Adaptive Scheduling and Tool Flow Control in Automated Manufacturing Systems

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and unstable customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has increased significantly. In traditional job shops, tooling is usually assumed as a fixed resource. However, when tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling setup, hence allows parts to be processed in small batches. In this research, a dynamic scheduling problem under flexible tooling resource constraints is studied. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in Automated Manufacturing Systems. It decomposes the overall problem into a series of static sub-problems for each scheduling window, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules.

Two types of manufacturing system models are used in simulation studies to test the effectiveness of the proposed dynamic scheduling approach. First, hypothetical models are generated using some generic shop flow structures (e.g. flexible flow shops, job shops, and single-stage systems) and configurations. They are tested to provide the empirical evidence about how well the proposed approach performs for the general automated manufacturing systems where parts have alternative routings. Second, a model based on a real industrial flexible manufacturing system was used to test the effectiveness of the proposed approach when machine types, part routing, tooling, and other production parameters closely mimic to the real flexible manufacturing operations. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including Cost Over Time (COVERT), Apparent Tardiness Cost (ATC), and Bottleneck Dynamics (BD), on due-date related performance measures under both types of manufacturing systems models. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics. This research also investigates in what conditions (e.g. the number of machines, the number of operation steps, and shop load conditions) the proposed approach works the best, and how the performance of this proposed approach changes when these conditions change.

When tooling resource is shared, parts can be routed to machines that do not have all the required tools. This may result in higher routing flexibility. However, research work to date in sharing of tooling resources often places more emphasis on the real-time control and manipulation of tools, and pays less attention to the loading of machines and initial tool allocation at the planning stage. In this research, a machine-loading model with shared tools is proposed to maximize routing flexibility while maintaining minimum resident tools. The performance of the proposed loading heuristic is compared to that of a random loading method using hypothetically generated single stage system models. The study result indicates that better system performances can be obtained by taking into account the resident tooling ratio in assigning part types and allocating tools to machines at the initial planning stage.