A Multi-Agent System using Fuzzy Logic to Increase AGV Fleet Performance in Warehouses

Market competition requires an ever increasing performance from warehouses. Coupled with information technologies, high automation levels are achieved. Such automation is seen in the use of AGVs for material handling. An important problem in AGV fleets is deciding what task should be assigned to each AGV. To tackle this problem, a multi-agent AGV system is proposed, which has three agents: an AGV agent, a Loading Point (LP) agent and a Storage Point (SP) agent. The AGV agent uses a Fuzzy system to decide what task it should take, and dispatch the AGV to the location of the task, using the A-star (A*) algorithm to find the shortest path to the task. The LP agent keeps a list of all available tasks in its corresponding loading point, such as a loading dock, and handles task requests from AGV agents. The SP agent manages a particular storage space, such as a rack section, and handles AGV requests for payloads stored in the rack or requests for free space. To validate the system, a warehouse operation was simulated and evaluated measuring the average task wait time, time to complete tasks and average jam time. Two other decision methods were used, First Come First Served (FCFS) and Contract Network (CNET), to compare with the Fuzzy method. Results show that the Fuzzy method enabled a greater average task wait time reduction than the other two decision methods, and also completed tasks in less time.