Optimization Study Of Collaborative Multi-Depot Electric Vehicle Routing Problem With Shared Charging Stations

Logistics distribution using electric vehicles(EVs)and new energy vehicles is gradually becoming the main service mode to satisfy the customer demands in the urban logistics networks with the promise of carbon peak and carbon neutrality.However,two main challenges exist in the implementation of EV distribution.On the one hand,the lack of charging infrastructure hinders the spread of the EV distribution.On the other hand,the restriction of EVs caused by a limited driving range and necessary charging time,and the growing demand for goods distribution,promote the occurrence of the unreasonable EV scheduling and facility coordination in the logistics networks composed of multiple depots,a large number of customers and several charging stations(CSs).A collaborative mechanism among multiple depots can effectively reduce the operating cost of logistics distribution network and improve transportation efficiency,EV distribution can reduce the environmental negative effect of logistics transportation,and sharing CSs can improve the sustainability of logistics networks and facilitate collaboration further.Therefore,the optimization research of collaborative multi-depot electric vehicle routing problem with shared charging sations proposed in this paper can simultaneously improve the economic and ecological benefits of EV distribution network,which has important theoretical significance and practical value,and build an economic and sustainable logistics network and improve the competitiveness of enterprises.In view of this,this paper mainly studies the following aspects:(1)A collaborative multi-depot EV routing problem with shared charging stations are studied,and solving it involves centralized transportation services among multiple depots,coordinated delivery services among multiple depots and the charging needs of EVs.First,a bi-objective nonlinear programming model for solving the problem is formulated to minimize the total operating cost and number of EVs.Then,Gaussian mixture clustering algorithm(GMCA)is developed to cluster customers and assign them to depots probabilistically to reduce the computational complexity,and an improved multi-objective genetic algorithm with tabu search(IMOGA-TS)is designed to optimize the EV routes and charging locations.With the combination of local and global searches and the improvement of solutions at each iteration by TS,the proposed hybrid algorithm can significantly accelerate the efficient exploration of optimal solutions.Finally,a realworld case study of EV distribution network in Chongqing City is conducted to further prove that the proposed methods are of practical significance in reducing operating costs,improving transportation efficiency,and achieving sustainable operation in EV-based logistics distribution networks.(2)A collaborative multi-depot multi-period EV routing problem with shared charging stations are studied.Resource sharing,among multiple depots within multiple service periods is proposed to adjust the transportation resource configuration for a sustainable logistics development.To solving a periodic CS selection and a multi-depot multi-period EV routing optimization,a bi-objective mathematical programming model is proposed to formulate the problem with a minimum total operating cost and number of EVs.A hybrid algorithm combining the three-dimensional clustering algorithm with the improved nondominated sorting genetic algorithm-II(INSGA-II)is designed to address the model.The clustering algorithm is employed to assign customers to appropriate depots in various service periods and the INSGA-II is adopted to obtain the Pareto optimal solutions by using the CS insertion operation to select CS and integrating the elite retention mechanism to ensure a stable and excellent performance.In addition,the empirical study of a logistics network in Chongqing and the multi-algorithm comparison results demonstrate that the multi-depot and multi-period resource sharing can satisfy the periodic needs of customers,and effectively coordinate the charging demand and distribution tasks of EVs.It can further improve the transportation efficiency and optimize the allocation of resources.The construction of collaborative multi-depot joint distribution network alliance with shared charging stations is studied.Firstly,the Shapley value model is applied to allocate the extra profit generated by the collaborative multi-depot alliance.Secondly,The benefits of each participant obtained from the establishment of collaborative alliance is measured by calculating the percentage of cost reduction based on the profit allocation results.The feasibility of the alliance sequence is analyzed according to the strict monotone path(SMP),and the optimal alliance sequence is selected based on the diagonal rule.Then,the allocation schemes calculated by various methods are compared and analyzed to verify the effectiveness of the proposed allocation method based on the snowball theory.The distance between each scheme and the core of the alliance is compared to ensure the stability and sustainability of the collaborative multi-depot alliance.Finally,the collaborative multi-depot mechanism with three different collaboration strategies and the charging station sharing strategy with 11 different alliances are discussed,and the optimal collaboration mode and alliance mechanism are selected by comparing the operating costs and the number of EVs used before and after the optimization to maximize the use of resources,which is conducive to the sustainable development of logistics network.