The Research Of Wartime Vehicle Scheduling Problem With Non-Full Load Based On Genetic Simulated Annealing Algorithm

Within the studies of Military Logistics, Wartime VSP (Vehicle Scheduling Problem) has always been a hot research issue both in theory and practice. That is because of the fact that cost of transportation holds a large proportion of the total cost of Logistics. In the wartime capacity tight budgetary situation, achieving transportation mission safely and efficiently is the key to master the initiative of war, and also the object of Military Logistics. However, existing Logistics theory mainly aim at lessening cost. It can not be used to solve Wartime VSP directly; therefore, it is necessary to establish the more scientific vehicle scheduling model according with the fact of wartime in order to satisfy wartime requests for material transportation. Based on analysis and comparison between the research status in related fields both at home and abroad, this thesis mainly gives the following research:(1) Contraposing the character and decision-making objective of the wartime VSP, the thesis builds the multiple-objective decision model of the problem. Because wartime VSP subject to the constraints of time, security and economic factors, belonging to the typical multiple-objective decision problem, the thesis constructs the utility function of each separate objective firstly, and then aggregates through the weights by multi-attribute utility theory, builds the decision-making utility function. It transforms the multiple-objective problem into single objective problem to solve.(2) Considering the situation in solving the wartime VSP that it is necessary to solve various objective extremum, the thesis classified the Genetic Simulated Annealing Algorithm as the shortest path algorithm and the longest path algorithm when designing the algorithm. And then it uses reasonable fitness function and encoding to make sure that it is easy to realize by computer.(3) The thesis frames the tactical supposition example combining with the author's actual work. By solving the example and analyzing the result, it validates the validity and popularity of the solution, and affirms the stability of the algorithm.In the end, it summarizes the Innovation and points out existing shortage and direction that it should continue researching. Because of the less research in wartime VSP, bringing forward such task and solving it has obvious theory and application value.