Location Selection for Regional Logistics Center Based on Particle Swarm Optimization

Abstract

The location of a logistics center is very important in a logistics system, as the success of the location determines the whole logistics system’s structure, shape, and mode, and not only affects the logistics center’s own operating costs, performance, and future development, but also affects the operation of the entire logistics system. Therefore, the selection of the location for a logistics center has great significance for improving the efficiency of regional logistics and optimizing the structure of a logistics system. This study constructed a multi-factor constrained P-median site-selection model to optimize the locations of logistics centers to improve the efficiency of logistics and optimize the structure of the logistics system in a region. The results show that the optimal distribution of logistics center sites and the coverage of freight capacity demand derived from the particle swarm algorithm are more balanced than those derived by the other algorithm. Following the comparison of the results for the utility of the optimized layout points solved by the particle swarm algorithm and the immune genetic algorithm, it is concluded that the optimal fitness value obtained by the particle swarm algorithm is lower than the other. It is proven that the particle swarm algorithm of the P-median site-selection model under this multi-factor constraint has some reference value for the selection of the sites of multi-logistics centers.

Making Logistics Decisions Using Information From FLP P-Median and Qgis

“…One of the FLP techniques well known in several academic works is the pmedian [7], [4], [1], [8] and [9]. According to the FLP technique, this study raises information about the free software QGIS (Quantum GIS) Geographic Information System (GIS) that allows the visualization, analysis and processing of geographic data [10].…”

Three-stage location-routing programming for heavy-duty railway maintenance machinery maintenance base based on multi-factor combined weights

“…Furthermore, the predominant reliance on Genetic Algorithm (GA) to solve bi-level LRP models raises concerns related to local optimization and overall efficiency, necessitating improvements in this regard. Among the heuristic algorithms currently employed, GA, tabu search algorithms [29], Ant Colony Algorithm (ACA) [30] and others like these [31] are prominent. The ACA, in particular, boasts robust combinatorial optimization capabilities, even though it may exhibit slow convergence and susceptibility to local optima.…”

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

“…Blanco et al [ 9 ] constructed a general modeling framework for a multi-type maximal covering location problem, introducing a natural nonlinear model, reformulating it into an integer linear programming model, and developing a branch-and-cut algorithm for improved resolution. Yingyi and colleagues [ 10 ] enhanced the one-dimensional target constraint location model, proposing a multi-factor constrained P-median model that accounts for operating costs and employing the Particle Swarm Algorithm and Immune Genetic Algorithm to identify an optimal location. Jalal and others [ 11 ] proposed a multi-product, multi-period, multi-mode network design and distribution planning decision-making mathematical model that includes the flow of high-value goods, transportation modes, freight types, fleet size, and escort services, introducing a local branching mathematical method that leverages the hierarchical problem structure.…”