A Distributed Coalition Selection of Distribution Centers for Delivery Stations

In modern logistics networks, an essential challenge is determining how delivery stations autonomously select the appropriate distribution center for dispatching their shipments. Traditional optimization approaches often rely on centralized decision-making and global computations, leading to high computational costs and limited adaptability in dynamic environments. To address these limitations, this paper proposes a coalition game-based distributed decision-making algorithm, allowing delivery stations to independently form coalitions and optimize their dispatch strategies. Unlike centralized optimization methods, the proposed approach enables real-time decision-making with reduced computational complexity while maintaining both system-wide efficiency and individual delivery station satisfaction. Extensive Monte Carlo simulations demonstrate that our algorithm achieves 8% higher average delivery station utility than Genetic Algorithms and 11% higher than Particle Swarm Optimization when the scale of the delivery stations is large, whereas maintaining comparable distribution center utility. Additionally, the proposed method reduces computational time by over 50% compared to baseline, making it a scalable and practical solution for real-time logistics scheduling. These results confirm that the coalition game-based framework effectively balances individual autonomy and global efficiency, offering a promising alternative for optimizing decentralized logistics dispatch decisions.