• Title/Summary/Keyword: Value Objective

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A Study on Policies and Practices for Family Value (가족가치확산을 위한 정책과제와 대안)

  • Jeong, Young-Keum
    • Journal of Family Resource Management and Policy Review
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    • v.19 no.1
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    • pp.73-92
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    • 2015
  • This study is to review the policy objective of the spread of family value in 2nd Family Policy 2011-2015. The spread of family value is newly adapted sphere in 2nd Family Policy. But this policy objective is not clear, diverse or comprehensive. So, this study attempts to examine two questions: what is the family value in healthy family policy? How this objective is reflected to policy services. Because families are shaped by changes in social norm or trend, this study examined the changing demographics of family affect to family value. And the meaning of family value and the viewpoints are clarified. Last, for the extend of this policy objective, this study suggests to reach consensus on future family in Korean society, to emphasize function of family as social safety net.

The Accuracy of Various Value Drivers of Price Multiple Method in Determining Equity Price

  • YOOYANYONG, Pisal;SUWANRAGSA, Issara;TANGJITPROM, Nopphon
    • The Journal of Asian Finance, Economics and Business
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    • v.7 no.1
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    • pp.29-36
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    • 2020
  • Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying "the ratio of stock price to a value driver" by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

Optimal Implementation of the Value Management Processes for Capital Facility Projects (건설산업에서의 가치경영 프로세스 효율적 활용 방안)

  • Cha, Hee-Sung
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • 2004.11a
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    • pp.89-94
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    • 2004
  • Many innovative management processes, which are also termed as best practices or value improving practice, have been proven to successfully improve the value of capital facility projects. With a lack of any guidance on how to implement the most suitable value management process for a particular project, the objective of this paper is to facilitate the VMP implementation. A CII (Construction Industry Institute)'s recent study, titled as 'Development of the Value Management Toolkit,' encompasses a comprehensive structure of value management and provides a new methodology in optimizing the implementation of the value management processes in order to leverage the unique project circumstances, such as project objectives, resource availability, and site limitations, etc. As a pioneering study, the findings contribute to the expedition of implementing value management processes in the industry and maximize the potential benefits in applying the most benefiical value management process for a particular capital facility project.

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A Study for Robustness of Objective Function and Constraints in Robust Design Optimization

  • Lee Tae-Won
    • Journal of Mechanical Science and Technology
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    • v.20 no.10
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    • pp.1662-1669
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    • 2006
  • Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

Multi-Objective Optimization Using Kriging Model and Data Mining

  • Jeong, Shin-Kyu;Obayashi, Shigeru
    • International Journal of Aeronautical and Space Sciences
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    • v.7 no.1
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    • pp.1-12
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    • 2006
  • In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation, each objective function is converted into the Expected Improvement (EI) and this value is used as fitness value in the multi-objective optimization instead of the objective function itself. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. The present method was applied to a transonic airfoil design. Design results showed the validity of the present method. In order to obtain the information about design space, two data mining techniques are applied to design results: Analysis of Variance (ANOVA) and the Self-Organizing Map (SOM).

A Strong LP Formulation for the Ring Loading Problem with Integer Demand Splitting

  • Lee, Kyung-Sik;Park, Sung-Soo
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2004.05a
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    • pp.306-310
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    • 2004
  • In this paper, we consider the Ring Loading Problem with integer demand splitting (RLP). The problem is given with a ring network, in which a required traffic requirement between each selected node pair must be routed on it. Each traffic requirement can be routed in both directions on the ring network while splitting each traffic requirement in two directions only by integer is allowed. The problem is to find an optimal routing of each traffic requirement which minimizes the capacity requirement. Here, the capacity requirement is defined as the maximum of traffic loads imposed on each link on the network. We formulate the problem as an integer program. By characterizing every extreme point solution to the LP relaxation of the formulation, we show that the optimal objective value of the LP relaxation is equal to p or p+0.5, where p is a nonnegative integer. We also show that the difference between the optimal objective value of RLP and that of the LP relaxation is at most 1. Therefore, we can verify that the optimal objective value of RLP is p+1 if that of the LP relaxation is p+0.5. On the other hand, we present a strengthened LP with size polynomially bounded by the input size, which provides enough information to determine if the optimal objective value of RLP is p or p+1.

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A Study on Multiobjective Genetic Optimization Using Co-Evolutionary Strategy (공진화전략에 의한 다중목적 유전알고리즘 최적화기법에 관한 연구)

  • Kim, Do-Young;Lee, Jong-Soo
    • Proceedings of the KSME Conference
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    • 2000.11a
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    • pp.699-704
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    • 2000
  • The present paper deals with a multiobjective optimization method based on the co-evolutionary genetic strategy. The co-evolutionary strategy carries out the multiobjective optimization in such way that it optimizes individual objective function as compared with each generation's value while there are more than two genetic evolutions at the same time. In this study, the designs that are out of the given constraint map compared with other objective function value are excepted by the penalty. The proposed multiobjective genetic algorithms are distinguished from other optimization methods because it seeks for the optimized value through the simultaneous search without the help of the single-objective values which have to be obtained in advance of the multiobjective designs. The proposed strategy easily applied to well-developed genetic algorithms since it doesn't need any further formulation for the multiobjective optimization. The paper describes the co-evolutionary strategy and compares design results on the simple structural optimization problem.

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Measurement of Fuzz Fibers on Fabric Surface Using Image Analysis Methods

  • Ucar Nuray;Boyraz Plnar
    • Fibers and Polymers
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    • v.6 no.1
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    • pp.79-81
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    • 2005
  • Fuzz on the fabrics, which is the fibers protruded from the fabric surface, is very important in view of appearance quality, since it causes unpleasant appearance on the fabrics and also leads to pilling which makes fabric appearance and soft­ness worse. However, fuzz on fabric surface is measured mostly by subjective methods (human vision) rather than objective methods. Thus, in this study, objective method using image analysis techniques has been developed for the measurement of fuzz on fabric surface. Fuzz on the fabric has also been ranked and rated by experts in order to see the reliability of the results obtained from the fuzz measurement. It was observed that correlation coefficient (r) between rating value and objective mea­surement value was 0.9 and this correlation coefficient value confirmed the reliability of this method.

No Tardiness Rescheduling with Order Disruptions

  • Yang, Jaehwan
    • Industrial Engineering and Management Systems
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    • v.12 no.1
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    • pp.51-62
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    • 2013
  • This paper considers a single machine rescheduling problem whose original (efficiency related) objective is minimizing makespan. We assume that disruptions such as order cancelations and newly arrived orders occur after the initial scheduling, and we reschedule this disrupted schedule with the objective of minimizing a disruption related objective while preserving the original objective. The disruption related objective measures the impact of the disruptions as difference of completion times in the remaining (uncanceled) jobs before and after the disruptions. The artificial due dates for the remaining jobs are set to completion times in the original schedule while newly arrived jobs do not have due dates. Then, the objective of the rescheduling is minimizing the maximum earliness without tardiness. In order to preserve the optimality of the original objective, we assume that no-idle time and no tardiness are allowed while rescheduling. We first define this new problem and prove that the general version of the problem is unary NP-complete. Then, we develop three simple but intuitive heuristics. For each of the three heuristics, we find a tight bound on the measure called modified z-approximation ratio. The best theoretical bound is found to be 0.5 - ${\varepsilon}$ for some ${\varepsilon}$ > 0, and it implies that the solution value of the best heuristic is at most around a half of the worst possible solution value. Finally, we empirically evaluate the heuristics and demonstrate that the two best heuristics perform much better than the other one.

Multi-objective Fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems

  • Zhang, Wenjuan;Ma, Haomiao;Zhang, Junli;Chen, Lingling;Qu, Yang
    • Journal of Power Electronics
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    • v.15 no.4
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    • pp.1119-1130
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    • 2015
  • This study investigates the multi-objective fuzzy optimization of crowbar resistance for the doubly fed induction generator (DFIG) low-voltage ride-through (LVRT). By integrating the crowbar resistance of the crowbar circuit as a decision variable, a multi-objective model for crowbar resistance value optimization has been established to minimize rotor overcurrent and to simultaneously reduce the DFIG reactive power absorbed from the grid during the process of LVRT. A multi-objective genetic algorithm (MOGA) is applied to solve this optimization problem. In the proposed GA, the value of the crowbar resistance is represented by floating-point numbers in the GA population. The MOGA emphasizes the non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy-set-theory-based is employed to obtain the best solution. The proposed approach has been evaluated on a 3 MW DFIG LVRT. Simulation results show the effectiveness of the proposed approach for solving the crowbar resistance multi-objective optimization problem in the DFIG LVRT.