• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3638-3657
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• 2022

In urban vehicular edge computing (VEC) environments, one edge server always serves many task requests in its coverage which results in the resource-constrained problem. To resolve the problem and improve system utilization, we first design a general hierarchical resource management framework based on typical VEC network structures. Following the framework, a specific interacting protocol is also designed for our decision algorithm. Secondly, a greedy bidding-based multi-hop task scheduling decision algorithm is proposed to realize effective task scheduling in resource-constrained VEC environments. In this algorithm, the goal of maximizing system utility is modeled as an optimization problem with the constraints of task deadlines and available computing resources. Then, an auction mechanism named greedy bidding is used to match task requests to edge servers in the case of multiple hops to maximize the system utility. Simulation results show that our proposal can maximize the number of tasks served in resource constrained VEC networks and improve the system utility.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.285-296
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• 2008

The purpose of this paper is to examine the IT governance decision structure of the four strategic modes of IT strategy grid, and compare and analyze the differences in the IT governance decision structure of companies that produce superior results and those that produce inferior results. The survey method was used for this paper, and data from a total of 209 companies that were listed on the KOSDAQ 300 and KOSPI 200 were used for the analysis. The results show that each mode has a different IT governance decision structure from the others, and the IT governance decision structure of companies with high results and those with low results are also different for each mode. The results of this paper are significant in that, for each mode, it presents the decision structure framework for promoting desirable behavior of companies carrying out IT governance.

• Journal of Construction Engineering and Project Management
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• v.7 no.1
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• pp.30-36
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• 2017

Most local agencies such as counties and small cities continuously express difficulties in making technically and financially defensible decisions on their pavement infrastructure maintenance and rehabilitation. Unlike pavement systems managed by state highway agencies, the total lane-miles of many local pavements are significantly short and they are managed by a limited number of staff who typically have multiple responsibilities. Most local agencies also do not have historical pavement performance data and the lack of a systematic decision making framework exacerbates the problem. A structured framework and an easily accessible decision support tool that reflects their local requirements, practices and operational conditions would greatly assist them in making consistent and defensible decisions. This study fills this gap by developing a systematic pavement treatment selection framework and a spreadsheet based tool for local agencies. It is expected that the proposed framework will significantly help local agencies to improve their pavement asset management practices at the project level.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1142-1149
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• 2003

Various organizational factors effect successful implementation of IT enabled business transformation. Among them, the most critical success factor is deemed to overcoming change management problem. Lots of studies have been made on Implementation methodologies and business process formalizations to encourage organizational members to accept new business process changes. However, the logic or process redesign still depends on qualitative problem solving techniques mostly depending on basically human intuition such as brainstorming. cause-and-effect analysis. and so on. In this paper, we focused on developing analytic framework to design to-be business process structure. which can complement qualitative problem solving procedures. With effective use of IT as an enabler, we provide algorithmic framework applicable to designing various business process changes such as process automation, business process resequencing, and more radical process integration. The framework follows dynamic programming approach in the literature, which is based on the decision making paradigm of organizations to abstract business processes as quantitative decision models. As such, our research ran fill the gap of limited development of theory based analytic methodologies for business process design, by providing objective rationale to reach the consensus among the organizational members including senior management.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.89-102
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• 2000

Many organizations have developed their own traditional quality information systems. But, they think of it as one of the functional information systems not as a company-wide decision support information systems. A study on traditional quality information systems(QIS) has been conducted and a new conceptual framework of quality information system is proposed in this paper. In order to support enterprise wide total quality management aggressively, a new conceptual framework, named quality management support information system(QMSIS) is developed and proposed. This framework is based upon Malcolm Baldrige National Quality Award(MBNQA) model integrates management information system approach and traditional quality information system concept. In this model, organizational performance and process performance can be monitored to support managers , decision making about organizational quality management activities.

• Journal of Environmental Impact Assessment
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• v.5 no.2
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• pp.79-91
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• 1996

In Chapter 40, "Information for decision-making", of Agenda 21, it was Slated that, "indicators of sustainable development need to be developed to provide solid bases for decision-making at all levels and to contribute to a self-regulating sustainability of integrated environment and development systems." Sustainable development has been defined as "development that meets the needs of the present without compromising the ability of future generations to meet their own needs", An indicator that measures sustainability should therefore focus on this definition. One of the most widely used frameworks for environmental indicators is the Pressure-State-Response model proposed by the OECD. And we introduced the the Driving force-State-Response framework, the adaptation of Pressure-State-Response model, for UN sustainable development indicators. Therefore, in our country, indicators for sustainable development should be developed by using the DSR framework.

• 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.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.86-91
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• 2003

The utility and the nuclear research institutes in korea have conducted research for improving inefficient requirements in technical specifications using the results of probability risk assessments and informations with risk. However, the guidance for reviewing the improved technical specifications has not been developed. The objective of this study is to develop a decision-making framework for investigating and reviewing the technical documents associated with the to changes of technical specification This study has developed a decision-making framework for reviewing the improvements of the RI-TS(Risk-Informed Technical Specifications). This work may contribute to enhancing both the safety and the efficiency of nuclear power plants by changing Technical Specifications proposed by the utility.

• Asia pacific journal of information systems
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• v.6 no.1
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• pp.39-74
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• 1996

Most of the research on a group decision support system [GDSS] has focused on directly examining its effect on the decision outcomes. Under this research framework, however, the role of group interaction process is largely ignored. This study focuses on the effect of the group interaction process on decision-making performance when a GDSS is used as the only medium for group interaction. Specifically, this study sought to determine whether significant relationships exist between the quality of the decision and the decision functions, contingent phases, and different decision paths. Natural interaction processes of decision -making groups was simulated in an experimental setting in which volunteer subjects from several business classes were assigned to dispersed three-person groups undertook the experimental task via a decision network. A baseline GDSS was developed for this setting. The results of this study confirmed earlier studies in a non - GDSS setting to suggest significant effects of decision functions and contingent phases on the quality of decision but no significant relationship between decision path and the quality of group decision.

• Educational Technology International
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• v.24 no.2
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• pp.295-329
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• 2023

The recent development of metaverse-related technology has led to efforts to overcome the limitations of time and space in education by creating a virtual educational environment. To make use of this platform efficiently, applying learning analytics has been proposed as an optimal instructional and learning decision support approach to address these issues by identifying specific rules and patterns generated from learning data, and providing a systematic framework as a guideline to instructors. To achieve this, we employed an inductive, bottom-up approach for framework modeling. During the modeling process, based on the activity system model, we specifically derived the fundamental components of the learning analytics framework centered on learning activities and their contexts. We developed a prototype of the framework through deduplication, categorization, and proceduralization from the components, and refined the learning analytics framework into a 7-stage framework suitable for application in the metaverse through 3 steps of Delphi surveys. Lastly, through a framework model evaluation consisting of seven items, we validated the metaverse learning analytics framework, ensuring its validity.