- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
The routing optimization problem of grain emergency vehicle scheduling with three objectives is studied in this paper. The objectives are: maximizing satisfaction of the needs at the emergency grain demand points, minimizing total cost of grain distribution and minimizing the distribution time. A hybrid algorithm is present to solve the proposed problem based on combining artificial immune and ant colony optimization (ACO) algorithms. This hybrid algorithm calculates the degree of crowding and conducts non-dominated sorting of the population in the ant colony optimization algorithm by applying a Pareto optimization model. A better solution set is quickly generated by making use of the fast global convergence and randomness of the improved immune algorithm together with the distributed search ability and positive feedback of the ACO algorithm. A better solution set obtained as the initial pheromone distribution, is solved further by using ACO until the approximate optimal solution set is obtained. A comparison of the proposed algorithm with several common optimization algorithms on the Solomon benchmark dataset demonstrates that this method obtains better performance in shorter time, and is an efficient way to solve the vehicle routing problem in emergency grain distribution scenarios.
This article established a multi-objective mathematical model for emergency grain distribution route optimization. This problem is based on the actual problem of emergency food logistics optimization together with its specific requirements and characteristics. This model was solved by the improved and optimized IACO algorithm and experimental simulation data were used to obtain the optimal route. This approach nicely solves "the last kilometer" problem in emergency grain distribution logistics. A hybrid algorithm was proposed to address the shortcomings of standard metaheuristic algorithms. ACO, IA, and GA were employed to assess IACO performance, and the results show that the IACO algorithm performs better than the other algorithms in this paper. Therefore, this study demonstrated the effectiveness of IACO for multi-objective optimization problems, and it can be concluded that IA combined with ACO is a potential tool to obtain a vehicle routing solution for emergency grain distribution logistics.