Game theory
Mohammad Shafiekhani; Alireza Rashidi Komijan; Hassan Javanshir
Abstract
The process of transferring money from the treasury to the branches and returning it at specific and limited periods is one of the applications of the Vehicle Routing Problem (VRP). Many parameters affect it but choosing the right route is the most key parameter so that the money delivery process is ...
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The process of transferring money from the treasury to the branches and returning it at specific and limited periods is one of the applications of the Vehicle Routing Problem (VRP). Many parameters affect it but choosing the right route is the most key parameter so that the money delivery process is carried out in a specific period with the least risk. In the present paper, new relationships are defined in the form of three concepts in order to minimize route risk. These concepts are: (i)the vehicle does not travel long routes in the first three movements. (ii)A branch is not served at the same hours on two consecutive days. (iii)An arc should not be repeated on two consecutive days. The proposed model with real information received from Bank Shahr has been performed for all branches in Tehran. Because the vehicle routing problem is an NP-Hard problem, a genetic algorithm was used to solve the problem. Different problems in various production dimensions were solved with GAMS and MATLAB software to show the algorithm solution quality. The results show that the difference between the genetic algorithm and the optimal solution is an average of 1.09% and a maximum of 1.75%.
Industrial Mathematics
Mohammad Shafiekhani; Alireza Rashidi Komijan; Hassan Javanshir
Abstract
In this paper, a new type of vehicle routing problem 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 ...
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In this paper, a new type of vehicle routing problem 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 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 the security of the service. In the second objective function, risk is a function of the amount of money, the probability of theft and the probability of its success. To solve the proposed model, two different meta-heuristic algorithms including genetic algorithm and ant colony optimization algorithm have been used. 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 with the optimal solution.
