Research On Intelligent Agent Based Warehouse Management,Schedule & Control System

Operations management, scheduling, and control systems are essential elements in manufacturing and distribution companies. A new modeling framework for developing efficient management, scheduling, and control systems, is presented in this thesis. It can be applied in manufacturing,warehouse,distribution companies. It is a hybrid of the hierarchical and heterarchical frameworks. While the hierarchical framework may give globally optimized planning and scheduling solutions, it is not robust, especially when there are disturbances in the system. On the other hand, the heterarchical framework provides robustness but may not generate a globally optimized scheduling solution. Throughout this thesis, the term globally optimized plan is used to refer to one that is near-optimal, but based on a global perspective. Our focus is on developing a robust modeling framework that can provide a globally optimized plan and control. In this proposed framework, entities (e.g., parts) and resources (e.g., material handling devices, machines, cells, departments) are modeled as intelligent Agents to function in a cooperative manner so as to accomplish individual as well as cell-wide and system-wide objectives. Lower level components may autonomously make their negotiations within the boundary conditions that the higher level holons set. Horizontal as well as vertical decisions are made between various levels of controllers. In order to validate the effectiveness of the new framework, the proposed framework is applied to an actual Aeronautic equipment warehouse problem. There are three levels of Agents in the proposed architecture: system level global planner, control level guide, and lower level execution Agents. The system level global optimizer Agent, with its global perspective, makes a balanced and synchronized order sequence and assigns resources to each order efficiently. The control level guide Agent takes the resource assignment decision from the higher level Agent and guides the lower level Agent to achieve global optimization. The lower level execution Agents make their decisions based on real time conditions. They suggest the alteration of predetermined resource assignments but have to get permission from the control level Agent. Several optimization models are developed for the Agents. The effectiveness of the hybrid architecture is demonstrated.