• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.161-170
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• 2021

Purpose: The purpose of this study was to evaluate the economic value of installing public Energy storage system (ESS) facilities using a logit regression analysis and Contingent Valuation Method (CVM). We focused mainly on the environmental benefits of ESS and analyzed how the users' attitude toward environmental protection and knowledge of renewable energy affect their Willingness to pay (WTP) Methods: A single-bounded dichotomous choice (SBDC) survey was used to collect the data. We asked participants whether they are willing to pay a randomly presented cost (KRW 100, 500, 1000, 1500, 2000, 2500, 5000, 10000) along with their attitude to toward environmental protection, knowledge of renewable energy, and perceived cost of electric bill. 417 valid samples were collected and used for the analysis. Results: The results of the logit regression show that the initial bid, attitude toward environmental protection, knowledge of renewable energy, and perceived cost of electric bill significantly affect the user's WTP of public ESS facilities. The CVM results show that users are willing to pay KRW 5,049.1/month to install ESS facilities. Conclusion: : According to results, we conclude that the users agree with the need to install ESSs and that environmental benefits of ESSs are important factors for ESS adoption. Therefore, policy makers need to emphasize environmental aspects to install the ESS facilities.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.164-165
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• 2015

Modular construction technique is an adaptation of factory-based mass production concept in ordinary manufacturing industries to construction industry and it assumes that panels, units, etc. are fabricated in factories and assembled in construction sites. Given its structural limitations, modular construction technique is primarily used in low-story buildings whose maximum height is usually five stories, but researchers are actively studying possible adaptation of modular construction technique to high-rise building designs these days as in the case of infill-type modular construction design. Infill-type modular construction technique, most frequently used in high-rise building construction projects, completes frame construction first in reinforced concrete structures and fills unit modules in such structures. However, infill-type modular construction technique leads to longer construction schedule accompanying increase in construction cost, cost overrun due to additional of temporary work, and possible damage to units in the wake of facility construction. Accordingly, this study is performed as a basic study for the development of bottom-up infill-type modular construction technique intended to construct structural frames and fill in units sequentially in a bid to address such drawbacks of current infill-type modular construction technique.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.207-223
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• 2013

Revenue management (RM) has been widely used to model products characterized as perishable. Classical RM model assumed that price is the sole factor in the model. Thus price adjustment becomes a crucial and costly factor in business. In this paper, an optimal pricing model is developed based on minimization of soft customer cost, one kind of price adjustment cost and is solved by Lagrange multiplier method. It is formed by expected discounted revenue/bid price integrating quantity-based RM and pricing-based RM. Quantity-based RM consists of two capacity models, namely, booking limit and overbooking. Booking limit, built by assuming uncertain customer arrival, decides the optimal capacity allocation for two market segments. Overbooking determines the level of accepted order exceeding capacity to anticipate probability of cancellation. Furthermore, pricing-based RM models occupancy/demand rate influenced by internal and competitor price changes. In this paper, a mathematical model based on game theoretic approach is developed for two conditions of deterministic and stochastic demand. Based on the equilibrium point, the best strategy for both hotels can be determined.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.151-160
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• 2019

EPC companies to continue operating overseas, it is increasingly becoming apparent that risk is no longer something to be avoided but a subject to be managed. During the bidding stage, the requirements, specifications and project line items within the bid package must be studied in details to analyze the various risk factors in order to avoid cost overruns. However, reviewing vast quantities of bidding documents is time consuming and labor intensive and is not an easy task and this is where automated information technology can help. For this study, I have constructed an ITB analysis model based on Watson AI that can analyze and apply vast amount of documents more effectively in a short time. Configuration of the Watson Explorer AI architecture for AI-based ITB risk management model research, the selection of learning procedures and analysis subjects, and the performance evaluation criteria were defined, and a test bed was constructed to conduct a pilot research. Consequently, I verified the effectiveness of the analytical time reduction and the quality of its results and VOC operations by professionals.

• Korean Journal of Construction Engineering and Management
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• v.14 no.4
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• pp.24-34
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• 2013

Since 2004, the government decided to gradually introduce Actual Cost Data into cost estimate for improving problems of below-cost tendering and to reflect fair market price through competition and carry contract efficiently. However, there are many concerns that Actual Cost Data has not reflected real market price, even that has contributed to reduce the government's budget. General construction firm's burden for labor cost is imputed to specialty contractors and eventually it becomes construction worker's burden. Therefore, realization of Actual Cost Data is very important factor to settle this system. To understand realization level and make short term forecast, this paper drew construction group of which labor cost constitutes more than 95% of direct cost, and compares their Actual Cost Data with relevant skilled workers's unit wage and predicts using time series analysis. The bid price which is not be reflected market price accelerates work environment changes and leads to directly affect such as late disbursement of wages, bankruptcy to workers. Therefore this paper is expected to be used to the preliminary data for solving the problem and establishing improvement of Actual Cost Data.

• Asia pacific journal of information systems
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• v.13 no.4
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• pp.29-47
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• 2003

This research explores early mover advantages and performance in the cyber market based on an empirical test. It also examines whether early mover strategic capabilities are able to adopt mutually cumulative relationship in the cyber market. Early movers such as eBay.com and Amazon.com seem to have been able to defy exclusive relationship between strategic capabilities. Compared with their followers such as uBid.com and buy.com, they have been able to adopt strong focus, differentiation, and cost leadership strategies. The purpose of this paper is to investigate the differences in strategic choices based on the strategic capabilities and performance of online firms between early movers and followers. The study reviews early mover advantages and disadvantages, and a strategic typology based on Porter's model, as well as strategic capabilities based on the sand cone model.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.1155-1162
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• 2004

This research proposes a new risk analysis method in order to guarantee successful performance of construction projects. The proposed risk analysis methods consists of four phases. First step, AHP model can help contractors decide whether or not they bid for a project by analysing risks involved in the project. Second step, the influence diagraming, decision tree and Monte Carlo simulation are used as tools to analyze and evaluate project risks quantitatively. Third step, Monte Carlo simulation is used to assess risk for groups of activities with probabilistic branching and calendars. Finally, Fuzzy theory suggests a risk management method for construction projects, which is using subjective knowledge of an expert and linguistic value, to analyze and quantify risk. The result of study is expected to improve the accuracy of risk analysis because three factors, such as probability, impact and exposure, for estimating membership function are introduced to quantify each risk factor. Consequently, it will help contractors identify risk elements in their projects and quantify the impact of risk on project time and cost.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.268-270
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• 2006

The purpose of this paper was to explore delay avoiding measures and strategies. The paper was based on previous work of authors on finding delay causes. Firstly, the paper has discussed about delay avoidance measures prescribed by the previous work. As the previous study identified five main causes of construction delays, various measures and strategies to overcome those delay problems have been discussed in sequence in the last sections. Major delay prevention strategies are: involving stakeholders in the project decisions, outreach program, realistic time and resource estimation, try to adjust the triple constraints of time, cost and scope, ensure fair and complete disclosure of information at an early stage of the construction project, contractor, itself should inquire about patent design errors prior to submitting its bid, owner should include in its contract with the consultant an indemnity (protection) clause etc.