Research On Cross Docking Vehicle Scheduling And Routing Optimization Based On Bee Colony Algorithm

With the rapid development of the Internet and modern logistics technologies,the demand for logistics and distribution has increased.As a result,the competition for market efficiency of various companies has become increasingly fierce.In order to improve the market competitiveness and market share of enterprises,cutting down the cost of logistics and distribution has become an effective way for companies to increase their profits.As a new type of logistics distribution,cross-docking can achieve the goal of reducing inventory management costs and rapid distribution.At the same time,with the demand of consumers and the variety of goods increasing,the demand for vehicle delivery goods also increases.For modern logistics management,the traditional distribution model can no longer meet the current logistics demand,This requires starting from the whole logistics supply chain,effectively coordinating the operation of each link,so that the overall efficiency is optimized.Therefore,this paper aims at minimizing the problem of the sum of the operating cost of the cross-docking center and the vehicle transportation cost for the problems of cross-docking vehicle scheduling and routing optimization.The problem studied in this paper is that the outbound and inbound vehicles are all waiting in the cross-docking center.After the inbound vehicles are parked in the inbound door,they can only leave after they have completely unloaded the cargo,and then follow the type and quantity of cargoes on the vehicles that are known to be outbound door.The cargoes are delivered to the outbound door.After the cargoes are loaded on the delivery vehicles,the cargoes are delivered to the customer according to the corresponding customer area.Based on the characteristics of the problem,a mathematical model was established.Based on the total number of doors,the total number of vehicles,and the number of customers,the research issues were divided into small,medium,and large scales,and genetic algorithm,bee colony algorithm and improved bee colony algorithm were designed to solve the model.First,according to the actual situation of the research problem and the characteristics of the algorithm,the bee colony algorithm and genetic algorithm are designed respectively.The designation of bee colony algorithm of the process includes algorithm coding,initial solution generation,integer specification principle,repair strategy,the calculation of fitness and concrete steps,and then different solve the problem size.The results show that the bee colony algorithm is superior to the genetic algorithm in solving the quality and solving time.Based on the analysis of the results of the bee colony algorithm,it is concluded that there are also local optimum problems and the lack of solution quality in the initial iteration.In order to improve the performance of the bee colony algorithm,The article proposes an improved method for basic bee colony algorithm,that is to balance the exploration and development capabilities of bee colony algorithm by improving the initialization method,improving the local search mechanism,and improving the scout mechanism.From the above three aspects,the convergence speed and the solution quality of the bee colony algorithm are improved.The results obtained from three different scale examples show that the improved bee colony algorithm averages 1% longer than the bee colony algorithm in terms of solution time.In terms of solving quality,it is obviously better than bee colony algorithm.This dissertation studies the problem of cross-docking vehicle scheduling and routing optimization based on bee colony algorithm and provides theoretical support for actual integrated scheduling.