| 摘要(英) |
In this thesis, an intelligent distributed control strategy for vehicle dispatching and control will be proposed for a manufacturing system with multiple-load Automated Guided Vehicles (AGVs), Just-In-Time (JIT) production, and flexible processing and routing capability. For years, companies have been looking for ways to help them be more productive and flexible. JIT production systems and the usage of automated material handling systems are some of them. Furthermore, the rapid progress of manufacturing and control methodology allows many engineers to improve the processing and/or routing flexibility of their manufacturing systems. Small batch-size production is one of the requirements for the implementation of a JIT production system. This requirement can generate a greater demand for material handling. As a result, the success of a JIT system relies much on the efficiency of its material handling system. Many automated material handling systems have been implemented in factories. Among them, AGVs have been considered by many as the most flexible ones due to their routing flexibility. For systems with flexible processing and routing capability, their material handling tasks are even more complicated. It is thus no surprise that AGVs are the chosen material handling system in our system here. It has been proved that multiple-load AGVs have many advantages over the single-load AGVs, although their vehicle problems are significantly more difficult than those of single-load ones. By now, it is easy for us to realize that the problem we have here is much more difficult than those found in the literature. In order to achieve the JIT production goal, the activation of dispatching vehicles to pick up a product in the proposed method is based on the current process of the product. Furthermore, the problem on determining which vehicle should pick up which job is investigated. A bidding-based distributed control strategy with reasoning and learning capability will be proposed. The bidding-based distributed control strategy is used since it can meet the multiple-criterion nature of the problem and allows one to have control over the way that the bidding procedure is conducted. Other problems, such as the route plan of each vehicle, the determination of bit values, and the job selection at each workstation, etc. will also be considered at this stage. Finally, a simulation model will be constructed to test and verify the methodology we developed in this thesis. |
| 參考文獻 |
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