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In the tool management of Flexible Manufacturing Systems (FMS), deciding the optimal tool flow path is one of the most critical and complex control challenges. It not only involves ensuring the correct selection of tools but also plays a vital role in reducing loading and unloading times, maximizing tool life, and ultimately lowering production costs while increasing efficiency. However, the problem's complexity makes it difficult to achieve optimal results using traditional methods.
Current optimization techniques often rely on heuristic algorithms, which are computationally intensive and lack precision in capturing the true optimization potential. Moreover, there remains significant room for improvement in multi-objective and real-time control approaches. To address these limitations, this article introduces a mean-based optimization control strategy that offers a more efficient and effective solution to the tool flow path problem.
The core idea of the mean control approach is to make global average selections by considering all elements involved in the decision-making process. The algorithm operates by dividing the elements into two groups: those already ranked (a) and those still to be arranged (b). A key evaluation function F is used to assess the performance of different configurations. By treating the elements as "average" components rather than focusing on their exact arrangement, the algorithm simplifies the optimization process and reduces computational complexity.
This method is designed to handle dynamic scenarios where multiple tools with similar functions are available. Although they may have the same processing capabilities, their remaining lifespans can vary significantly. The goal is to distribute the usage evenly across tools to avoid premature wear and ensure longevity. An evaluation function is defined based on factors such as average remaining life, expected demand time, and other relevant parameters.
The recursive calculation process involves selecting one element from the unsorted group, calculating its impact on the overall evaluation function, and then repeating the process until all elements are optimally placed. When multiple minimum values of F are encountered, the algorithm allows for flexibility, either choosing one at random or fine-tuning based on additional criteria like variance in remaining life.
In terms of global decision-making, the concept of tool lag time is introduced. This refers to the waiting time a machine experiences when a tool is not immediately available. Minimizing this lag is crucial for maintaining high production efficiency. A total evaluation function is proposed that combines both tool utilization and lag time, allowing for a balanced approach to online control.
Several examples are provided to demonstrate the effectiveness of the mean control strategy. These cases show that the method achieves high tool life utilization rates, often exceeding 90%, while keeping calculation times manageable even for large-scale systems. The results confirm that the approach is not only efficient but also suitable for real-time applications in FMS environments.
Overall, the mean control strategy presents a promising alternative to traditional heuristic methods, offering a structured, computationally feasible, and globally optimized solution for managing tool flow in flexible manufacturing systems.