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In the context of Flexible Manufacturing Systems (FMS), managing tools effectively is one of the most challenging tasks, especially when it comes to determining the optimal tool flow path. This process not only involves selecting the right tools but also aims to minimize loading and unloading times while maximizing tool life. These factors directly impact production costs and overall efficiency. However, due to the complexity of the problem, current optimization methods often rely on heuristic algorithms, which can be computationally intensive and may not always yield the best results. Additionally, there remains significant room for improvement in multi-objective and real-time control strategies. To address these challenges, this paper introduces a mean-based optimization control strategy that offers a more efficient and effective solution.
The core idea behind mean control is to make decisions based on the average performance of elements being optimized. Consider an ordered set A = {aâ‚, aâ‚‚, ..., aâ‚™}, where a subset of elements is already arranged (denoted as 'a'), another subset is unsorted (denoted as 'b'), and a single element 'aΔ' is under evaluation. The goal is to determine the optimal arrangement of these elements using an evaluation function F(a, aΔ, b, C), where C represents other influencing factors. Instead of evaluating all possible permutations—which would be computationally prohibitive—the algorithm simplifies the process by treating the elements as "average" components, allowing for a more manageable and efficient search for the optimal configuration.
In the context of FMS, the evaluation function plays a critical role in determining the best tool to use at each stage of the manufacturing process. Tools with similar functions are considered "sister tools," and although they may perform the same task, their remaining service lives can vary. The objective is to distribute the usage evenly to prevent premature wear on any single tool. The evaluation function takes into account the average remaining life of each tool, the time required for each operation, and the number of unassigned parts.
A recursive calculation process is used to evaluate each candidate tool. One element from the unsorted set is selected as the test candidate, and the evaluation function is computed. This process continues until all candidates have been evaluated, and the one with the minimum value is chosen. In cases where multiple tools yield the same minimum value, additional criteria such as variance in remaining life or distribution of usage can be applied to make a final decision.
Global decision-making also considers the tool lag time—the time a machine waits for a tool to become available. This factor significantly affects system efficiency. The position of the tool—whether in a local magazine, central magazine, or another machine—determines the lag time. By incorporating both the tool's remaining life and its availability, a total evaluation function is created to guide real-time decisions.
Several examples were tested using the mean control method. Each example involved a sequence of part demands for a specific type of tool. The results showed that the method was effective in optimizing tool utilization, with high rates of total tool life being used. The calculation times varied depending on the number of parts and tools involved, but even for larger systems, the algorithm remained efficient enough for online control.
Overall, the mean control strategy provides a practical and effective approach to tool flow optimization in FMS. It reduces computational complexity, improves tool utilization, and enhances system efficiency, making it a valuable tool for modern manufacturing environments.