Ilkyeong Moon
Seoul National University · 工学
研究室紹介
Professor Ilkyeong Moon's research lab specializes in operations research and supply chain management, focusing on stochastic inventory systems, sustainable production planning, and optimization under uncertainty. The lab develops advanced mathematical models and solution methodologies—such as distribution-free approaches, geometric programming, and metaheuristics—for complex decision-making in supply chains. Key research directions include multi-item newsvendor models, economic production quantity models with carbon constraints, and integrated supply chain network design involving facility location, allocation, and vehicle routing. The lab emphasizes practical applicability through real-world case studies and computational validation of heuristic and exact algorithms.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15AbstractStochastic inventory models, such as continuous review models and periodic review models, require information on the lead time demand. However, information about the form of the probability distribution of the lead time demand is often limited in practice. We relax the assumption that the cumulative distribution function, say F, of the lead time demand is completely known and merely assume that the first two moments of F are known and finte. The minmax distribution free approach for the
Although important for production industries to reach fully sustainable manufacturing processes, those implementing production systems face challenges in reaching this reliability goal. In this direction, a production system is modelled through a basic economic-production paradigm under carbon emissions with a storage constraint and demand-dependent unit production cost. More reliable production houses produce fewer defective products than the unreliable production system. As the model contains
The purpose of this paper is to study the classic newsboy model with more realistic assumptions. First, we allow customers to balk when inventory level is low. Secondly, we relax the assumption that the cumulative distribution function of the demand is completely known and merely assume that the first two moments of the distribution function are known.
This paper deals with a multi-item newsvendor problem subject to a budget constraint on the total value of the replenishment quantities. Fixed costs for non-zero replenishments have been explicitly considered. Dynamic programming procedures are presented for two situations: (i) where the end item demand distributions are assumed known (illustrated for the case of normally distributed demand) and (ii) a distribution free approach where only the first two moments of the distributions are assumed k