem </b>

Abstract

This paper deals with the vehicle routing problem involved with fuzzy/imprecise vehicle travel times and customer service times, these fuzzy/imprecise times are represented as fuzzy numbers and interpreted as possibility distributions. According to the same consideration as the stochastic programming with recourse, we treate the influence of the fuzziness of travel times and service times as recourse cost. and solve the fuzzy vehicle routing problem through twostage decisions. As the result, a two-stage possibilistic programming model is formulated. By choosing an appropriate definition of fuzzy mean, we can show that the proposed model is equivalent to an ordinary crisp programming problem. Furthermore, We propose a solution method based on Ant Colony System (ACS) to obttain the best solution of the problem. Finally, some examples are given to illustrate the two-stage model and the solution algorithm.

Finding Optimal Routes for Main Haul Roads Development in Mountainous Open Pit using A* algorithm

“…While Qu et al [11] have clearly showed that neither the algorithm nor the common method can provide drivers with a satisfactory solution for wayfinding in a complex road network of the city on the basis of analysing the advantages and disadvantages of the shortest path algorithm and the problem solving based on knowledge method. Besides, Dong et al [12] used an ant colony optimization method for fuzzy vehicle routing problem, and Abraham et al [13] found the alternative routes in road networks with independent cost function. Also, Arunadevi et al [14] selected route to a given destination on an actual map under a static environment, with a parallel genetic algorithm (PGA) implemented using high performance cluster.…”