Abstract
Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.
Highlights
Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities
A bi-objective integer programming model is first proposed for twoechelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles
Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is designed to handle the 2E-CMDPDTW model. e customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. e Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. e comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm
Summary
Traditional multidepot vehicle routing problems with time windows (MDVRPTW) attempt to seek the optimal routes to minimize traveled distances of the vehicles and the total cost and satisfy customers’ demands. Considering the resource sharing of multiple distribution centers in a two-echelon distribution network, Wang et al [23] proposed a bi-objective mixed-integer programming model to minimize the operation cost and carbon dioxide emissions of the whole logistics network. Research on PDP, MDVRPTW, and their variants established an optimization model with a single objective to optimize the logistics network, especially to minimize the cost, waiting time of customers, and number of transportation tools of the network [31, 37, 45]. Both the operating efficiency and cost of logistics systems can be improved via resource sharing in collaborative networks. In response to the research needs, the major contributions of this study can be summarized in the following aspects. (1) Constructing a collaborative 2E-MDLPDN, and discussing the influence of resource sharing on the network optimization. (2) Establishing a bi-objective integer programming model for minimizing the total operating cost and the number of vehicles for the 2E-CMDPDTW. (3) Designing a hybrid method integrating the customer clustering process, the greedy algorithm, and the Im-NSGA-II to solve the optimization model efficiently. (4) Proposing an optimal alliance sequence selection method on the basis of the Shapley value method and the SMP method to solve the problems of profit allocation and the order of joining an alliance
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