• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.30-38
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• 2005

This paper proposes a new method which can calculates nodal price as an economic signal at non-optimal operation. By using pseudo constraints in 11 cases, we calculate shadow price and nodal price based on non-optimal operation. By comparing shadow price and nodal prices based on optimal and non-optimal operation effectiveness of the method is verified.

• Health Policy and Management
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• v.13 no.4
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• pp.84-98
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• 2003

This research objective is to determine the optimal price break points for n­block tariff, because comparing non­linear pricing with uniform pricing on the basis of profit, n­block tariff outperforms two­part tariff, all unit discount price schedule, and uniform pricing. Although the merits of non­linear pricing are well documented, the attempt to practice the non-linear pricing in medical service sector has been relatively rare. The determination of the parameters under n­block tariff is the interesting decision making agenda for marketers. Under n­block tariff, the marketers should decide the optimal price break points and the optimal marginal price for each price zone. The results can be summarized as follows: The researchers found that mixture model can be the feasible methodology for determining the optimal number of n­block tariff and identifying the optimal segmentation criteria. We demonstrate the feasibility and the superiority of the mixture model by applying it to the database of medical examination. The results appear that the number of patients per month can be the optimal segmentation variable. And 6­block tariff is the optimal price break for this medical service.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.95-102
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• 2013

It is difficult to determine an appropriate discount price for daily perishable products to increase profit from a long-term standpoint. Even if the discount pricing is efficient to increase profit of the day, consumers memorize the sales price and they might hesitate to purchase the product at a regular price the following day. The authors discussed the inventory clearance pricing for a single period in our previous study by constructing a mathematical model to derive an optimal sales price to maximize the expected profit by considering the reference price effect of demand. This paper extends the discussion to handle the discount pricing for multiple periods. A mathematical analysis is first conducted to reveal the properties on an objective function, which is the present value of total expected profits for multiple periods. An algorithm is then proposed to derive an optimal price for asymmetric consumers. Numerical experiments investigate the characteristics of the objective function and optimal pricings.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.30-38
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• 2012

This paper discusses a clearance pricing on daily perishable products considering a reference price of consumers. The daily perishable products are sometimes sold at a discount price before closing time to stimulate demand when the number of unsold products is more than initially envisioned. The discount pricing results both in an increase of the revenue of the day and in a decrease of the disposal cost. The discounting, however, also declines a reference price of consumers which is a mental price and serves as an anchor price to judge if a current sales price is loss or gain for the consumers. An excess discounting decreases the demand for the products sold at a regular price in the future and diminishes long-term profit. This study conducts a mathematical analysis on the clearance pricing problem for a single period with stochastic variations both on demand and on the inventory level at clearance time. The expected profit function especially depends on the response of consumers to the clearing price against their reference prices. A procedure is proposed to derive an optimal clearance price when consumers are loss-neutral. A sufficient condition is shown to obtain an optimal price for loss-averse and loss-seeking consumers by an analogous procedure.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.1
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• pp.1-17
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• 2008

Market segmentation is a key strategic factor in increasing the expected profits, especially in the practice of revenue management. A manufacturing firm should manage both manufacturing quantities and pricing decisions over its segmented markets to maximize the expected profits, setting different price for each different segment. Also, market segments should be kept separate in order to prevent demand leakages between different market segments. In fact, even though the markets for different products are firmly segmented, it is not easy to keep separate segmentation because many products might be substitutable by customer buying behavior. That is, customers respond to price changes by purchasing other market's products instead of purchasing the originally requested products, which causes demand substitution effect ; This kind of substitution is referred to as price-driven substitution. Therefore, decisions on optimal prices should take into account the differences in customers' valuation of the different products. We consider a deterministic model for deciding optimal prices in the presence of price-driven substitution, and we compare both symmetrical-and asymmetrical-type demand substitutions between two segmented markets. The objective of this study is to develop analytical and numerical models to examine the impact of price-driven substitution on the optimal price levels and the total expected profits.

• IE interfaces
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• v.24 no.4
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• pp.295-303
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• 2011

This paper presents a discrete time optimal asset buying problem with a predetermined final deadline where an available budget is limited. A cost is paid to search for assets called the search cost. A seller who shows up offers a price for the asset and then the buyer decides whether or not to buy the asset by comparing the offered price to his optimal selection threshold. When the budget becomes less than the search cost or the price of the asset the buyer can get a necessary loan with some interests. We clarify the properties of the buyer's optimal selection threshold in order to maximize the expected value of budget which is left after paying all the search costs and the price of the asset at that point in time.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.411-413
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• 2001

This paper discusses on an optimal bidding strategy of a generator in a competitive electricity spot market using the information of predicted spot price with some assumptions. Optimal bidding strategy of a generator is derived by solving a profit-maximizing optimization problem with a constraint where the forecasted spot price is treated as a constant value. The main advantage of this methodology is that the optimal bidding strategy of each generator can be obtained independently where the gaming characteristics of generators are merged into the forecasted spot price.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Journal of the Korean Society of Food Culture
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• v.37 no.4
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• pp.335-344
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• 2022

The study was aimed to investigate the operational meal costs by kindergarten size in Seoul and to analyze recognition for optimal meal prices. A survey (31.6% recovery rate) was conducted on all kindergartens (779 kindergartens) in Seoul on April 2021 using descriptive analysis, t-test, and dispersion method. A price sensitivity measurement (psm) method was used to determine optimal meal prices. Result showed an average food cost for kindergartens of 2,647 won, an average labor cost of 605 won, an average operating cost of 146 won, and the total meal cost of 3,506 won. Total meal cost decreased with increasing kindergarten size (p<0.001). On the other hand, kindergartens with more students decreased the ratio of food cost to total meal cost, and operating cost and labor costs (p<0.001) increased. The optimal price of kindergarten operators' meal cost (OPP) was KRW 3,673. Furthermore, the analysis showed the sensitivity of operators' meal costs to kindergarten size was insignificant.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.5
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• pp.606-617
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• 2002

The purpose of this study is to develop a decision model that helps manufacturers and retailers determine the optimal timing of markdown in order to maximize their profit. An optimal timing decision model was developed based on three steps; conjoint measurement, scenario analysis and simulation. Data were collected from the sample of 149 out of 170 undergraduate and graduate students in Seoul in 1997. From the Jeans market, 8 brands; Levi's, lee, Guess, Calvin Klein, Pintos, Get used, MFG, and Basic, were selected as competitors for this study. In the conjoint measurement, respondents estimated the level of preference, from 1 to 100, for each item in which brand, price, style, and colors were used to explain product characteristics. Then, in order to reflect competitive situation in Jeans market, four types of scenarios were developed. In each scenario, simulations were applied to decide optimal timing of markdowns that leads to maximal profitability and sales volume. The profit was calculated based on the equation; Profit = Jean's market volume x market share of each brand - cost, where market volume was obtained by integral calculus for market utility function, and market share by logit value of part-worth from the conjoint analysis. For the purpose of the parsimony of the research, costs and the level of markdown were fixed to 30% of the regular price. In results, the optimal timing decision model identified 3 different types of brands. The brands that do not need to take markdown were Ievi's, MFG, and Basic Jeans characterized by the highest brand power and the highest price zone. The brands that needed to take early markdowns were Guess, Lee, Calvin Klein, and Get Used with the intermediate level of brand power and price. The brand that need late markdown was Pintos with the weakest brand power among the competitors and the lowest price. The optimal range of markdown remains for further research.