There is a demand for introducing a challenging and innovative R&D system to develop new technologies to generate weapon system requirements. Despite the increasing trend in annual core technology development tasks, the infrastructure expansion, including personnel in research management institutions, is relatively insufficient. This situation continuously exposes difficulties in task planning, selection, execution, and management. Therefore, there is a pressing need for strategies to initiate timely research and development and enhance budget execution efficiency through the streamlining of task agreement schedules. In this study, we propose a strategic model utilizing a flexible workforce model, considering constraints and optimizing workload distribution through resource allocation to minimize bottlenecks for efficient task agreement schedules. Comparative analysis with the existing operational environment confirms that the proposed model can handle an average of 67 more core technology development tasks within the agreement period compared to the baseline. In addition, the risk management analysis, which considered the probabilistic uncertainty of the fluctuating number of core technology research and development projects, confirmed that up to 115 core technology development can be contracted within the year under risk avoidance.

Determining the number of operators who set up the machines in a human-machine system is crucial for maximizing the benefits of automated production machines. A man-machine chart is an effective tool for identifying bottlenecks, improving process efficiency, and determining the optimal number of machines per operator. However, traditional man-machine charts are lacking in accounting for idle times, such as interruptions caused by other material handling equipment. We present an adjusted man-machine chart that determines the number of machines per operator, incorporating idleness as a penalty term. The adjusted man-machine chart efficiently deploys and schedules operators for the hole machining process to enhance productivity, where operators have various idle times, such as break times and waiting times by forklifts or trailers. Further, we conduct a simulation validation of traditional and proposed charts under various operational environments of operators’ fixed and flexible break times. The simulation results indicate that the adjusted man-machine chart is better suited for real-world work environments and significantly improves productivity.

In an automotive plant, an automated storage and retrieval system (ASRS) synchronizes material handling flows from a part production line to an auto-assembly line. The part production line transfers parts on small-/large-sized pallets. The products on pallets are temporarily stored on the ASRS, and the ASRS retrieves the products upon request from the auto-assembly line. Each ASRS aisle is equipped with narrow-/wide-width racks for two pallet sizes. An ASRS aisle with narrow-/wide-width racks improves both storage space utilization and crane utilization while requiring delicate ASRS aisle design, i.e., the locations of the narrow-/wide-width racks in an ASRS aisle, and proper operation policies affect the ASRS performance over demand fluctuations. We focus on operation policies involving a common storage zone using wide-width racks for two pallet sizes and a storage-retrieval job-change for a crane based on assembly-line batch size. We model a discrete-event simulation model and conduct extensive experiments to evaluate operation policies. The simulation results address the best ASRS aisle design and suggest the most effective operation policies for the aisle design.

An automated material handling system (AMHS) has been emerging as an important factor in the semiconductor wafer manufacturing industry. In general, an automated guided vehicle (AGV) in the Fab’s AMHS travels hundreds of miles on guided paths to transport a lot through hundreds of operations. The AMHS aims to transfer wafers while ensuring a short delivery time and high operational reliability. Many linear and analytic approaches have evaluated and improved the performance of the AMHS under a deterministic environment. However, the analytic approaches cannot consider a non-linear, non-convex, and black-box performance measurement of the AMHS owing to the AMHS’s complexity and uncertainty. Unexpected vehicle congestion increases the delivery time and deteriorates the Fab’s production efficiency. In this study, we propose a Q-Learning based dynamic routing algorithm considering vehicle congestion to reduce the delivery time. The proposed algorithm captures time-variant vehicle traffic and decreases vehicle congestion. Through simulation experiments, we confirm that the proposed algorithm finds an efficient path for the vehicles compared to benchmark algorithms with a reduced mean and decreased standard deviation of the delivery time in the Fab’s AMHS.