Design and Profit Allocation in Two-Echelon Heterogeneous Cooperative Logistics Network Optimization

Abstract

In modern supply chain, logistics companies usually operate individually and optimization researches often concentrate on solving problems related to separate networks. Consequences like the complexity of urban transportation networks and long distance deliveries or pickups and pollution are leading problems to more expenses and more complaints from environment protection organizations. A solution approach to these issues is proposed in this article and consists in the adoption of two-echelon heterogeneous cooperative logistics networks (THCLN). The optimization methodology includes the formation of cooperative coalitions, the reallocation of customers to appropriate logistics facilities, and the determination of the best profit allocation scheme. First, a mixed integer linear programing model is introduced to minimize the total operating cost of nonempty coalitions. Thus, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are hybridized to propose GA-PSO heuristics. GA-PSO is employed to provide good solutions to customer clustering units' reallocation problem. In addition, a negotiation process is established based on logistics centers as coordinators. The case study of Chongqing city is conducted to verify the feasibility of THCLN in practice. The grand coalition and two heterogeneous subcoalitions are designed, and the collective profit is distributed based on cooperative game theory. The Minimum Cost Remaining Savings (MCRS) model is used to determine good allocation schemes and strictly monotonic path principles are considered to evaluate and decide the most appropriate coalition sequence. Comparisons proved the combination of GA-PSO and MCRS better as results are found closest to the core center. Therefore, the proposed approach can be implemented in real world environment, increase the reliability of urban logistics network, and allow decision makers to improve service efficiency.

Multidepot Recycling Vehicle Routing Problem with Resource Sharing and Time Window Assignment

“…e clustering algorithm is always utilized to construct clusters to each depot in multidepot VRP to reduce computational complexity [62,63]. e traditional k-means clustering algorithm groups customers on the basis of the spatial distances between customers and clustering centers [64,65]. e 3D k-means clustering algorithm, a variant of the traditional k-means clustering algorithm, constructs clusters according to the spatial and temporal distances among clustering centers and customers.…”

Oportunidades para la transformación digital de la cadena de suministro del sector bananero basado en software con inteligencia artificial

“…Por último, y no menos importante se presentan aportes de los AG en el área de almacenamiento y gestión de inventarios [45][46][47][48][49][50][51] y en el área de selección, evaluación de proveedores respecto a una demanda variable [52][53][54][55][56][57].…”

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

“…The MCRS proposed by Heaney and Dickinson [64] is an effective profit allocation method to reasonably obtain the allocated cost for each participant in the collaboration [65,66]. Allocated costs in the MCRS can be calculated on the basis of the participants' feasible minimum and maximum costs.…”