• Management Science and Financial Engineering
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• v.16 no.3
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• pp.21-47
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• 2010

Quantity discounts provide a practical foundation for supply chain inventory policies, improving the supplier's profit and reducing the buyer's inventory cost simultaneously. Traditional quantity-discount research, which deals with inventory coordination between a buyer and a supplier, is extended to a stationary stochastic environment. This research shows that the magnitude of the optimal discounts scheduled by the deterministic quantity discount models may not be large enough to cover the buyer's additional inventory stocking risks under uncertain conditions. As a result, the buyer's total inventory cost may often increase rather than decrease. In contrast, the proposed model allows the supplier to identify the discount level, which shares the buyer's amplified risk associated with temporary overstocking and ensures that both buyer and supplier benefit economically. The performance of the proposed model was tested in the continuous review environments via numerical experiments. The experimental results support the proposed method as a feasible alternative in coordinating inventory decisions under stochastic demand.

• Korean Management Science Review
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• v.31 no.2
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• pp.1-13
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• 2014

In almost all of the organizations, the cost for acquiring and maintaining the inventory takes a considerable portion of the management budget, and thus a certain constraint is set upon the budget itself. The previous studies on inventory control for each item that aimed to improve the fill rate, backorder, and the expenditure on inventory are fitting for the commercially-operated SCM, but show some discrepancies when they are applied to the spare parts for repairing disabled systems. Therefore, many studies on systematic approach concept considering spare parts of various kinds simultaneously have been conducted to achieve effective performance for the inventory control at a lower cost, and primarily, METRIC series models can be named. However, the past studies were limited when dealing with the probability distributions for representing the situation on demand and transportation of the parts, with the (S-1, S) inventory control policy, and so on. To address these shortcomings, the Continuous Time Markov Chain (CTMC) model, which considers the phase-type distributions and the (s, Q) inventory control policies to best describe the real-world situations inclusively, is presented in this study. Additionally, by considering the cost versus the system availability, the optimization of the inventory level, based on this model, is also covered.

• Korean Management Science Review
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• v.28 no.1
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• pp.117-127
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• 2011

In the Multi-Echelon maintenance environment, METRIC(Multi-Echelon Technique for Repairable Item Control) has been used in several different inventory level selection models, such as MOD-METRIC, Vari-METRIC, and Dyna- ETRIC. While this model's logic is easy to be implemented, a critical assumption of infinite maintenance capacity would deteriorate actual values, especially Expected Back Order(EBO)s for each item. To improve the accuracy of EBO, we develop two models using simulation and queueing theory that calculates EBO considering finite capacity. The result of our numerical example shows that the expected backorder from our model is much closer to the true value than the one from Vari-METRIC. The queueing model is preferable to the simulation model regarding the computational time.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.323-330
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• 2014

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.117-124
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• 1994

In this paper an inventory model is presented for determining the ordering schedule in which the demand rate is changing linearly with time and the decay is assumed to be a constant rate of the on-hand inventory. An easy to use heuristic is developed to find the times and sizes of replenishments so as to keep the total of ordering, inventory carrying and deteriorating costs as low as possible. Solutions of the model to test problems show that our heuristic model outperforms other existing models in the literature without sacrificing the computational complexity. When there is no deterioration, the model developed is related to the corresponding model of nondeteriorating items.

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.92-98
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• 1988

In this paper a model is developed for an inventory system in which the number of units of acceptable quality in a replenishment lot is uncertain and the demand. during the stockout period is back ordered and. also under the same condition an inventory model with experdited stockout is developed. It is assumed that the fraction of the acceptable quality in a replenishment lot is a random variable whose probability distribution is known. The optimal replenishment policy is synthesized for such a system. A numerical example is used to illustrate the theory.

• The Journal of Fisheries Business Administration
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• v.25 no.2
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• pp.103-114
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• 1994

We consider the continous, deterministic, infinite horiton, perishable item inventory, within the setting of a retail sector, in which the price for an item is dependent on the lifetime of inventory. Replenishment cost is kept constant but the carrying cost per units is allowed to vary according to product lifetime. Tro possibilities of variation are considered : (1) Product lifetime is longer than cycletime and (2) Product lifetime is shorter than cycletime. We find the optimal policies and decision rules for perishable product.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.386-397
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• 2008

Due to the rapid advancement of technologies, a growing number of innovative products with a short life-cycle have been introduced to the market. As the life-cycles of such products are shorter than those of durable goods, the demand variation during the life-cycle adds to the difficulty of inventory management. Traditional inventory planning models and techniques mostly deal with products that have long life-cycles. The assumptions on the demand pattern and subsequent solution approaches are generally, not suitable for dealing with products with short life-cycles. In this research, inventory replenishment problems based on the logistic demand model are formulated and solved to facilitate the management of products with short life-cycles. An extended Wagner- Whitin approach is used to determine the replenishment cycle, schedules and lot-sizes.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.65-78
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• 2011

In the continuous review (Q, r) model one continuously monitors inventory level and places a replenishment order when the inventory position reaches the reorder point. In many business practices, however, inventory decreases in a discrete fashion. As a result, replenishment orders are usually placed after the inventory position gets far below the reorder point. This makes a chance of shortage more likely and the service level lower than designed. Such a discrepancy can be compensated for by raising the reorder point to some extent. The question is how much the reorder point should be raised in order to compensate for a potential shortage. In this study, we present experimental analyses for this question. Regression models are also proposed for on-site use.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.354-363
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• 2016

Due to uncertain environment, various parameters such as price, queuing length, warranty, and so on influence on inventory models. In this paper, an inventory-queuing-pricing problem with continuous review inventory control policy and batch arrival queuing approach, is presented. To best of our knowledge, (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations, a fuzzy programming approach is applied. Therefore, the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems, Pareto-based approaches based on non-dominated ranking and sorting genetic algorithm are proposed and justified to solve the model. Several numerical illustrations are generated to demonstrate the model validity and algorithms performance. The results showed the applicability and robustness of the proposed soft-computing-based approaches to analyze the problem.