Seoul National University · Engineering
이 교수의 연구실은 공급망 관리, 생산 및 재고 최적화 분야에서 주로 활동하며, 특히 불확실한 수요와 제약 조건 하에서의 의사결정 문제에 초점을 맞추고 있습니다. 분포 자유(_distribution-free_) 접근법, 다기능 생산 시스템, 그리고 제약 조건이 있는 다항목 재고 모델링을 통해 현실적인 생산 및 공급망 설계 문제를 해결하는 데 기여하고 있습니다. 특히, 에너지 효율성과 탄소 배출을 고려한 생산 시스템 최적화 및 제조 공정의 신뢰성 향상에 대한 연구도 진행 중입니다.
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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
AbstractStochastic 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
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
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