• Journal of Korean Port Research
• /
• v.14 no.1
• /
• pp.37-45
• /
• 2000

Centralized safety stock in a periodic replenishment system which consists of one central warehouse and m regional warehouse can reduce backorders allocating the centralized safety stocks to regional warehouse in a certain instant of each replenishment cycle. If the central warehouse can not monitoring inventories in the regional warehouse, then we have to predetermine the instant of allocation according to demand distribution and this instant must be same for all different replenishment cycle. However, transition of inventory level in each cycle need not to be same, and therefore different instant of the allocation may results reduced shortage compare to the predetermined instant of allocation. In this research, we construct a dynamic model based on the assumption of monitoring inventories in the regional warehouse everyday, and develop an algorithm minimize shortage in each replenishment cycle using dynamic programming approach.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 1999.10a
• /
• pp.83-90
• /
• 1999

Centralized safety stock in a periodic replenishment system which consists of one central warehouse and m regional warehouse can reduce backorders allocation the centralized safety stocks to regional warehouse in a certain instant of each replenishment cycle. If the central warehouse can not monitoring inventories in the regional warehouse, then we have to predetermine the instant of allocation according to demand distribution and this instant must be same for all different replenishment cycle. However, transition of inventory level in each cycle need not to be same, and therefore different instant of the allocation may results reduced shortage compare to the predetermined instant of allocation. In this research, we construct a dynamic model based on the assumption of monitoring inventories inventories in the regional warehouse everyday, and develop an algorithm minimize shortage in each replenishment cycle using dynamic programming approach.

• Industrial Engineering and Management Systems
• /
• v.12 no.3
• /
• pp.172-180
• /
• 2013

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
• /
• 2002.05a
• /
• pp.343-348
• /
• 2002

We are concerned with a long-term replenishment contract for the ARIMA demand process in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows us to contract future replenishments at a time with a price discount. Due to the larger forecast error of future demand, the buyer should keep a higher level of safety stock to provide the same level of service as the usual (s, Q) policy. However, the buyer can reduce his purchase cost by ordering a larger quantity at a discounted price. Hence, there exists a trade-off between the price discount and the inventory holding cost. For the ARIMA demand process, we present a model for the contract and an algorithm to find the number of the future replenishments. Numerical experiments show that the proposed algorithm is efficient and accurate.

• Journal of Korean Institute of Industrial Engineers
• /
• v.27 no.4
• /
• pp.345-351
• /
• 2001

We are concerned with a multiple replenishment contract with a purchase price discount in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy but allows contracting several firmed orders at a time with a price discount. Due to a larger forecast error of the future demand, the buyer should keep a higher level of safety stock to provide the same level of service of the usual (s, Q) policy but can reduce his purchase cost by placing larger quantity. Thus there exists a trade-off between the price discount and inventory holding cost. We present a model for the contract and an algorithm to find the optimum number of the firmed orders. Computer experiments show that the algorithm finds the global optimum solution very fast.

• International Journal of Industrial Entomology and Biomaterials
• /
• v.18 no.2
• /
• pp.69-75
• /
• 2009

Daba ecorace of Antheraea mylitta Drury (Lepidoptera: Saturniidae), the semi domesticated Indian tropical tasar silkworm being reared outdoor; the egg and silk yields are dependent of genotype environment interaction. The insufficient maintenance and multiplication of its P4 seed stock need a coherent as well as scientific strategy to safeguard breed potential, being commercially applied ecorace. The sort-out lines of P4 stock studied over five generations highlighting on commercial trait up gradation suits for a tropical crop season, revealed enhanced performance. The line with high pupal parents (T2) shown improved fecundity (12.9%) and the line with high shell parents (T3) recorded higher shell weight (40.0%) and silk ratio (24.1%). While, the line of high pupal female and high shell male (T4) reveal enhancement in fecundity (9.0%), egg hatching (14.1%), shell weight (50.0%), silk ratio (35.2%) and absolute silk yield (52.0%) indicating the need and role of varied basic seed stock lines. The approach could improve economically vital egg fecundity and cocoon shell weights besides balancing them in same line for commercial operation. The progressive show of lines (T1 to T4) along successive generations (G1 to G5), in spite of passing through seed crop (Jul-Aug) and commercial crop (Sep-Nov) seasons emphasize their compatibility. The study infers that the strategic plan of combining preferred parental phenotypes, methodical selection for desired commercial trait(s) through generations with best possible genotype environment interaction has enriched P4 stock with elevation in needy trait(s) besides assuring choice of suitable lines for seasons and regions and timely replenishment of basic seed of Daba ecorace.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2004.05a
• /
• pp.441-445
• /
• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2006.05a
• /
• pp.235-240
• /
• 2006

The existing inventory managements bear a relation to forecasting or assumptions. So these methods become more complicated and more expensive systems as time goes. This paper developed a practical inventory system which is called DCC(demand control chart). DCC does not 'forecast' but 'control' the trend of demand without assumptions. According to the trend of sales, DCC adjusts an order quantity considering the capacity of shelf in a store. Specially, DCC is a useful method under FRID system. Besides, this paper introduces EPFR(Every Period Full Replenishment) policy for reducing stocks.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.1
• /
• pp.141-150
• /
• 2014

Due to their accessibility and 24-hr availability, convenience stores are an integral part of daily lives. Because they sell a limited number of products and have small shelf space, shelf space allocation and inventory replenishment are important considerations for inventory management that critically affect profit. In this paper, we propose five solutions for the vendor-managed inventory problem of convenience stores that maximize profit while considering stock-out-based product substitutions. The performance of the proposed solutions is evaluated via simulation to reflect the demand uncertainty and marketing activity.

• International Journal of Advanced Culture Technology
• /
• v.3 no.2
• /
• pp.1-14
• /
• 2015

The purpose of this paper is to reveal two problems in the existing inventory systems in retail market, and to suggest a Two-Bin System under Automatic Ordering System considering only base-stock. Large retailers already have a sophisticated inventory system based on an automatic ordering principle. However, why does the out-of-stock (OOS) happen in large discount stores in spite of having a good inventory system? This paper suggests two systems after finding the root causes concerning the previous question. For evaluating the performance of each system, the random 200 data set in each sample group was generated from MINITAB 16 and obeyed the Poisson distribution. The existing inventory system in retail market cannot help generating OOS due to indwelling contradiction in itself. The reasons are the ordering deadline and the relationship between ordering quantity and base stock. This paper also demonstrates that these previous studies on inventory fall into the closed loop. Also the paper shows that the performance of the replenishment policy was better than traditional methods dealing with ordering quantity and base stock.