• Journal of the military operations research society of Korea
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• v.11 no.2
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• pp.64-68
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• 1985

This paper considers the following case: (1) the product is paid by the right price for a lot accepted by a given consumer's acceptance sampling plan, and (2) the product is paid by the discounted price for a lot rejected by this plan. In such a case, the producer's sampling plan need not be the same as that of the consumer's. From the producer's view point, the producer need to determine the preliminary sample size which maximizes his revenue. This paper, therefore, determines an optimal preliminary sample size from the producer's view point. This preliminary sample size is affected by the consumer's acceptance sampling plan, percent defective, preliminary inspection cost and the discount rate of the price.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.95-115
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• 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 the Korean Operations Research and Management Science Society
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• v.30 no.2
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• pp.105-115
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• 2005

By committing to a long-term replenishment contract, buyers can purchase a product at a lower price from a supplier who is less pressured to find new customers due to the long-term contract and can charge a discounted price. We develop an analytical model from the buyer's perspective to investigate a capacity reservation contract. We are considering the system with a single supplier and a buyer. The buyer can purchases any desired amount from a spot market at a higher price in addition to the contracted amount. For such a system, we propose an algorithm to derive the optimal contract terms. The result of computational experiments shows that the algorithm finds the global optimum solution in a resonable amount of time.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.43-48
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• 1986

In this paper, the one period multi-item inventory model is considered in which it is required to determine the production quantity and selling price of each item which maximize the probability of realizing predetermined level of profit. The objective function of this model is the sum of weighted probabilities which represent the possibility of obtaining the predetermined level of profit for each item. Budget constraint, inventory site constraint and constraints of price are considered. Finally this paper shows a numerical example in which random demand of each item has exponential distribution.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.1-16
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• 2016

Yield management, which originated from the U.S. service industry, uses pricing techniques and information systems to make demand management decisions. Demand uncertainty is an important factor in the area of demand management. A key strategy to reduce the effects of demand uncertainty is substitution. The most generally known type of substitution is inventory-driven substitution, in which consumers substitute an out-of-stock product by buying a similar or other type of product. Another type of substitution is the price-driven substitution, which occurs as a result of price changes. In this research, we consider two market segments that have unique perishable products. We develop yield management optimization models with stochastic demand based on the newsvendor model where inventory-driven and price-driven substitutions are allowed between products in the two market segments. The most significant contribution of this research is that it develops analytical procedures to determine optimal solutions and considers both types of substitution. We also provide detailed theoretical analysis and numerical examples.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.567_568
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• 2009

In the past, pumping-storage generator pumped the water from the lower part storage to the upper part storage during the base load periods. And, it generated during the peak load or when other generator was outage. However, pumping-storage generator will operate different ways in the competitive power markets. It will pump during lower price periods and generate at higher price periods. This paper presents economic analysis of pumping-storage generator using price (or SMP) duration curve.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.323-326
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• 2006

We investigate pricing and ordering policies in a supply chain consisting of a single manufacturer and a single retailer. Demand at the retailer depends on the retail price and is assumed to be constant over time for the fixed price. The retailer places orders according to an EOQ policy and the manufacturer produces the order quantity according to a lot-for-lot policy. The retailer and the manufacturer cooperates each other to maximize the average profit for the supply chain. A mathematical model is presented and a solution procedure is developed to determine the optimal retail price and order quantity.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.6-9
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• 2000

This paper presents the detailed derivation of optimal nodal price for active power to regionally evaluate system loss and congestion cost. The method is to decompose them into different components corresponding to system loss, transmission congestion, voltage constraint, and so on. The decomposed information for nodal price can be used to provide economic signals for generation or transmission investment as well as to improve the efficient usage of power grid and congestion management. The result of case study on IEEE 30 bus system is reported to illustrate the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.266-272
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• 2011

Real Time Pricing(RTP) is used not only to stabilize the price volatility in electricity market, but to hedge the price risk for Load Serving Entity(LSE). This paper presents an efficient method to reduce the risk of the price volatility in real-time electricity market. For designing the RTP, load patterns of customer are calculated by applying the demand elasticity and customer's utility is also analyzed to compute the RTP revenue through the risk-attribute of the LSE. In the end, the distribution of the LSE's profits can be evaluated to lead the optimal RTP value, depending on the level of customer's participation. Results from the case study based on PJM data are reported to illustrate the proposed method.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.44-48
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• 2015

Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.