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This paper introduces a Threshold- and Priority-Based Dispatching Rule (TPDR) heuristic for dynamic scheduling optimization in Make-To-Order Automated Manufacturing Systems, addressing the challenge of managing high-volume, highly-customized orders while preventing resource contention. The TPDR heuristic dynamically adjusts the dispatching sequence based on real-time system/machine performance, aiming to minimize flow time and avoid deadlocks. Through a discrete-event simulation case study of a Mail Order Pharmacy Automation system, the TPDR heuristic outperformed three widely used dispatching rules in terms of productivity and bottleneck reduction while maintaining throughput.
A novel dispatching rule slashes flow time in automated manufacturing by dynamically prioritizing orders based on thresholds and real-time system performance.
Efficient production planning is a critical and challenging task in Make-To-Order (MTO) Automated Manufacturing Systems (AMSs), requiring a flexible production process capable of managing large volumes of highly-customized orders while preventing resource contention. Considering the timing of customers’ needs and the availability of production resources, it becomes important to find an efficient order-dispatching sequence to optimize the coordination across multiple production units. To achieve this, a simulation model is essential to evaluate and validate the proposed algorithm’s performance prior to real-world implementation. In this study, a heuristic algorithm based on a Threshold- and Priority-Based Dispatching Rule (TPDR) is presented aimed at minimizing flow time while avoiding potential deadlocks and meeting key performance indicators (KPIs). The proposed heuristic is integrated into a discrete-event simulation (DES) framework, allowing for dynamic adjustments to the dispatching sequence of high-volume and highly-customized orders based on real-time system/machine performance. To assess its effectiveness, a case study of a Mail Order Pharmacy Automation (MOPA) system is conducted within three DES models, comparing the proposed TPDR-based heuristic with three widely used dispatching rules. The simulation results demonstrate that the TPDR-based heuristic algorithm significantly enhances productivity and eliminates production bottlenecks while maintaining throughput levels.
