Game Theory Based Solver for Dynamic Vehicle Routing Problem

Abstract

In recent decades, research concern in vehicle routing has progressively more concentrated on dynamic and stochastic approaches in solving sophisticated dynamic vehicle routing problems (DVRP) due to its importance in helping potential customers to better manage their applications and to provide e-freight and e-commerce systems similarly. Many techniques were introduced in the DVRP field to solve part of its challenges since it’s hard to achieve equilibrium between finding feasible set of tours, minimum total travel time, shortest routing path and the capability of redirect a stirring vehicle to a new demand for additional savings; due to the fact that each one can be achieved at the expense of the other and combining them does not give an ideal result which requires a solution to track the optimum route during changes. In this paper, the game theory (GT) is integrated with Ant Colony Optimization algorithms (ACO) to adjust the attractiveness of arc and the pheromone level as it considers them competing players that prefer one according to the expected payoff. In addition, GT can be adapted inside the algorithm to facilitate the optimization, as it is considered a powerful technique in decision making and to find optimal solutions to conditions of conflict and cooperation. Experimental results show that the integration of GT with ACO algorithm improves the system performance in tackling DVRPs.