Developing a multi-objective model for the routing problem of vehicles carrying valuable commodity under route risk conditions (case study of Shahr Bank)

In this paper, a new type of Vehicle Routing Problem (VRP) in the valuable commodity transportation industry is modeled considering the route risk constraint. The proposed model has two objective functions for risk minimization. In the first objective function, three concepts are presented, which are 1) the vehicle does not travel long distances in the first three moves because it carries more money, 2) to serve the same branch on two consecutive days, at the same time, and 3) the bow should not be repeated in two consecutive days. This reduces the possibility of determining a fixed pattern for the service and increases its security. In the second objective function, the risk is a function of the amount of money, the probability of theft, and the probability of its success. Two different meta-heuristic algorithms have been used to solve the proposed model, including the genetic and ant colony optimization algorithms. In computational testing, the best parameter settings are determined for each component, and the resulting configurations are compared in the best possible settings. The validity of the answers of the algorithms has been investigated by generating different problems in various dimensions and using the real information of Shahr Bank. The results show that the genetic algorithm provides better results compared to the ant colony algorithm, with an average of 0.93% and a maximum of 1.87% difference from the optimal solution.