• Asia Marketing Journal
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• v.16 no.1
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• pp.35-69
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• 2014

The impact of competition on pricing has been studied in the context of counterfactual merger analyses where expected optimal prices in a hypothetical monopoly are compared with observed prices in an oligopolistic market. Such analyses would typically assume static decision making by consumers and firms and thus have been applied mostly to data obtained from consumer packed goods such as cereal and soft drinks. However such static modeling approach is not suitable when decision makers are forward looking. When it comes to the markets for durable products with indirect network effects, consumer purchase decisions and firm pricing decisions are inherently dynamic as they take into account future states when making purchase and pricing decisions. Researchers need to take into account the dynamic aspects of decision making both in the consumer side and in the supplier side for such markets. Firms in a two-sided market typically subsidize one side of the market to exploit the indirect network effect. Such pricing behaviors would be more prevalent in competitive markets where firms would try to win over the battle for standard. While such qualitative expectation on the relationship between pricing behaviors and competitive structures could be easily formed, little empirical studies have measured the extent to which the distinct pricing structure in two-sided markets depends on the competitive structure of the market. This paper develops an empirical model to measure the impact of competition on optimal pricing of durable products under indirect network effects. In order to measure the impact of exogenously determined competition among firms on pricing, we compare the equilibrium prices in the observed oligopoly market to those in a hypothetical monopoly market. In computing the equilibrium prices, we account for the forward looking behaviors of consumers and supplier. We first estimate a demand function that accounts for consumers' forward-looking behaviors and indirect network effects. And then, for the supply side, the pricing equation is obtained as an outcome of the Markov Perfect Nash Equilibrium in pricing. In doing so, we utilize numerical dynamic programming techniques. We apply our model to a data set obtained from the U.S. video game console market. The video game console market is considered a prototypical case of two-sided markets in which the platform typically subsidizes one side of market to expand the installed base anticipating larger revenues in the other side of market resulting from the expanded installed base. The data consist of monthly observations of price, hardware unit sales and the number of compatible software titles for Sony PlayStation and Nintendo 64 from September 1996 to August 2002. Sony PlayStation was released to the market a year before Nintendo 64 was launched. We compute the expected equilibrium price path for Nintendo 64 and Playstation for both oligopoly and for monopoly. Our analysis reveals that the price level differs significantly between two competition structures. The merged monopoly is expected to set prices higher by 14.8% for Sony PlayStation and 21.8% for Nintendo 64 on average than the independent firms in an oligopoly would do. And such removal of competition would result in a reduction in consumer value by 43.1%. Higher prices are expected for the hypothetical monopoly because the merged firm does not need to engage in the battle for industry standard. This result is attributed to the distinct property of a two-sided market that competing firms tend to set low prices particularly at the initial period to attract consumers at the introductory stage and to reinforce their own networks and eventually finally to dominate the market.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1296-1301
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• 2004

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy -neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes learning approach of fuzzy-neural network by immune algorithm. The proposed learning model is presented in an immune based fuzzy-neural network (FNN) form which can handle linguistic knowledge by immune algorithm. The learning algorithm of an immune based FNN is composed of two phases. The first phase used to find the initial membership functions of the fuzzy neural network model. In the second phase, a new immune algorithm based optimization is proposed for tuning of membership functions and structure of the proposed model.

• Journal of KIBIM
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• v.7 no.3
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• pp.40-48
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• 2017

It only takes one failed project to wipe out an entire year's profit, when the projects are not managed efficiently. Additionally, escalating costs of materials and a competitive local construction market make subcontractors a challenge. Subcontractors have finite resources that should be allocated simultaneously across many projects in a dynamic manner. Significant scheduling problems are posed by concurrent multi-projects with limited resources. The objective of this thesis is to identify the effect of productivity changes on the total cost resulting from shifting crews across projects using a descriptive model. To effectively achieve the objective, this study has developed a descriptive cost model for a subcontractor with multi-resources and multi-projects. The model was designed for a subcontractor to use as a decision-making tool for resources allocation and scheduling. The model identified several factors affecting productivity. Moreover, when the model was tested using hypothetical data, it produced some effective combinations of resource allocation with associated total costs. Furthermore, a subcontractor minimizes total costs by balancing overtime costs, tardiness penalties, and incentive bonus, while satisfying available processing time constraints.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.5
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• pp.67-74
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• 2002

Technology Management and Research & Development(R&D) have been one of the most difficult divisions for measurement and evaluation. In spite of these difficulties, the importance of R&D has been dramatically increased. It is very difficult to manage more efficiently and effectively than any other departments of production, finance, marketing and so on. As criticizing the shortcomings of the traditional evaluation system in making decisions for corporate management which has only been focused on financial indices, so Kaplan & Norton has suggested the Balanced Scorecard(BSC) which can be managed Critical Success Factors(CSF) in accordance with corporate's strategy. The Analytic Network Process(ANP), based on the Analytic Hierarchy Process, allows the decision makers to leap beyond the traditional hierarchy to the interdependent environment of network modeling. Based on BSC, this study has developed the evaluation system for R&D which has used ANP transforming quantitative and qualitative indices to the quantifying scales in evaluating R&D.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.870-875
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• 2004

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, the robust design strategy is developed based on the DACE and the global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the system. The robustness is determined by the DACE model to reduce the real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• Journal of the military operations research society of Korea
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• v.17 no.2
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• pp.31-43
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• 1991

The fire effectiveness and the operationability of the ground weapon system (such as tank, armored vehicle, howitzer, MLRS, ${\cdots}$), whose operations are usually happened on the ground, are dependent not only on their performances but also on the terrain environments. Especially, the artillery weapons systems' effectiveness is largely varied, because their maneuverability (such as translation, occupation of their sites) and the fire effectiveness are very dependent on the terrain. In this paper, presented are the methods how to analyze the terrain using the digital terrain data. And a software (which are implemented on the IBM PC compatible personal computer) is developed for the analysis of the terrain using the various method of computer Aided Geometric Design and Modeling. The S/W is expected to be very useful for the evaluation of the artillery weapon systems and for the commanders' decision making.

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.890-900
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• 2010

The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.436-439
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• 2003

Urbanization is worldwide phenomenon and unexceptional in Korea. It is necessary in the spatial decision making steps to predict urban forms for the efficient land use. This study aims to develop urban growth model based on allometry which deals with relationships between urban populations and urban area. For the input data and accuracy assessments, various GIS techniques are used. Although this research is an exemplary urban growth model dealing with physical data only, it can be a good start to develop a more practical model having socio-economic sides for planning practices.

• Computers and Concrete
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• v.12 no.2
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• pp.109-129
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• 2013

This work presents an approach to model concrete shrinkage. The goal is to permit the concrete industry's experts to develop independent prediction models based on a reduced number of experimental data. The proposed approach combines fuzzy logic and genetic algorithm to optimize the fuzzy decision-making, thereby reducing data collection time. Such an approach was implemented for an experimental data set related to self-compacting concrete. The obtained prediction model was compared against published experimental data (not used in model development) and well-known shrinkage prediction models. The predicted results were verified by statistical analysis, which confirmed the reliability of the developed model. Although the range of application of the developed model is limited, the genetic-fuzzy approach introduced in this work proved suitable for adjusting the prediction model once additional training data are provided. This can be highly inviting for the concrete industry's experts, since they would be able to fine-tune their models depending on the boundary conditions of their production processes.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.202-206
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• 1994

Mathematical model of the pollution rate of substation insulators is constructed, taking the model parameters as wind speed, wind direction, typhoon conditions and rainfall in an hourly basis. The main feature of model construction is to distinguish the effect of each parameter by separately analyzing the positive and negative pollution causing factors. Model parameters for the insulators of Karatsu substation, Saga, Japan were estimated and model validation was done using the actual data, in which the pollution deposits on the insulators were measured using pilot insulator and 'salt meter'. The proposed model of the pollution rate [mg/cm$^{2}$/hr] enables the identification of the effective parameters and prediction of the pollution rate so that it helps for the automatic decision making for insulator cleaning or the model can be used as a tool for the substation engineers to make precautionary measures.