Daiki Min, Seokgi Lee and Yuncheol Kang(2025), "Reinforcement learning model for optimizing bid price and service quality in crowdshipping", Systems, 13(6), 440
Jiyoung Oh and Daiki Min (2024), " Prediction of energy consumption for manufacturing small and midum-sized enterprises (SMEs) considering industry characteristics", Energy, 300, 131621
Hyungjun Park, Dong-Gu Choi and Daiki Min (2023), " Adaptive inventory replenishment using structured reinforcement learning by exploiting a policy structure", International Journal of Production Economics, 266, 109029.
N. Thoummala, Y. Kang, Daiki Min (2023), "A deep learning-baed approach to a newsvendor problem considering uncertainty and time-varying costs", Optimization Letters, 18, 1963-1974.
Jong-Hyun Ryu, Daiki Min and Dong-Gu Choi (2022), "Effective subsidy policy for a grid-connected microgrid: Evidence from a Korean case study", International Journal of Industrial Engineering:Theory, Applications and Practice, 29(3), 372-388.
Hyungjun Park, Daiki Min, Jong-Hyun Ryu and Dong-Gu Choi (2022), "DIP-QL: A novel reinforcement learning method for constrained industrial systems", IEEE Transactions on Industrial Informatics, 18(11), 7494-7503.
Hansung Kim, Dong Gu Choi and Daiki Min (2021), “ A sampling-based solution approach for electricity capacity expansion planning with chance constraint", Computers & Industrial Engineering, 162(12), 107710.
Eungab Kim and Daiki Min (2021), “ A two-stage hybrid manufacturing model with controllable make-to-order production rates”, Journal of Manufacturing, 60(1):676-691.
Daiki Min and Y. Kang (2021), “ A learning-based approach for dynamic freight brokerages with transfer and territory-based assignment”, Computers & Industrial Engineering, 153, 107042.
J. Kim and Daiki Min (2021), “A feature selection method for classifying highly similar text documents”, Industrial Engineering & Management Systems, 20(2):148-162.
Daiki Min, Jong-hyun Ryu and Dong Gu Choi (2020), “Effects of the move towards renewables on the power system reliability and flexibility in South Korea”, Energy Reports, 6, 406-417
Hana Moon and Daiki Min (2020), “A DEA approach for evaluating the relationship between energy efficiency and financial performance for energy-intensive firms in Korea”, Journal of Cleaner Production, 255, 120283
Jinah Yang, Daiki Min, Jeenyoung Kim (2020), “The use of big data and its effects in a diffusion forecasting model for Korean reverse mortgage subscribers”, Sustainability, 12, 979.
Daiki Min, H.S. Chon and H. Im (2019), “A DEA model for using qualitative data to rank options for adapting to climate change”, Industrial Engineering & Management Systems, 18(2):260-273.
Daiki Min, Jong-Hyun Ryu and Dong-Gu Choi (2018), “A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies”, Computers & Operations Research, 96:244-255.
J.H. Ryu, Daiki Min and D.G. Choi (2018), “Economic value assessment and optimal sizing of an energy storage system in a grid-connected wind farm”, Energies, 11(3).
Engab Kim and Daiki Min (2018), “Designing an optimal inventory replenishment strategy in a combined MTS-MTO supply chain”, International Journal of Industrial Engineering-Theory Applications and Practice, 25(5):620-633.
Daiki Min and Kwanghun Chung (2017), “A joint optimal decision on shipment size and carbon reduction under direct shipment and peddling distribution strategies”, Sustainability, 9
Hyung-Suk Choi and Daiki Min (2017), “Efficiency of well-diversified portfolios: Evidence from data envelopment analysis”, Omega, 73:104-113.
Hana Moon and Daiki Min (2017), “Assessing energy efficiency and the related policy implications for energy-intensive firms in Korea: DEA approach”, Energy, 133:23-34.
Daiki Min (2014), “Heuristic procedures for a stochastic batch service problem”, A Quarterly Journal of Operations Research, 12:157-174.
Daiki Min and Yuehwern Yih (2014), “Managing a patient waiting list with time-dependent priority and adverse events”, RAIRO-Operations Research, 48(1):53-74.
Kwanghun Chung and Daiki Min (2014), “Staffing a service system with appointment-based customer arrivals”, Journal of the Operational Research Society, 65(10):1533-1543.
Daiki Min and Jaewoo Chung (2013), “Evaluation of the long-term power generation mix: The case of South Korea's energy policy”, Energy Policy, 62:1544-1552.
Sangbok Lee, Daiki Min, Jonghyun Ryu, Yuehwern Yih (2013), “A Simulation Study of Appointment Scheduling in Outpatient Clinics: Open Access and Overbooking”, Simulation: Transactions of the Society for Modeling and Simulation International, 89(12):1459-1473.
Daiki Min and Yuehwern Yih (2011), “Fuzzy Logic-based approach to RFID data management in an outpatient clinic environment”, Journal of Medical Systems, 35(3):423-432.
Daiki Min and Yuehwern Yih (2010), “Scheduling elective surgery patients under uncertainty and downstream capacity constraints”, European Journal of Operational Research, 206(3):642-652.
Daiki Min and Yuehwern Yih (2010), “An elective surgery scheduling problem considering patient priority”, Computers and Operations Research, 37(6):1091-1099.
Daiki Min and Yuehwern Yih (2009), “A simulation study of registration queue disciplines in an outpatient clinic: a two-stage patient flow model”, European Journal of Industrial Engineering, 3(2):127-145.
Jiyoung Oh and Daiki Min (2025), "Optimizing of survey strategies for industrial sector energy consumption using dynamic programming approach", Korean Management Science Review, Korean Management Science Review, 42(1):75-83.
H. Lee and Daiki Min (2025), "A multi-objective reinforcement learning approach for developing tourism route recommendation system", The Journal of Society for e-Business Studies, 30(1):227-250.
J. Seo, D. Lee, B. Jung, M. Cho and Daiki Min (2024), "A dynamic pricing model for an online-selling parking permits - Reinforcement learning approach", Journal of the Korean Operations Research and Management Science Society, 49(3):1-14
*한국경영과학회 2024 우수논문상 수상, ** 한국경영학회 K-Management 혁신논문상 우수상 수상
S. Park and Daiki Min (2024), "A study on changes in tourism issues after covid-19 through news big data topic modeling analysis - tourism objects and tourism media perspecitves", Journal of Korea Service Managment Society, 24(4):132-162.
*한국서비스경영학회 2024 우수논문상 수상
H. Seo and Daiki Min (2024), "A hierarchical approach for predicting consumers' preference on wahsing mahcine types", The Journal of Society for e-Business Studies, 29(1):161-179.
Y. Kim and Daiki Min (2023), "A reinforcement learning for electric vehicle routing problem considering the waiting time at charging stations", Korean Management Science Review, 40(4):55-65.
J. Yang, D.G. Choi and Daiki Min (2023), "A conjoint analysis for evaluating customer preferences on vehicle attributes: Focusing on eco-friendly fuel vehicles", Journal of Tranport Research, 30(2):1-14.
N. Park, X. Lau and Daiki Min (2022), “The use of Q-learning method for a multi-period Newsvendor Problem with budget constraints”, Journal of the Korean Operations Research and Management Science Society, Accepted.
S. Paek and Daiki Min (2021), "Contents preference model combined with Matrix Factorization for movie recommendation", Journal of the Korean Institute of Industrial Engineers, 47(3):280-288.
S. Jang and Daiki Min (2020), "A study on the relationship between class similarity and the performance of hierarchical classification method in a text document classification problem", The Journal of Society for e-Business Studies, 25(3):77-93.
C. Jeong and Daiki Min (2020), "A study on the performance evaluation of machine learning for predicting the number of movie audiences", The Journal of Society for e-Business Studies, 25(2):49-63.
Y. Noh and Daiki Min (2020), "Prediction of short-term power consumption using LSTM-based two-step disaggregation methods", Korean Management Science Review, 37(1):75-88.
J. Hwang, Daiki Min and Alex J. Kim (2019), "Selection of effective keywords for online search advertising using attribute-based clustering analysis", Journal of the Korean Institute of Industrial Engineers, 45(5):451-464.