Logistics Facility Location Using Particle Swarm Optimization Algorithm

This program implements MATLAB source code for logistics facility location optimization using Particle Swarm Optimization (PSO) algorithm. The solution employs swarm intelligence principles where candidate locations are represented as particles moving through solution space to find optimal positions. Key algorithmic components include velocity updates, personal best tracking, and global best convergence mechanisms. The implementation helps users make data-driven decisions in logistics facility placement through iterative optimization processes. Designed with efficient matrix operations and customizable fitness functions, this robust yet user-friendly program adapts to various logistics scenarios including distribution centers, warehouses, and supply chain nodes. Users can rapidly identify optimal locations by configuring parameters like swarm size, iteration limits, and objective functions balancing transportation costs, service coverage, and operational constraints. The algorithm's parallel evaluation capability enables quick convergence to solutions that enhance logistics efficiency, reduce operational costs, and improve customer satisfaction. Suitable for both large corporations and small businesses, this tool provides competitive advantages in dynamic markets through mathematical optimization. The code structure separates core PSO mechanics from problem-specific configuration, allowing easy adaptation to different location models. Implementation features include visualization options for convergence monitoring and result validation, making it an invaluable asset for logistics planning and strategic decision-making.