Lee, Soon-Kyo (이순교)

Yuh, Jun-Sang (유준상)

Kang, Seung-Ho (강성호)

Kim, Eun-Bi (김은비)

Chung, Seokhyun (정석현)

Moon, Joon (문준)

Chun, Joo-Young (천주영)

Lee, Yong-Ho (이용호)

Lee, Chan-Ho (이찬호)

Kang, Woong (강웅)

Kim, David (김다윗)

Jung, Yong-Hui (정용희)

Shin, Hyo-Eun (신효은)

Yoon, Ju-Young (윤주영)

Kim, Dong-Soo (김동수)

Choi, Mu-Sun (최무선)

Park, Kun-Woo (박건우)

Park, Joong-Hoo (박중후)

Ahn, Hyun-Soo (안현수)

Lee, Jung-hwa (이정화)

Lee, Hyun-Woo (이현우)

Huh, Ye-Kwon (허예권)

Lee, Yea-Jin (이예진)

Park, Hyeong-Min (박형민)

Kim, Dong-Gyu (김동규)

Kwon, San (권산)

Kim, Tae Young (김태영)

공급사슬 및 가치망 분석 연구실
에서는
열정과 의지를 가진 구성원들이 연구에 매진하고 있습니다. Supply chain & VAlue Network Analytics (SaVAnNA) Lab [School of Industrial Management Engineering Korea University #209 Colleage of New Engineering Building Anam-Dong, Seongbuk-Gu Seoul 136-713, South Korea]

공급사슬 및 가치망 분석 연구실
에서는
열정과 의지를 가진 구성원들이 연구에 매진하고 있습니다.

VR 노출 치료 중 생체신호 분석을 통한 사회불안장애 예측: 설문 조사 이전 사회불안 장애의 심각도 및 VR 부작용 예측

시계열 데이터 이미지 변환을 활용한 전압 기반 Arc Fault 탐지 모델

Parallel machine scheduling problem with additional resource inputs in shipbuilding industry

An exact A*-based tree search algorithm for TSP with sequence-and-load dependent risk

Drone-truck hybrid delivery model considering returns

Dynamic-persistent csma: A reinforcement learning approach for multi-user channel access

Prediction of specific anxiety symptoms and virtual reality sickness using in situ autonomic physiological signals during virtual reality treatment in patients with …

Benefit allocations of the strategic alliance in a logistics industry

Driving-Pattern-Based Ensemble Clustering for SOC Prediction in Battery-Swapping Electric Two-Wheeled Vehicles

Load-reliant traveling salesman problem for hazardous material delivery

Unmanned aerial vehicle and autonomous delivery robot station for last-mile delivery services

FAR applied

Collaborative traveling salesman problem with ground vehicle as a charger for unmanned aerial vehicle

Multi-Period Multi-Depot Vehicle Routing Problem for Mobile Center-based Last Mile Delivery

Extracting Supply Chain Information From News Articles Using Large Language Models: A Fully Automatic Approach

Unmanned aerial vehicle last-mile delivery considering backhauls

FDN: A real-time ensemble fire detection network

Smart Battery Swapping Control for an Electric Motorcycle Fleet With Peak Time Based on Deep Reinforcement Learning

Effects of yield and lead-time uncertainty on retailer-managed and vendor-managed inventory management

Life Cycle Cost Analysis of Alternative Marine Fuels Under Carbon Price Uncertainty

건물 에너지 효율 향상을 위한 심층 강화학습 기반 HVAC 제어

Solving the dynamic vehicle routing problem under traffic congestion

제철 부산물을 활용한 고함철단광 제조의 혼합공정 최적화 연구

The value of supply chain coordination under moral hazard: A case study of the consumer product supply chain

재난 상황에서 다중 드론을 활용한 수송 문제의 혼합 정수 계획 모형

빅 데이터에 근거한 강의실 자동 배정 알고리즘 설계와 학사관리 시스템 적용사례

제조데이터 기반 머신비전 불량 판정 인공지능

Practical EV and E–drone routing problem: Recharging E-drone battery using EV

불확실한 수요에 따른 유통 소요 계획 개선을 위한 시나리오 기반의 다중 강건 최적화

지속가능한 교통체계를 위한 강화학습 기반의 지능형 교통 신호 제어 연구

공급사슬 상에서 공급 불확실성이 소매자주도 및 공급자주도 재고관리방안에 미치는 영향

Solving the Dynamic Vehicle Routing Problem Under Traffic Congestion

Intelligent traffic control for autonomous vehicle systems based on machine learning

Impact of supply chain power and drop-shipping on a manufacturer’s optimal distribution channel strategy

Quality improvement incentive strategies in a supply chain

Fault Detection and Classification in Plasma Etch Equipment for Semiconductor Manufacturing e-Diagnostics

Dynamic Traveling Salesman Problem: Value of Real-Time Traffic Information

A Minimax Distribution-free Procedure for a Newsvendor Problem with Free Shipping

Modeling the resilience of interdependent networks: The role of function dependency in metro and bus systems

A mathematical programming approach for integrated multiple linear regression subset selection and validation

Routing for an on-demand logistics service

In-transit perishable product inspection

Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation

Locating battery swapping stations for a smart e-bus system

Strategic Alliance with Competitors in the Electric Vehicle Market: Tesla Motor’s Case

REMS and RBPTS: ALE-compliant RFID middleware software platform

The value of information on supply risk under random yields

Study of RFID middleware framework for ubiquitous computing environment

Effect of Inventory Information Discrepancy in a Drop-Shipping Supply Chain

Inventory model for sustainable operations of a closed-loop supply chain: Role of a third-party refurbisher

SangHwa Song

Yew-Soon Ong

Young Woong Park

Mark Goh

CHEONG, Taesu

This paper proposes a dynamic vehicle routing problem (DVRP) model with nonstationary stochastic travel times under traffic congestion. Depending on the traffic conditions, the travel time between two nodes, particularly in a city, may not be proportional to distance and changes both dynamically and stochastically over time. Considering this environment, we propose a Markov decision process model to solve this problem and adopt a rollout-based approach to the solution, using approximate dynamic programming to avoid the curse of dimensionality. We also investigate how to estimate the probability distribution of travel times of arcs which, reflecting reality, are considered to consist of multiple road segments. Experiments are conducted using a real-world problem faced by Singapore logistics/delivery company and authentic road traffic information.

This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We …

With the expansion of the Internet and the proliferation of e-businesses, many manufacturers have chosen to distribute products through online retail channels in addition to brick-and-mortar retail channels. In this paper, we consider a dual-channel supply chain in which a manufacturer considers selling a product through a conventional retail channel and an online channel. Considering two common procurement and order fulfillment policies for online retailers (etailers), conventional batch ordering and drop-shipping, we investigate the impact of supply chain power structure in terms of market power and retail channel dominance on a manufacturer’s optimal distribution channel strategy. We analyze and compare the two procurement models with respect to the etailer’s order fulfillment policies. We find that a manufacturer never prefers a drop-shipping etailer as the first mover in a sequential pricing game with a batch …

This paper investigates several incentive mechanisms for collaborative product quality improvement in a buyer-driven supply chain, and the impacts of those mechanisms on supply chain performance. The buyer, the Stackelberg leader, determines the sales price of a product while the supplier is responsible for production and product quality determination. We develop analytical models incorporating two reward schemes to better understand how the buyer can facilitate the supplier’s quality improvement efforts. We offer managerial insights and practical guidelines for implementing quality management in the supply chain, derived from both an analytical comparison and numerical experiments.

A method of fault detection and classification (FDC) for semiconductor manufacturing equipment -diagnostics using equipment data is presented. Detecting faulty processes, identifying any anomaly at their onsets, and rapidly classifying the root cause of the fault are crucial for maximizing equipment utilization in current semiconductor manufacturing; however, tool data acquired from production equipment contains much information that is often challenging to analyze due to its sheer volume and complexity. In this paper, modular neural network (MNN) modeling is presented as a method for fault detection modeling in plasma etching. Based on the result from the MNN modeling, a tool data set is grouped according to its related subsystems, and FDC is performed using Dempster-Shafer (D-S) theory to address the uncertainty associated with fault diagnosis. Subsystem level fault detections, such as radio frequency …

We investigate the value of choosing the next stop to visit in a multistop trip based on current traffic conditions to minimize the expected total travel time of the tour. We model this problem as a Markov decision process. Numerical examples are presented that indicate that the value of using current traffic information for dynamic tour determination can be significant.

In this paper, we study the optimal policies of retailers who operate their inventory with a single period model (i.e., newsvendor model) under a free shipping offer where a fixed shipping fee is exempted if an order quantity is greater than or equal to a given minimum quantity. Zhou et al. (2009) have explored this model, and we further investigate their analysis for the optimal ordering policies which they did not sufficiently develop. Based on the investigation, we extend the base model in order to deal with the practically important aspect of inventory management when the exact distribution function of demand is not available. We incorporate the aspect into the base model and present the optimal policies for the extended model with a numerical example. Finally, we conduct extensive numerical experiments to evaluate the performance of the extended model and analyze the impacts of minimum free shipping quantity …

Owing to the pervasive interdependency among networks and the great threat of various disasters, one of the most challenging issues is the resilience evaluation of interdependent networks. Existing studies have been conducted to analyze interdependent network resilience addressing unidirectional dependency, which triggers and propagates network failures. The loss and recovery of network functionality are complicated and important under such interdependency. Ignoring the interdependency nature of different networks would lead to incomplete or incorrect results in their resilience assessment. This paper developed a resilience assessment model for interdependent transit networks under failures. Taking the interdependency relations, network topology, flow characteristics, and demand distribution into account, the proposed methodology explicitly quantifies the impacts of varying network interdependency on …

Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is built, various statistical tests and diagnostics are conducted to validate the model and to determine whether the regression assumptions are met. Most traditional approaches require human decisions at this step. For example, the user may repeat adding or removing a variable until a satisfactory model is obtained. However, this trial-and-error strategy cannot guarantee that a subset that minimizes the errors while satisfying all regression assumptions will be found. In this paper, we propose a fully automated model building procedure for multiple linear regression subset selection that integrates model building and validation based on mathematical programming. The proposed model minimizes mean squared errors while ensuring that the …

In this paper, we examine an optimization problem for a shipping company that provides an on-demand delivery service in which its customers visit a specified location called a delivery center (e.g., a convenience store or Amazon locker) to pick up their items themselves, rather than delivering the products purchased online to the customers directly. In this problem, we select delivery centers that are accessible for all customers and determine the optimal delivery route that minimizes the costs proportional to the distance traveled by the delivery vehicle, the delivery time, and costs. This is an extension of the traveling salesman problem with a time window constraint, which simultaneously determines the locations of the delivery centers for customer pickup and the delivery route. To solve this problem and to improve managerial practice, we propose a mathematical programming model and an efficient two-phase …

We determine the value of monitoring perishable freight in-transit for a single vehicle traveling from an origin to a destination. We develop a computationally practical approach for determining the optimal expected cost function and an optimal policy, based on an infinite horizon partially observed Markov decision process model. Structural properties of the optimal expected cost function and optimal policy are determined. These results can lend insight when deciding whether to acquire the capacity to monitor freight status in transit and what actions to take, based on the data from the in-transit monitoring, that optimally increase expected supply chain productivity.

Utilizing unmanned aerial vehicles for delivery service has been drawing attention in the logistics industry. Since commercial unmanned aerial vehicles have fundamental limitations on payloads and battery capacities, hybrid ground vehicle and unmanned aerial vehicle models have been actively investigated as practical solutions. However, these studies have focused on linehaul (delivery) demands, excluding a large number of backhaul (pickup) demands. If we consider both demands at the same time, an empty unmanned aerial vehicle that finished linehaul service can be immediately used to serve a backhaul customer. In this study, we investigate the differences that arise by considering backhauls as an additional element of the routing problem. A mixed integer linear programming model is developed, and a heuristic is constructed to solve large-scale problems. To demonstrate the effectiveness of our model …

Microgrids can effectively integrate renewable energy sources (RESs) and provide power for local customers. However, uncertainties of RESs and loads pose challenges to microgrid operation. The traditional point optimization method is unrealistic, and the widely used stochastic optimization (SO) method is time-consuming. Besides, battery energy storage systems (BESSs) are critical dispatchable devices to alleviate adverse effects of uncertainty, so an accurate nonlinear degradation cost model of BESSs should also be proposed. To handle such problems, the paper proposes an affine–arithmetic (AA)-based microgrid interval optimization (IO) method considering uncertainty and BESS degradation. First, the AA theory is introduced to model the RES and load variation ranges as intervals and calculate the interval uncertainty. Then, a nonlinear BESS degradation cost model is proposed, which can assess battery …

With the growing interest and popularity of electric vehicles (EVs), the electrification of buses has been progressing recently. To achieve the seamless operation of electric buses (e-Buses) for public transportation, some bus stations should play the role of battery swapping station due to the limited travel range of e-Buses. In this study, we consider the problem of locating battery swapping stations for e-Buses on a passenger bus traffic network. For this purpose, we propose three integer programming models (set-covering-based model, flow-based model and path-based model) to model the problem of minimizing the number of stations needed. The models are applied and tested on the current bus routes in the Seoul metropolitan area of South Korea.

We investigate how the choice of coopetition of the simultaneous pursuit of collaboration and competition dynamically impacts both the participating firms and also the other self‐developing ones in the same market. A conceptual framework of mathematical models obtained from the arguments and insights in the literature is used to undertake an in‐depth study through a multiperiod analysis from 2013 to 2020 of an exemplar case of coopetition, the two concurrently ongoing coopetition partnerships in the US electric vehicle (EV) market, the Tesla Motors‐Daimler AG alliance and the Tesla Motors‐Toyota alliance and the other firms which are not involved in coopetition.

Radio Frequency Identification (RFID) middleware is a new breed of software system which facilitates data communication between automatic identification equipments like RFID readers and enterprise applications. It provides a distributed environment to process the data coming from tags, filter and then deliver it to a variety of backend applications via various communication protocols including web services. In this paper, we demonstrate the implementation of the RFID middleware system that is not only compatible with EPCglobal ALE (Application Level Events) specification but also provides various types of RFID readers spanning from passive readers to active readers. Overall functional components of the middleware software as well as the system framework are illustrated.

For companies facing challenge of managing unreliable supply sources, one of the operational goals is to increase the overall profit by improving performance of uncertain supply. We here develop stochastic decision frameworks which evaluate necessary information about uncertainties and help newsvendor and suppliers to enhance supply reliability. We first analyze the newsvendor’s purchasing decision under supply uncertainty and the derived decision framework is used to identify stochastic dominance conditions. We found that partial supply risk information is sufficient to determine regular ordering quantity, but to improve the overall profit, it is important to gather more information about stochastic dominance conditions.

`Ubiquitous Computing' was appeared first time by Mark Weiser in 1988. Recently, as basic technologies for realization are offered and technical necessity is appeared, various researches are progressed about this. Various forms of technologies are needed to satisfy ubiquitous computing environment, such as home networking, telematics, industry automation and so on. Particularly, in the industry automation field that utilizes RFID technology, there have been many researches on the automation of supply chain management, control of goods in stock, physical distribution management, warehouse management, etc. We report about current research contents about RFID middleware framework which is necessary for RFID application development in ubiquitous environments. Then we analyze this and propose to RFID middleware framework's development direction that is used in ubiquitous computing environment …

This article investigates the impact of information discrepancy between a drop‐shipper and an online retailer on the drop‐shipping supply chain performance. The inventory information misalignment between them contributes to the failure of order fulfillment and demand satisfaction, and hence the associated penalties are incurred. In this article, we first analyze the penalties of ignoring such information discrepancy on both the drop‐shipper and the online retailer. We then assess the impact of information discrepancy on both parties when the drop‐shipper understands the existence of the information discrepancy but is not able to eliminate the errors. The numerical experiments indicate that both parties can have significant amount of the percentage cost reductions if the information discrepancy can be eliminated, and the potential savings are substantial especially when the errors have large variability. Furthermore …

This paper investigates the joint decision on inventory and refurbishing strategies in a closed-loop supply chain, consisting of a manufacturer responsible for the production and first-market sales and a third-party refurbisher responsible for the refurbishment and second market. Based on a game-theoretic model, we investigate the effects of supply chain structures on the production operations, sales price of a refurbished product, profits and environmental performance. Through numerical analyses, we reveal that the integrated system yields higher first-market sales and a larger production lot size, while the decentralized one shows higher second-market sales and a larger refurbishing lot. Interestingly, the integrated system sets the higher price for the refurbished item due to channel conflict issues. Efforts to increase the second-market sales can harm the overall supply chain profit by leading to the decrease in the …

SaVAnNA Lab

교수님 소개

정태수

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Multi-Period Multi-Depot Vehicle Routing Problem for Mobile Center-based Last Mile Delivery

Parallel machine scheduling problem with additional resource inputs in shipbuilding industry

Driving-Pattern-Based Ensemble Clustering for SOC Prediction in Battery-Swapping Electric Two-Wheeled Vehicles

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

정태수

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

Multi-Period Multi-Depot Vehicle Routing Problem for Mobile Center-based Last Mile Delivery

Parallel machine scheduling problem with additional resource inputs in shipbuilding industry

Driving-Pattern-Based Ensemble Clustering for SOC Prediction in Battery-Swapping Electric Two-Wheeled Vehicles

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집