• 산업공학
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• 제18권1호
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• pp.63-72
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• 2005

In this paper, we present a distribution planning method for a supply chain. Like a typical distribution network of manufacturing firms, we have the form of arborescence. To consider more realistic situation, we investigated that an outside supplier has limited capacity. The customer demands are given in deterministic form in finite number of discrete time periods. In this environment, we attempt to minimize the total costs, which is the sum of inventory holding and backorder costs over the distribution network during the planning horizon. To make the best of the restricted capacity, we propose the look-ahead feature. For looking ahead, we convert this problem into a single machine scheduling problem and utilize tabu search approach to solve it. Numerous simulation tests have shown that the proposed algorithm performs quite well.

• 산업경영시스템학회지
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• 제21권48호
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• pp.15-23
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• 1998

This paper concerns the problem of determining the spare inventory level for multi-echelon repairable-item inventory system with finite number of operating items. We consider the system which has several bases and a central depot. When an item fails, it is dispatched to a repair facility and, a spare, if available, is plugged in immediately. When the failed item is repaired, it is sent to the base and either is used to fill a backorder or is stored at a spare inventory point. Using queueing network, we develop an algorithm to find the spare inventory level which minimizes the total expected cost and, simultaneously, satisfies a specified minimum fill rate. The results of the algorithm clearly indicate that the algorithm successfully generate output with optimal solution.

• 산업경영시스템학회지
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• 제17권31호
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• pp.59-72
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• 1994

This paper deals with ordering policies of consumable goods which have large demand rates in a multi-level distribution system. The system we are concerned consists of one Central Distribution Center(CDC) and N non-identical Regional Distribution Centers(RDCs) which have different demand rates, minimum fillrates, leadtimes, etc. The customer demand on the RDC is stationary poisson and the RDCs demand on the CDC is superposition of Q-stage Erlang distributions. We approximate the RDCs and CDC demand distribution to nomal in order to enhance the efficiency of algorithm. The relevant costs include a fixed ordering cost and inventory holding cost, and backorder cost. The objective is to find a continuous-review ordering policy that minimizes the expected average costs under constraints of minimum fill rates of RDCs and maximum allowable mean delay of CDC. We developed an algorithm for determining the optimal ordering policies of the CDC and the RDCs. We verified and compared the performance of the algorithm through the simulation using the algorithm result as the input parameters.

• 산업경영시스템학회지
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• 제19권37호
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• pp.31-40
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• 1996

The amount of safety stock is decided from various information such as the forecasted demand, the lead time, the size of the order quantity and the desired service level. There are two cases to consider the problem of setting safety stock when both the demand in a period and the lead time are characterized as random variables: the first case is the parameters of the demand and lead time distributions are known, the second case is they are unknown and must be estimated. The objective of this study is to present the procedure for setting safety stocks in the case the parameters of the demand and lead time distributions are unknown and must be estimated. In this study, a simple exponential smoothing model is used. to generate the estimates of demand in each period and a discrete distribution of the lead time is developed from historical data, and the optimal service level is used which determined to consider both of a backorder and lost sale.

• 산업경영시스템학회지
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• 제13권21호
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• pp.101-110
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• 1990

This study presents a two-level inventory distribution system with one-upper level facility(warehouse) in first echelon and n-lower level facilities(stores) in the second echelon. The demand process at the upper level is induced by the aggregated backorder processes of n independently operated lower level facilities in parallel. For the upper level and the lower level we find the decision points and the distribution function of the aggregate backorders from n-stores. Optimal allocation units and expected system profits are obtained. If the product is of great enough importance to the customer, echelon structure will include a shadow installation. Also, this case is analyzed. Numerical examples are presented to illustrate the results for each case.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.566-569
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• 2004

본 연구에서는 생산능력의 제한이 있는 다계층(multi-echelon) 공급사슬에서의 효과적인 분배계획수립을 목적으로 한다. 수요 정보는 계획 기간(planning horizon) 동안 확정적인 형태로 주어지고 이러한 상황 하에서 전체 공급사슬에서 발생하는 재고유지 비용(holding cost)과 재고이월 비용(backorder cost)의 합을 최소화하는 분배 문제의 해법을 제시하고자 한다. 본 연구에서는 총비용을 최소화하는 각 노드별 분배량을 결정하기 위해서 look-ahead 기법을 사용한다. Look-ahead 기법을 통해 분배 계획 수립 시 해당 기간의 수요 정보뿐만 아니라 미래 수요를 함께 반영함으로써 생산능력 제한에 효과적으로 대처할 수 있도록 하였다. Look-ahead 기법을 적용하기 위해 본 문제를 단일 기계 일정계획(single machine scheduling) 문제로 전환하고 이에 대한 효율적인 발견적 기법을 제시한다. 또한 제안된 발견적 기법의 성능을 평가하기 위해 다양한 실험 조건하에서 제안된 알고리듬과 기존의 미래 수요를 고려하지 않는 계층 재고 정책(echelon stock policy)의 결과를 비교하여 본 연구의 우수성을 입증하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2004년도 추계학술대회 및 정기총회
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• pp.578-581
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• 2004

본 연구에서는 수요가 정규분포의 형태를 따르는 N차 시리얼(serial) 시스템을 대상으로 한다. 최상위 노드는 상위의 공급자로부터 받고자 하는 물량을 제한 없이 받을 수 있으며 하위 노드로 이러한 물량을 공급하게 된다. 최하위 노드에서는 고객의 직접적인 수요가 발생하고 만족시키지 못한 수요는 다음 기간으로 이월된다. 이러한 환경 하에서 전체시스템에서 발생하는 재고유지 비용(holding cost)과 재고이월 비용(backorder cost)의 합을 최소화하는 각 노드별 최적의 기초 재고 수준(base stock level)을 결정하는 문제를 다룬다. 본 논문에서는 모의실험과 계층 재고(echelon stock)의 개념을 통해 수요 분포 내의 적절한 분위수(quantile)를 결정하는 접근방법으로 각 노드의 기초 재고 수준을 구하는 방안을 제시하고자 한다.

• 대한안전경영과학회지
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• 제11권3호
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• pp.139-148
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• 2009

In a real-life supply chain environment, demand forecasting is usually represented by probabilistic distributions due to the uncertainty inherent in customer demands. However, the customer demand used for an actual supply chain planning is a single deterministic value for each of periods. In this paper we study the choice of single demand value among of the given customer demand distribution for a period to be used in the supply chain planning. This paper considers distributed multi-echelon supply chain and the objective function of this paper is to minimize the total costs, that is the sum of holding and backorder costs over the distribution network under the service level constraint, by using demand selection scheme. Some useful findings are derived from various simulation-based experiments.

• 대한안전경영과학회지
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• 제8권5호
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• pp.139-150
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• 2006

The aim of this study is to establish an efficient distribution planning for a capacitated multi-stage supply chain. We assume that the demand information during planning horizon is given a deterministic form using a certain forecasting method. Under such a condition, we present a cost effective heuristic method for minimizing chain-wide supply chain inventory cost that is the sum of holding and backorder costs by using look-ahead technique. We cope with the capacity restriction constraints through look-ahead technique that considers not only the current demand information but also future demand information. To evaluate performance of the proposed heuristic method, we compared it with the extant research that utilizes echelon stock concept, under various supply chain settings.

• 대한안전경영과학회지
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• 제8권3호
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• pp.115-123
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• 2006

The main focus of this study is to investigate the performance of a clark-scarf type multi-echelon serial supply chain operating with a base-stock policy and to optimize the inventory levels in the supply chains so as to minimize the systemwide total inventory cost, comprising holding and backorder costs as all the nodes in the supply chain. The source of supply of raw materials to the most upstream node, namely supplier, is assumed to have an infinite raw material availability. Retailer faces random customer demand, which is assumed to be stationary and normally distributed. If the demand exceeds on-hand inventory, the excess demand is backlogged. Using the echelon stock and demand quantile concepts and an efficient simulation technique, we derive near optimal inventory policy. Additionally we discuss the derived results through the extensive experiments for different supply chain settings.