Study On Vehicle Routing Problem Based On The Lowest Fuel Consumption Of Logistics Distribution

The logistics is the basic industry of the modern economy and occupies a very important role in the economic activities. Logistics distribution is an important link in modern logistics, vehicle routing is the key to the logistics distribution optimization. Optimizing delivery route can improve vehicle utilization, reduce travel and service cost, save fuel, bring the enormous economic benefits to enterprises. This paper is concerned with the research on the logistic distribution based on the lowest fuel consumption. The specific work is as follows:First, the concept of vehicle routing problem (VRP) is presented, and the common model of VRP is built. Several algorithms of the problem are described and compared. We try to use modern heuristic algorithm to solve this problem.Thereafter, the problem of fuel consumption in the logistics activities is analyzed. The lowest fuel consumption model of VRP is built.Next, by analyzing the characteristics and algorithms for the model, the improved GA and ACA are used to solve this problem. Selection operator, crossover operator and mutation operator are improved in GA. The pheromone update method and heuristic function are improved in ACA. These two algorithms are designed to solve the model.Then, it is difficult to get a better result by only single algorithm. The disadvantages and advantages of GA and ACA are analyzed and we try to use GA hybrid ACA to solve VRP. This algorithm thought is as follows:at former stage, it uses GA to form the initial solution rapidly, and then transforms the initial solution into pheromone needed by ACA, at last, makes use of the characters of ACA to find the optimal result quickly. This algorithm makes use of the advantages of GA and ACA. It avoids the redundant iteration at later period of GA and overcomes the difficulties of lack of information element at early period of ACA, greatly improves the search efficiency.At last, the program is designed to solve the model with hybrid ACA, and obtain better results.