Yonsei University · 経営学
Professor Byung Do Chung's research lab specializes in intelligent supply chain systems, smart manufacturing, and sustainable production technologies. The lab focuses on integrating advanced digital technologies—such as cloud-based systems, IoT, and 3D printing—into dynamic, personalized supply chain operations. Key research directions include optimizing supply chain cost and performance under uncertainty, enhancing energy efficiency and sustainability in smart production, and leveraging data-driven models for dynamic pricing and demand learning.
Figures are computed from collected data and may differ slightly.
For a complex product production, any flexible manufacturing system with a work-in-process inventory is recommended for a supply chain management (SCM) system. Building a flexible manufacturing system increases the total cost of the supply chain; for this reason, a discrete investment is important. For flexible production systems, production rate within a finite specific interval of production rate as work-in-process inventory is calculated. The aim of the supply chain is to reduce the total cos
Interest in smart factories and smart supply chains has been increasing, and researchers have emphasized the importance and the effects of advanced technologies such as 3D printers, the Internet of Things, and cloud services. This paper considers an innovation in dynamic supply-chain design and operations: connected smart factories that share interchangeable processes through a cloud-based system for personalized production. In the system, customers are able to upload a product design file, an o
Dynamic traffic assignment, Transportation planning, Chance-constrained programming, Joint chance constraint, Data uncertainty,
In this paper, we propose a revenue optimization framework integrating demand learning and dynamic pricing for firms in monopoly or oligopoly markets. We introduce a state-space model for this revenue management problem, which incorporates game-theoretic demand dynamics and nonparametric techniques for estimating the evolution of underlying state variables. Under this framework, stringent model assumptions are removed. We develop a new demand learning algorithm using Markov chain Monte Carlo met
A cloud computing service is the next-generation core service of green ICT for operational management of ICT resources and maximization of energy consumption efficiency. Cloud service is expected to rapidly increase in its market size in the future through its convenience in usage, efficiency of operational management and economic effects and so on. In users’ perspectives, the choice of the appropriative cloud service is very important to meet their business goals and strategies. A cloud service
Open papers in the app to read, cite, and organize with AI.
