An ideal logistics problem is considered as a network flow problem which generates a logistics plan and subsequently executes the plan. A real-world logistics plan is different from its ideal counterpart modeled as a network flow problem in the sense that each node of the logistics graph is operated independently with disparate objectives. In contrast to the nodes of a network flow problem, agents are considered as software entities which embody elegant reasoning ability to justify their own actions towards individual objectives, and also interact with other agents. Hence, a group of agents or a multiagent system is best suited to solve real-world logistics problems with each agent representing a node of the graph. We have built a three-tier framework where a customer's problem can be decomposed and assigned to all the agents which together generate a logistics plan. We employ two simulation software as planning tools which enable us to simulate appropriate events. The key ideas behind this paper are large-scale multiagent architectural modeling issues (scalability), computation task control, information sharing among several customers, and a problem solving procedure before the planning process. The problem solving procedure is considered as determining the computational tasks required to be invoked to initiate the planning process. We describe the implementation of the framework.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence