Hybrid Metaheuristic-Based Spatial Modeling and Analysis of Logistics Distribution Center

Abstract

The location analysis of logistics distribution centers is one of the most critical issues in large-scale supply chains. While a number of algorithms and applications have been provided for this end, comparatively fewer investigations have been made into the integration of geographical information. This study proposes logistic distribution center location analysis that considers current geographic and embedded information gathered from a geographic information system (GIS). After reviewing the GIS, the decision variables and parameters are estimated using spatial analysis. These variables and parameters are utilized during mathematical problem-based analysis stage. While a number of existing algorithms have been proposed, this study applies a hybrid metaheuristic algorithm integrating particle swarm optimization (PSO) and genetic algorithm (GA). Using the proposed method, a more realistic mathematical model is established and solved for accurate analysis of logistics performance. To demonstrate the effectiveness of the proposed method, Korea Post distribution centers were considered in South Korea. Through tests with several real-world scenarios, it is proven experimentally that the proposed solution is more effective than existing PSO variations.

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

“…Jun and associates [ 6 ] introduced three socio-economic indicators—economic development, traffic congestion levels, and total logistics demand—and crafted a two-stage model that enhances clustering algorithms and the centroid method, addressing multi-facility issues in practical scenarios. Maryam and Hyunsoo [ 7 ] focused on minimizing transportation costs between nodes, utilizing an integrated meta-heuristic algorithm that merges Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to solve the challenge of optimal site selection for logistics centers. Ge and others [ 8 ] explored the facility location problem in the U.S. fresh produce supply chain, proposing a model that incorporates empirical scenarios to acquire vital information for making optimal location decisions.…”

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

“…Swarm intelligent optimization algorithms are represented by ant colony algorithm, particle swarm algorithm, and genetic algorithm, which are widely used in many fields such as neural networks, electric power, the chemical industry, image recognition, and so on [23][24][25]. With the continuous enrichment and development of related research, many new intelligent optimization algorithms such as the cuckoo algorithm, swarm algorithm, firefly algorithm, etc., among which the swarm algorithm is simple in structure, flexible, efficient, and good in performance, which has been applied by many types of research to solve the optimization problems in many fields, and proved its effectiveness and superiority [26][27][28].…”

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

“…Therefore, there is a need to utilize practical algorithms utilizing the heuristic information available in the problem model [6]. Accordingly, hybrid heuristic-metaheuristic techniques are recently more favored for solving complex optimization problems [7][8][9][10].…”