使用引力搜索算法和多智能体系统优化供应链管理
Optimizing Supply Chain Management using Gravitational Search Algorithm and Multi Agent System
AI总结：
研究构建了一个多智能体系统（MAS），实现了改进的引力搜索算法（GSA）来优化印度食品公司的粮食分配供应链，通过模拟万有引力定律动态平衡供需，并减少通信瓶颈。
摘要：
供应链管理是一个非常动态的运筹学问题，人们必须根据环境中感知到的变化快速适应，以最大化收益或最小化损失。因此，我们需要一个能根据不断变化的需求而改变的系统。近年来，多智能体系统技术已成为许多此类复杂问题高效解决方案实现的一种可能途径。我们在此的研究重点是构建一个多智能体系统（Multi Agent System, MAS），该系统实现了引力搜索群体智能算法（Gravitational Search swarm intelligence Algorithm, GSA）的修改版本，以找出管理需求供应链的最优策略。我们将印度食品公司（Food Corporation of India, FCI）各中心之间的粮食分配系统作为应用领域。我们假设库存较大的中心为质量更大的物体，反之亦然。按照GSA中建议的应用牛顿万有引力定律，质量较大的物体会将质量较小的物体吸引向自身，从而创建一个虚拟的粮食供应源。随着较重物体通过向需求方供应部分物资而减轻其质量，它会失去引力，从而使整个供应链系统保持完美的平衡。多智能体系统借助对环境变化做出反应并据此行动的自主智能体，帮助实现整个系统的持续更新。该模型还在很大程度上减少了通信瓶颈。
英文摘要：
Supply chain management is a very dynamic operation research problem where one has to quickly adapt according to the changes perceived in environment in order to maximize the benefit or minimize the loss. Therefore we require a system which changes as per the changing requirements. Multi agent system technology in recent times has emerged as a possible way of efficient solution implementation for many such complex problems. Our research here focuses on building a Multi Agent System (MAS), which implements a modified version of Gravitational Search swarm intelligence Algorithm (GSA) to find out an optimal strategy in managing the demand supply chain. We target the grains distribution system among various centers of Food Corporation of India (FCI) as application domain. We assume centers with larger stocks as objects of greater mass and vice versa. Applying Newtonian law of gravity as suggested in GSA, larger objects attract objects of smaller mass towards itself, creating a virtual grain supply source. As heavier object sheds its mass by supplying some to the one in demand, it loses its gravitational pull and thus keeps the whole system of supply chain per-fectly in balance. The multi agent system helps in continuous updation of the whole system with the help of autonomous agents which react to the change in environment and act accordingly. This model also reduces the communication bottleneck to greater extents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
