| In the context of rapid economic development,the development of China’s logistics also ushered in a period of vigorous development.The increasingly perfect logistics system has greatly improved the circulation rate of goods compared with the past,set off an upsurge of online shopping,and bring convenience to people’s life.However,due to the rapid development of logistics in China,there are still some deficiencies.Among them,the combination optimization problem of logistics distribution center location and distribution path optimization has been treated as two independent problems in the past,with less interaction and lack of two-way constraints,resulting in an increase in cost and distribution time,which has certain limitations compared with system optimization.With the development of economic level,the quantity of goods circulation and the number of customers is increasing day by day.How to better improve the quality of distribution services,avoid falling into low price competition,further reduce operating costs,and thus increase their own competitive strength is the focus of future development of logistics enterprises.At the same time,in the current trend of global warming,the problem of high energy consumption caused by the increasing business of the logistics industry also needs to be solved.It needs to be further optimized to reduce carbon dioxide emissions,so as to actively respond to the national "carbon peaking and carbon neutrality" policy.In view of the above problems,in order to solve the problem of lack of systematic optimization of logistics distribution center location and distribution path optimization(LRP),considering the logistics center construction cost,customer distribution cost,the minimization of carbon emissions generated by transport vehicle energy consumption,and the maximization of customer satisfaction,the logistics distribution center location problem is included in the upper model,and the distribution path optimization problem is included in the lower model,The bilevel programming model is established.in addition,an immune-improved ant colony algorithm is designed for this model.The immune optimization algorithm is combined with the improved multi-objective ant colony algorithm to solve the problem,compared with traditional ant colony algorithm,it can avoid falling into local optimum when solving such problems.In the upper model,the immune optimization algorithm is used to optimize the location problem of logistics distribution center,and the demand points are clustered to obtain the distribution scheme of demand points.The multi distribution center problem is transformed into a single distribution center problem,thus reducing the size of the algorithm and optimizing the solution results;In the lower model,the improved multi-objective ant colony algorithm is used to optimize the distribution path,and a number of Pareto optimal solutions are obtained,generating a variety of distribution schemes based on different objectives,providing a multi angle reference for enterprises.Finally,a related case is used to verify the validity and rationality of the bilevel programming model.This model can effectively reduce the operating costs of logistics enterprises,reduce carbon emissions,and improve the service quality and customer satisfaction of enterprises;Accelerate the implementation of the "carbon peaking and carbon neutrality" policy,and lay a solid foundation for future sustainable development.Figure 24 Table 6 Reference 94... |
