Vehicle Routing Problem Study Based On Contigent Events

Vehicle routing problem is an important study element of logistics management, which solved effectively, can not only reduce distribution costs, but also increase the responsiveness speed for individuation requirement of customer, service quality and customer satisfaction for logistics and distribution service. Emergencies caused by traffic congestion, road network disruption and increased travel time would greatly increase the cost of logistics, seriously damaged the interests of logistics enterprises, and also lead to social emergency logistics and distribution and transfer of sick and wounded, and other issues. The traditional model of vehicle routing problem often neglected emergencies on the reliability of distribution service, if only using average travel time as a precondition for vehicle routing planning; in emergencies can not really meet the requirements. To reflect the stochastic and dynamic charactorics of distribution vehicle routing reasonable, we must consider more factors to build the vehicle routing problem model by introducing reliability, so that the model can be closer to the actual operation. By using various performance indicators of the network reliability, the operation features of logistics distribution system can be embodied, which tailored to special customers in order to satisfy their requirements as well as en enhancing the market competition of logistics enterprises.The main study of this paper as follows:(1) Aim to the slow convergence of basic ant colony algorithm for vehicle routing problem, a fast convergence method is proposed by using vehicle loaded rate as a regulator to control pheromone changing so that the ant can find the optimal path as soon as possible. Compared with the basic ant colony algorithm, the convergence speed and solution quality is obviously superiority in this thesis.(2) Mathematical model of vehicle routing problem based on the connectivity reliability and travel time reliability are proposed. By using ant algorithm the state transition probability formula in the ant colony algorithm, combining with connectivity reliability and travel time reliability are applicated to the vehicle routing problem under emergency, which enrich the content of vehicle routing problem. At the same time, additional parameters are added in accordance with the different nature of the problem, enriching the parameters set of ant colony algorithm. By an example, analysing and exploring how to set the values of the parameters effect on the results, which influences the reasonably setting of parameters.(3) Travel time directly impacts on customer satisfaction with the logistics and distribution services. Vehicle routing problem with fuzzy travel time should consider the impact of ambiguous appointed time on customer satisfaction. We integrate multiple objective functions applicating linear weighted weighted method, and analyse result through because of the ant colony algorithm parameters through an example.(4) According to emergency logistics characteristics when disaster happened, a solution for emergency distribution of multi-objective optimization problem based on ant cluster optimization algorithm is proposed. Multi-objective problem is transformed into a single one using linear weighted method. With the ant behavior characteristics in constructuring graves, by using improved LF ant clustering model, taking minimize dissatisfied node need and shortest travel time as the optimization objective, clustering with the constrains by LF ant clustering method, the vehicle routing lines are determined finally.The study of vehicle routing problem under emergency has just begun, its applicability has not been proved by practical application, and technology to solve is beyond a mature stage. However, based on connectivity reliability and travel time reliability of the logistics industry is beyond impact in this field. With national conditions, the network reliability theory integrate with vehicle routing problem, which in large improve the existing state of logistics services, has broad practical application prospaction. With the development of intelligent transportation systems, swarm intelligence technology will be applied to logistics planning and the great theoretical and practical significance could be expected.