• Journal of Korean Society for Quality Management
• /
• v.13 no.1
• /
• pp.56-64
• /
• 1985

This paper presents a general algorithm of multi-item continuous review models to obtain simultaneous solutions for ordering quantities and reorder points for each item in an inventory, while satisfying constraints on average inventory investment and reordering workload. Two models are formulated'in each model the heuristic method is utilized, and the partial back-logging is considered. In the first model, the objective function is the minimization of total inventory variable cost. In the second model, the objective function is the minimization of total time-weighted shortages, and the ordering, holding, and stockout costs in this model are independent each other. A numerical example is also solved to present application of each model.

• Journal of the Korean Society of Systems Engineering
• /
• v.12 no.2
• /
• pp.47-57
• /
• 2016

As the inventory costs of repairable items in military logistics continue to increase, many studies for optimal inventory level of these items are being carried out in advanced countries, including the US, to reduce these costs. Research on inventory level optimization for repairable items aimed to achieve the availability goal of a system with a MIME(Multi Indenture Multi Echelon) repair policy structure first began with Sherbrooke's METRIC and developed into various types. This research is to analyze and compare recent V-METRIC related studies to search for another variation in this field. This paper mainly looks at how to determine optimum inventory level for each repairable item to achieve a specific availability target within a limited budget, and also how to minimize inventory cost while achieving its availability target by determining optimal inventory level of each repairable item.

• Journal of Korean Institute of Industrial Engineers
• /
• v.34 no.4
• /
• pp.373-385
• /
• 2008

This paper presents an overview of inventory management. This includes a categorization, by a number of dimensions, of inventory problems and associated models. Relevant literature references are provided within the dimensions. The paper points out the continuing gap between theory and practice, followed by a number of suggested research topics to help bridge the gap.

• Journal of applied mathematics & informatics
• /
• v.5 no.1
• /
• pp.253-261
• /
• 1998

(s,S) inventrory system of a perishable item with positive lead times and finite backlogs under N-policy Under this policy as and when the inventory level drops to s-N during a lead time local purchase is made. Three models are con-sidered. The limiting distribution on the inventory level is obtained and an associated cost analysis is made. Results ae numerically illustrated.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.11 no.17
• /
• pp.9-14
• /
• 1988

A (s, S) inventory policy is studied for a continuous inventory model in which lead times are dependent on the ordering quantity. The model assumes that at most one order is outstanding and demands occur in a compound poison process. The steady-state probability distributions of the inventory levels are derived so as to determine the long-run expected average cost. And the computational procedure is presented.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.1 no.1
• /
• pp.151-170
• /
• 1976

There are many cases of production processes which intermittently produce several different kinds of products for stock through one set of physical facility. In this case, an important question is what size of production run should be prduced once we do set-up for a product in order to minimize the total cost, that is, the sum of the set-up, carrying, and stock-out costs. This problem is used to be called scheduling of multiple products through a single facility in the production management field. Despite the very common occurrence of this type of production process, no one has yet devised a method for determining the optimal production schedule. The purpose of this study is to develop quantitative analytical models which can be used practically and give us rational production schedules. The study is to show improved models with application to a can-manufacturing plant. In this thesis the economic production quantity (EPQ) model was used as a basic model to develop quantitative analytical models for this scheduling problem and two cases, one with stock-out cost, the other without stock-out cost, were taken into consideration. The first analytical model was developed for the scheduling of products through a single facility. In this model we calculate No, the optimal number of production runs per year, minimizing the total annual cost above all. Next we calculate No$_{i}$ is significantly different from No, some manipulation of the schedule can be made by trial and error in order to try to fit the product into the basic (No schedule either more or less frequently as dictated by) No$_{i}$, But this trial and error schedule is thought of inefficient. The second analytical model was developed by reinterpretation by reinterpretation of the calculating process of the economic production quantity model. In this model we obtained two relationships, one of which is the relationship between optimal number of set-ups for the ith item and optimal total number of set-ups, the other is the relationship between optimal average inventory investment for the ith item and optimal total average inventory investment. From these relationships we can determine how much average inventory investment per year would be required if a rational policy based on m No set-ups per year for m products were followed and, alternatively, how many set-ups per year would be required if a rational policy were followed which required an established total average inventory inventory investment. We also learned the relationship between the number of set-ups and the average inventory investment takes the form of a hyperbola. But, there is no reason to say that the first analytical model is superior to the second analytical model. It can be said that the first model is useful for a basic production schedule. On the other hand, the second model is efficient to get an improved production schedule, in a sense of reducing the total cost. Another merit of the second model is that, unlike the first model where we have to know all the inventory costs for each product, we can obtain an improved production schedule with unknown inventory costs. The application of these quantitative analytical models to PoHang can-manufacturing plants shows this point.int.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.38 no.2
• /
• pp.95-115
• /
• 2013

In traditional inventory models, purchase prices of raw materials are assumed to be fixed and have no effect on the optimal choice of inventory policies. However, when purchase prices fluctuate continuously over time, inventory costs are heavily affected by purchasing prices. Risk-averse inventory model decides order quantity and ordering time by considering not just purchase prices but also the risk from the discrepancy between estimated prices and realized prices. In this paper, we propose a myopic inventory policy which incorporates price risk into deciding ordering time and quantities. While the existing risk-averse model has no mechanism to reallocate inventories already purchased for a specific future period, the revised one reallocates initial inventories of each period to other future periods so that it can avoid purchasing raw materials at high prices. Experimental results demonstrate that the revised model outperforms the existing one in respect of total cost and variability.

• Journal of applied mathematics & informatics
• /
• v.8 no.2
• /
• pp.519-530
• /
• 2001

A two commodity continuous review inventory system with independent Poisson processes for the demands is considered in this paper. The maximum inventory level for the i-th commodity fixed as $S_i$(i = 1,2). The net inventory level at time t for the i-th commodity is denoted by $I_i(t),\;i\;=\;1,2$. If the total net inventory level $I(t)\;=\;I_1(t)+I_2(t)$ drops to a prefixed level s $[{\leq}\;\frac{({S_1}-2}{2}\;or\;\frac{({S_2}-2}{2}]$, an order will be placed for $(S_{i}-s)$ units of i-th commodity(i=1,2). The probability distribution for inventory level and mean reorders and shortage rates in the steady state are computed. Numerical illustrations of the results are also provided.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.3
• /
• pp.313-320
• /
• 2014

We consider the problem to find economic inventory quantity of a single commodity under stochastic demands and order cancellation. In contrast to the traditional economic production quantity (EPQ) model, we assume that once the amount of inventory reaches to a predetermined level of quantity then the production is not halted but its production speed decreases until the inventory level drops to zero. We establish two probabilistic models representing the behaviors of both the high-production period and low-production period, respectively, and derive the relationship between the level of inventory and costs of production, cancellation, and holding, from which the quantity of economic inventory is obtained.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2003.06a
• /
• pp.129-134
• /
• 2003

In this paper, we determine optimal reduction in the lead time and setup cost for some stochastic inventory models. And we propose more general model that allow the backorder rate as a control variable. We first assume that the lead time demand follows a normal distribution. And we assume that the backorder rate is dependent on the length of lead time through the amount of shortages. The stochastic models analyzed in this paper are the classical continuous and periodic review policy models with a mixture of backorders and lost sales. For each of these models, we provide a sufficient conditions for the uniqueness of the optimal operating policy. We also develop algorithms for solving these models and provide illustrative numerical examples.