We are pleased to announce the publication of a new book authored by Professor Bonggwon Kang, now available through Kyobo Book Centre.

• Title: Production Logistics System: Simulation Modeling

• Author: Bonggwon Kang

• Publisher: Kyobo Book Centre

• Link: Click here to view the book

This book provides a comprehensive introduction to discrete-event simulation modeling with a particular focus on production and logistics systems. Grounded in practical experiences from semiconductor and display industries, it offers both key concepts and techniques for digital twin applications. The book is intended as a practical resource for researchers, undergraduate students, and industry practitioners who wish to build simulation models, analyze system performance, and apply simulation-based insights to manufacturing and logistics environments.

We hope this book will contribute to academic research and industrial practices, and we warmly invite readers to explore its contents.

"A digital twin calibration for an automated material handling system in a semiconductor fab" has been accepted for publication in Journal of Manufacturing Systems, a well-regarded journal in the Operations Research and Management Science field.

Abstract) To address the complex, dynamic, and stochastic nature of an automated material handling system (AMHS) in a semiconductor fabrication facility (fab), practitioners have used a high-fidelity discrete-event simulation as its digital twin model for decision-making over several decades. Previous studies have focused on fast digital twin-based decision-making in AMHSs under the assumption that their digital twin models are credible enough to prescribe decisions. However, parameter uncertainty and intrinsic model bias in an AMHS digital twin model can lead to an inaccurate representation of its field system. To address the challenge, this paper introduces the Bayesian calibration, which modularly estimates a digital twin outcome and its discrepancy using Gaussian process priors. A calibration framework for digital twin-based decision-making is also presented using an AMHS example. Our experimental results with various AMHS operating scenarios demonstrate that: (1) a sophisticated digital twin calibration is necessary, especially when AMHSs operate under heavy-workload scenarios; and (2) exploring the model bias considerably decreases the prediction error of an AMHS digital twin with a limited number of field observations. Moreover, we discuss the applicability of the approach across various domains of digital twin applications.

We are pleased to announce that our patent titled "Method and Apparatus for Deriving Management Policy of Vehicles" has been officially registered in March 2025. 

• Patent Holders: S. Hong, B. Kang 

• Patent Type: Korean Patent Funding

• Source: Research and Business Development Foundation, Pusan National University 

This patent introduces an innovative method and system for optimizing vehicle management policies, enhancing efficiency and decision-making in transportation systems. 

"A Simulation Study of the Vehicle Repositioning Policy with the Minimum and Maximum Service Levels in a Demand Responsive Transit System" was published in the Journal of the Korean Society of Supply Chain Management. 

Abstract) Public transportation faces challenges due to the depopulation in rural areas and the increase in unprofitable routes caused by population polarization. Demand responsive transit has emerged to provide flexible public transit services tailored to various demand patterns. In this study, we present the vehicle repositioning policy based on the minimum and maximum service levels. The proposed policy is to adjust the service levels of each station for a prompt reaction to transit requests. To consider system complexity and dynamics, we develop a discrete-event simulation model and comparatively investigate the impact of the vehicle repositioning policy on operational performance. The simulation experiments empirically demonstrate that the suitable setting of the minimum and maximum service levels significantly improves operational efficiency compared to conventional vehicle repositioning policies.

I delivered a presentation at the Fall KIIE Conference in Seoul, South Korea, titled 'Discrete-event Simulation Calibration for a Large-scale Material Handling System: A Case Study of a Semiconductor Fab'. This research highlights the use of Bayesian calibration for digital twin construction in an automated material handling system within a semiconductor fab. I sincerely thank Professor Park, Professor Kim, and Professor Hong for their valuable feedback on this research!

The 3rd Joint Workshop on Recent Digital Twin and Production Logistics Research (DTPL) was held on August 20, 2024, at the University of Washington. Attendees are Dr. Bonggwon Kang, Mr. Bosung Kim, Prof. Soondo Hong, and Prof. Chiwoo Park. During the workshop, we had in-depth discussions on future collaborative plans related to digital twin calibration approach.

The 2nd Joint Workshop on Recent Digital Twin and Production Logistics Research (DTPL) was held from August 11 to 15, 2024, at the University of Washington. Attendees included Bonggwon Kang, Gwangheon Lee, Prof. Soondo Hong, and Prof. Chiwoo Park. During the workshop, we engaged in in-depth discussions on collaborative research related to digital twin-based decision-making in the warehouse and semiconductor industries. We would like to extend our special gratitude to Prof. Park for his significant efforts and time in organizing and inviting us to this event.

I and Professor Hong participated in the joint meeting on the recent digital twin research and simulation education at the University of Washington from February 18th to 23th. We had a couple of meetings on the research topics such as the digital twin and its industry application. We'd like to express gratitude to Professor Park for his effort for organizing this workshop.

I gave a presentation at the 'Semiconductor Smart Manufacturing Working Group' during a planning session at the 2023 KIIE conference. The title of my talk was "Simulation-based optimization alternatives for large-scale material handling systems". I would like to express my gratitude to my professors Hong and Kim for their support. Furthermore, I'd like to extend a special thanks to the audience for their constructive comments and attentive engagement throughout my presentation."

"Simulation optimization of collaborative handshake operations for twin overhead shuttle cranes in a rail-based automated container terminal under demand uncertainty" has been accepted for publication in IEEE Access.

Abstract) A handshake operation can mitigate workload imbalance and interference between twin transporters in a material handling system. Terminal operators in a rail-based automated container terminal can employ the handshake operation to twin overhead shuttle cranes (OSs) under maritime demand uncertainty. Since a handshake location is critical to collaboration performance, terminal operators often rely on simulation experiments with a manual iterative design to determine optimal handshake locations. However, the simulation optimization is still challenging when a simulation execution is computationally expensive. This study proposes a Bayesian optimization-based approach to expedite the decision-making process. The approach infers the conditional outcomes of a simulation and actively searches optimal handshake locations. Our optimization results show that the proposed approach maximizes the collaborations between the twin OSs within fewer simulation runs. This study also provides extensive simulation analysis of the handshake locations. The experiments indicate that a handshake location has a significant influence on the required space for handshake operations and the workloads of the twin OSs.

"A Simulation-based Optimization for Scheduling in a Fab: Comparative Study on Different Sampling Methods" has been accepted in Journal of the Korea Society for Simulation.

Abstract) A semiconductor fabrication facility(FAB) is one of the most capital-intensive and large-scale manufacturing systems that operate under complex and uncertain constraints through hundreds of fabrication steps. To improve fab performance with intuitive scheduling, practitioners have used weighted-sum scheduling. Since the determination of weights in the scheduling significantly affects fab performance, they often rely on simulation-based decision making for obtaining optimal weights. However, a large-scale and high-fidelity simulation generally is time-intensive to evaluate with an exhaustive search. In this study, we investigated three sampling methods (i.e., Optimal Latin hypercube sampling(OLHS), Genetic algorithm(GA), and Decision-based sequential search(DSS)) for the optimization. Our simulation experiments demonstrate that: (1) three methods outperform greedy heuristics in performance metrics; (2) GA and DSS can be promising tools to accelerate the decision-making process.

I participated in an international joint research and the preprint of our manuscript is now available here. The research title is "Active Learning of Piecewise Gaussian Process Surrogates". It is such a big honor for me to join the collaborative research with Chiwoo Park, Robert Waelder, Benji Maruyama, Soondo Hong, and Robert Gramacy. I truly learned a lot from the collaboration. 

Abstract) Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity

I had a presentation at PNU Grand Performance Exchange Programme. My topic was "Simulation-based decision making in large-scale simulations".  Since many manufacturing and material handling systems have enlarged the scales of factories and warehouses to achieve economies of scale, simulation has been widely used as a promising tool to diagnose and predict their decision making for upcoming demands. In this presentation, I shared my recent research progress and got comments from other graduate students.  

"A GA-based Optimization of a Weighted Lot Targeting Rule in a Semiconductor Wafer Fab" was published in Journal of the Korean Institute of Industrial Engineers. The paper is now available here.

Abstract) Production scheduling in a semiconductor wafer fabrication (FAB) can be decomposed into two phases: lot targeting and lot dispatching. A weighted dispatching rule is a widely applied concept to obtain the production schedule in the FAB under its complex manufacturing factors. The weights of the dispatching rule should be carefully determined since the weights substantially impact the performance of the FAB. In this study, we investigate a weighted lot targeting rule considering the time-variant manufacturing factors, i.e., processing times, set-up operations, work-in-process levels, and transportation times for the bottleneck (photolithography) process. We propose a Genetic Algorithm (GA) to determine the efficient weights of the weighted lot targeting rule within a limited simulation run. Our simulation experiments demonstrate that the proposed approach outperforms widely used targeting rules under the time-variant manufacturing factors in the FAB.

"A Dynamic OHT Routing Algorithm in Automated Material Handling Systems" was published in Journal of Korean Society of Industrial and Systems Engineering. The publication is now available here.

Abstract) 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.

"A Simulation-based Genetic Algorithm for a Dispatching Rule in a Flexible Flow Shop with Rework Process" was published in Korea Journal of BigData. The publication is now available here. 

Abstract) This study investigates a dynamic flexible flow shop scheduling problem under uncertain rework operations for an automobile pipe production line. We propose a weighted dispatching rule (WDR) based on the multiple dispatching rules to minimize the weighted sum of average flowtime and tardiness. The set of weights in WDR should be carefully determined because it significantly affects the performance measures. We build a discrete-event simulation model and propose a genetic algorithm to optimize the set of weights considering complex and variant operations. The simulation experiments demonstrate that WDR outperforms the baseline dispatching rules in average flowtime and tardiness.

"A Simulation Study of a Storage Policy for a Container Terminal " was published in Dynamics in Logistics. The book chapter is now available here.

Abstract) This paper proposes a storage policy for container terminals that handle large numbers of vessels and containers. The storage policy considers the estimated workload at a certain area in a given period; the partition of a storage block into subblocks; the proximities between containers belonging to the same group; the segregation between different groups of containers; and the stack heights of containers. We develop a framework for simulating container repositioning and vehicle congestion and use it to evaluate the yard crane productivity rate, amount of repositioning, and service time of a real-world port terminal. The preliminary result shows that the container terminal operates more efficiently under the storage policy with a bay as a subblock setting.