In‐Jae Jeong
Hanyang University · 工学
研究室紹介
Professor In-Jae Jeong's research lab specializes in distributed decision-making and optimization in complex, multi-agent systems, with a focus on industrial and transportation systems. The lab develops advanced methodologies for cooperative scheduling, maintenance planning, and resource allocation under information asymmetry and decentralized control. Key research directions include distributed optimization using Lagrangian relaxation, intelligent fault diagnosis via probabilistic models, and sustainable logistics for alternative fuel vehicles. The lab emphasizes real-world applicability in manufacturing systems, smart mobility, and energy infrastructure.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This paper describes an integrated decision support system to diagnose faults and generate efficient maintenance and production schedules. The proposed integrated system is composed of three modules, namely, the Diagnosis Module, the Maintenance Planning Module, and the Scheduling Module. In the Diagnosis Module, a vector of symptoms is fed into an influence diagram representing the causal relationships of the system. Given an instantiation of the symptom vector, a stochastic sampling algorithm
Abstract This paper develops a methodology for decision-making in organizationally distributed systems where decision authorities and information are dispersed in multiple organizations. Global performance is achieved through cooperative interaction and partial information sharing among organizations. The information shared among organizations is contrived using modified Lagrangian relaxation techniques. Novel to the methodology is that no single master problem with a global view of the system i
This paper considers a distributed job shop scheduling problem where autonomous sub-production systems share common machines with each other. Each sub-production system is responsible for the scheduling of a set of jobs to minimise the total completion time on shared machines. A sub-production system has ultimate responsibility on maintaining private information such as objective function, processing time and routings on shared machines. Also sub-production systems must cooperate each other in o
This article deals with the refueling-station location problem for alternative fuel vehicles in a traffic network. Alternative fuel vehicles can be characterized by the vehicle range that limits the travelable distance with fuel at full capacity. I propose an efficient formulation of the refueling-station location problem using an optimal property and prove that the problem is NP(Non-deterministic Polynomial)-complete in the strong sense. I consider a special case of the refueling-station locati
A driver who drives an alternative-fuel vehicle (AFV) from an origin point to a destination point needs to consider how to get there (i.e., the routing problem), when to stop, and how and when to refuel (i.e., the refueling plan). In this study, models and algorithms are proposed that optimize a one-way-trip path such that the total travel time from the origin to the destination is minimized. The travel time consists of the setup time, the refueling time and the driving time. The setup time incl
This paper considers a single-machine scheduling problem where the decision authorities and information are distributed in multiple subproduction systems. Subproduction systems share the single machine and must cooperate with one another to achieve a global goal of minimizing a linear function of the completion times of the jobs; e.g., total weighted completion times. It is assumed that neither the subproduction systems nor the shared machine have complete information about the entire system. Th