• International conference on construction engineering and project management
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• 2015.10a
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• pp.114-116
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• 2015

This paper presents a Stochastic Time-Cost Tradeoff analysis system (STCT) that identifies optimal construction methods for activities, hence reducing the project completion time and cost simultaneously. It makes use of schedule information obtained from critical path method (CPM), applies alternative construction methods data obtained from estimators to respective activities, computes an optimal set of genetic algorithm (GA) parameters, executes simulation based GA experiments, and identifies near optimal solution(s). A test case verifies the usability of STCT.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.25-42
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• 2013

We consider a project time-cost tradeoff problem with two milestones, where one of the jobs has an uncertain processing time. Unless each milestone is completed on time, some penalty cost may be imposed. However, the penalty costs can be avoided by compressing the processing times of some jobs, which requires additional resources or costs. The objective is to minimize the expected total costs subject to the constraint on the expected project completion time. We show that the problem can be solved in polynomial time if the precedence graph of a project is a chain.

• Management Science and Financial Engineering
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• v.22 no.1
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• pp.5-12
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• 2016

We consider a time-cost tradeoff problem with multiple milestones under a chain precedence graph. In the problem, some penalty occurs unless a milestone is completed before its appointed date. This can be avoided through compressing the processing time of the jobs with additional costs. We describe the compression cost as the convex or the concave function. The objective is to minimize the sum of the total penalty cost and the total compression cost. It has been known that the problems with the concave and the convex cost functions for the compression are NP-hard and polynomially solvable, respectively. Thus, we consider the special cases such that the cost functions or maximal compression amounts of each job are identical. When the cost functions are convex, we show that the problem with the identical costs functions can be solved in strongly polynomial time. When the cost functions are concave, we show that the problem remains NP-hard even if the cost functions are identical, and develop the strongly polynomial approach for the case with the identical maximal compression amounts.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.109-110
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• 2015

This paper presents a computational method that identifies an exact set of optimal overlap rates between critical activities to meet job site specific needs by using rework cost-slope. The procedures to compute the exact solution are provided in peudocode algorithm. The method is coded into Exact Concurrent Construction Scheduling system that allows practitioners to make more informed decision in accordance with the site-specific condition involved in the overlapping of critical activities. Test cases verify the validity of the computational method and the usability of the system.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.85-92
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• 2018

We consider a project scheduling problem in which the jobs can be compressed by using additional resource to meet the corresponding due dates, referred to as a time-cost tradeoff problem. The project consists of two independent subprojects of which precedence graph is a chain. The due dates of jobs constituting the project can be interpreted as the multiple assessments in the life of project. The penalty cost occurs from the tardiness of the job, while it may be avoided through the compression of some jobs which requires an additional cost. The objective is to find the amount of compression that minimizes the total tardy penalty and compression costs. Firstly, we show that the problem can be decomposed into several subproblems whose number is bounded by the polynomial function in n, where n is the total number of jobs. Then, we prove that the problem can be solved in polynomial time by developing the efficient approach to obtain an optimal schedule for each subproblem.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.410-415
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• 2009

With the implementation of electric power industry reform, the utilities are looking for effective ways to improve the economic efficiency. One area in particular, the equipment maintenance, is being scrutinized for reducing costs while keeping a reasonable level of the reliability in the overall system. Here the conventional RCM requires the tradeoff between the upfront maintenance costs and the potential costs of losing loads. In this paper we describe the issues related to applying so-called the "Reliability-centered Maintenance" (RCM) method in managing electric power distribution equipment. The RCM method is especially useful as it explicitly incorporates the cost-tradeoff of interest, i.e. the upfront maintenance costs and the potential interruption costs, in determining which equipment to be maintained and how often. In comparison, the "Time-based Maintenance" (TBM) method, the traditional method widely used, only takes the lifetime of equipment into consideration. In this paper, the modified Markov model for maintenance is developed. First, the existing Markov model for maintenance is explained and analyzed about transformer and circuit breaker, so on. Second, developed model is introduced and described. This model has two different points compared with existing model: TVFR and nonlinear customer interruption cost (CIC). That is, normal stage at the middle of bathtub curve has not CFR but the gradual increasing failure rate and the unit cost of CIC is increasing as the interruption time is increasing. The results of case studies represent the optimal maintenance interval to maintain the equipment with minimum costs. A numerical example is presented for illustration purposes.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.391-394
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• 2011

3rd party content service is a consumer-based wide selection of contents in which IPTV provider believes will enhance the service considerably extending the variety of contents to finally provide a personalized service. Therefore, the combination of 3rd party content services and the IPTV infrastructure creates a good revenue opportunity but they need to be created. IPTV offers to create personalized channels to distribute 3rd party content service. However, the cost of $3^{rd}$ party content delivery is high when the service is provided from a faraway server. IPTV provider requires an effective strategy of replica placement to minimize the cost without a tradeoff on the performance. We propose a cluster-based strategy of replica placement to find the right balance between cost and performance. The results demonstrate the achievement of an outstanding delivery time and low cost.