Bong-Ju Jeong
Yonsei University · 工学
研究室紹介
Professor Bong-Ju Jeong's research lab specializes in sustainable materials and advanced manufacturing systems, with a strong focus on resource recovery, recycling technologies, and circular economy strategies for high-tech and energy-related industries. The lab investigates intelligent optimization methods—such as evolutionary algorithms and robust modeling—for disassembly planning, supply chain management, and electric vehicle logistics under uncertainty. Key research directions include the recovery of critical raw materials (e.g., indium, gallium, rare earth elements) from end-of-life photovoltaic systems and electronic devices, as well as the development of sustainable supply chain frameworks under policies like Extended Producer Responsibility and strategic stockpiling. The lab also explores advanced materials, particularly high-performance steels with enhanced mechanical and recyclability properties, to support green industrial transformation.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The photovoltaic (PV) generation system has been widely used since the late 1990s. Considering its lifespan of 20 to 30 years, many end-of-life systems will emerge in the near future. This is why recycling PV systems will be beneficial (and may even be detrimental) to both the environment and the economy. Through the recycling process, hazardous by-product substances such as cadmium and lead can be treated properly. Moreover, valuable materials including indium, gallium, and tellurium can be ext
Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined
Rare metals (RMs) are becoming increasingly important in high-tech industries associated with the Fourth Industrial Revolution, such as the electric vehicle (EV) and 3D printer industries. As the growth of these industries accelerates in the near future, manufacturers will also face greater RM supply risks. For this reason, many countries are putting considerable effort into securing the RM supply. For example, countries including Japan, Korea, and the USA have adopted two major policies: the st
This study aims to improve the efficiency of disassembly planning in remanufacturing environment. Even though disassembly processes are considered as the reverse of the corresponding assembly processes, under some technological and management constraints the feasible and efficient disassembly planning can be achieved by only well-designed algorithms. In this paper, we propose a heuristic for disassembly planning with the existence of disassembled part/subassembly demands. A mathematical model is
As the greenhouse gas emission regulations have strengthened, establishing a sustainable transportation system has become more essential. Thus, studies on the transportation system using electric vehicles have received more research attention. However, operation using electric vehicles has obstacles such as technical limitations of vehicle batteries and insufficient number of charging stations, which can be much affected by the traffic flow changes. Therefore, we propose a robust electric vehicl