Two-Echelon Multidepot Logistics Network Design with Resource Sharing

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.

Using Routing Heuristics to Improve Cost Interoperability: Strategy, Modelling Annotations, and Dynamism

“…Precise allocation on permissible elapsing deployment period enables strategic deposition of the desired commodity across the distribution nodes as well as improved estimation of the distribution scope, deployable range, and commodity. The inclusion of strategic routing modeling warranted with ever-adapting and flexible time windows feature implementation when devising a systemic scheduling system plays a pivotal role in both simulation and actual round trip time (Luo, Wang, Tang, Guan, & Xu, 2021;Wahyuningsih & Satyananda, 2020). The execution time required to produce good solutions when simulating the solution steps for very large cases using the routing formulation is different from the constrained response time permitted during realistic implementation.…”

“…The two main decision-making categories for cost reduction capabilities are long-term pre-allocation choices, which consider demographics and resource availability (Luo et al, 2021), and short-term preallocation and stock ordering choices, which take a demand-based distribution from impacted sectors into account (Tunnisaki & Sutarman, 2023). Modeling the aspiration criteria concerning cost minimization for scheduling operations can help to improve cost interoperability.…”