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.
Highlights
The efficiency of logistics networks and systems in decreasing travel time and reaching longer-distance markets is considered important for enhanced economic growth [1].By acknowledging the importance of logistics activities, the World Bank helps countries measure their improvement by providing a logistics performance index (LPI)
This study proposes a hybrid metaheuristic model based on spatial analysis for measuring distribution performance of logistic centers quantitatively
This study analyzes the performance of logistics centers by providing the hybrid PSOGA algorithm, and the result is compared with the existing techniques and approaches such as BPSO and particle swarm optimization (PSO) to evaluate the performance of proposed method
Summary
The efficiency of logistics networks and systems in decreasing travel time and reaching longer-distance markets is considered important for enhanced economic growth [1].By acknowledging the importance of logistics activities, the World Bank helps countries measure their improvement by providing a logistics performance index (LPI). LPI methodology [2] features micro-level performance data and geospatial data in their qualitative assessments, demonstrating that geospatial data plays an important role in measuring logistics performance. Most countries consider their transportation infrastructure and logistics centers as business generators [3]. The location decision of logistics centers and their performances are crucial, as they help companies minimize costs, traffic congestion, and environmental pollution levels, and improve the scheduling system and vehicle routing [4,5,6,7].
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