Application of improved ant colony algorithm in vehicle scheduling problem

Abstract

In this paper, the ant colony algorithms is studied, and improve the shortcomings of the algorithm, And the improved algorithm is introduced into the field of logistics transportation. Aiming at the complexity and uncertainty of logistics transportation vehicle scheduling problem, a new algorithm is designed. The experimental results show that the improved algorithm can choose the transport route, speed up the transportation speed, improve the service quality, reduce the transportation cost and increase economic benefits. Introduction The vehicle scheduling problem was first proposed by Dantzing and Ramser in 1959. Because of this problem is involved in many subject, the theoretical abstraction of many practical problems can be attributed to this problem; it has a broad application prospect. It has always been the research hotpots in the field of operations research and combination optimization[1]. Vehicle optimal scheduling problem is classified according to the relative importance of the spatial and temporal characteristics of the problem[2]. Modern logistics transportation vehicle scheduling process is complicated, and the existing mathematical methods in solving this problem is not perfect, the lack of scientific theory for guidance. To solve these problems, it is often required to make decisions and judgments with the heuristic method, and the optimal solution of the overall optimum, the total cost and the total benefit of the transportation system[3]. In China, with the rise of the logistics industry, we put forward the comprehensive requirements of economic, accuracy and flexibility for the transportation of goods. Therefore, the research work in this area is being carried out in a large scale. Scholars study the form, analysis, and model and solving method of vehicle scheduling, which have practical significance and application value to the establishment of modern logistics transportation[4]. Vehicle scheduling problem model In order to establishing the model facilitate, we make the following assumptions. Vehicle weight is known. The vehicles only can unloading and no loading at each service point. The demand point of distribution task are limited by time window. Time window range is set according to the needs of each demand point . The average speed of the vehicle is known, the distance is proportional to the time. Under these assumptions, the construction of the mathematical model is as follows: Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015) © 2015. The authors Published by Atlantis Press 656 ) ( min 1 1 1 S sign l l Z k