Research And Application Of Vehicle Routing Optimization Based On Improved Artificial Bee Colony Algorithm

With the rapid development of China's economy,Logistics activities become more frequent,the logistics,known as the "third party profit source",plays a more and more important role in the national life.In the background of promoting low carbon logistics,logistics industry is not only facing the high logistics cost test,but also facing the pressure of the environment and energy,and the competition is becoming increasingly fierce.Logistics transportation,as an important part of logistics activities,Optimizing vehicle transportation path during transportation can effectively reduce logistics cost and reduce carbon emissions,realize the unification of economic and environmental benefits,which is of great significance to improve the core competitiveness of enterprises.The study of vehicle routing problem is an effective way to rationalize vehicle routing.In this paper,a multi-objective vehicle routing problem model with time window constraints considering carbon emission is proposed.The specific research contents are as follows:(1)This paper first introduces the vehicle routing problem model with time window constraints,and points out that the model is simple to optimize the one target and does not take into account the situation that often needs to optimize multiple targets.Therefore,the factors affecting the total transportation cost,carbon emission and customer satisfaction are analyzed,and the estimation methods of three target functions are given.A multi-objective vehicle path optimization model with time window constraints is constructed to minimize the transportation cost,minimize the carbon displacement and maximize customer satisfaction.(2)Considering the complexity of multi-objective vehicle routing optimization model with time windows constraints,an improved artificial bee colony algorithm is applied to solve the problem.The tabu table in the tabu search algorithm is introduced to the artificial bee colony algorithm to avoid the blindness of the algorithm search.At the same time,the generation of initial solution,the neighborhood search strategy,the food source evaluation strategy and the food source update strategy are studied.(3)The improved artificial bee colony algorithm is used to simulate the multi-target model in the enterprise environment,a series of Pareto solutions are obtained and the results are compared with those obtained by the original algorithm,the validity of the algorithm is verified.Finally,the TOPSIS comprehensive evaluation method is used to evaluate the Pareto solution set.The optimal vehicle scheduling scheme is obtained.