• Knowledge Management Research
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• v.24 no.4
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• pp.23-42
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• 2023

This study originated from recognition of importance of firefighters' decision-making in fire response, coupled with existing gap in research. By utilizing Q-methodology, the study aimed to categorize firefighters' subjectivity in fire response decision-making. Through this categorization, the study sought to highlight insights into the current technological and data limitations, as well as potential directions for future R&D in the field of firefighting. The findings of the study revealed that firefighters' subjectivity could be classified into three factors: "emphasis on direct information related to rescue," "emphasis on information related to the target property," and "emphasis on information related to command and coordination." The study theoretically confirmed that the subjectivity of firefighters' decision-making in fire response is partially influenced by their experiences and job. Additionally, the study's significance lay in its approach of collecting specific decision-making factors in fire response, moving beyond general theoretical models. Furthermore, from a policy perspective, the typification of decision-making factors contributed to connecting the identified data-based administrative needs from prior studies. Insights from the study emphasized the importance of leveraging on-site experience in Korea to aid decision-making, calling for the development of equipment and data collection methods that can rapidly and accurately assess on-site conditions.

• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.55-71
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• 1990

The behavioral mechanism underlying the traffic assignment model is a choice, or decision-making process of traveling paths between origins and destinations. The deterministic approach to traffic assignment assumes that travelers choose shortest path from their origin-destination pair. Although this assumption seems reasonable, it presumes that all travelers have perfect information regarding travel time, that they make consistently correct decision, and that they all behave in identical fashion. Stochastic user equilibrium assignment relaxes these presumptions by including a random component in traveler's perception of travel time. The objective of this study is to compare "A Model of Deterministic User Equilibrium Assignment" with "Models of Stochastic User Equilibrium Assignment" in the theoretical and practical aspects. Specifically, SUE models are developed to logit and probit based models according to discrete choice functions. The models were applied to sioux Falls net ork consisting of 24 zones, 24 nodes and 76 links. The distribution of perceived travel time was obtained by using the relationship between speed and traffic flow.

• Management Science and Financial Engineering
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• v.10 no.1
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• pp.25-41
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• 2004

There have been many studies to build a model that can help investors construct optimal portfolio. Most of the previous models, however, are based upon the path-breaking Markowitz model (1959) which is a quantitative model. One of the most important problems with that kind of quantitative model is that, in reality, most of the investors use not only quantitative, but also qualitative information when they select their optimal portfolio. Since collecting both types of information from the markets are time consuming and expensive, making a set of target assets smaller, without suffering heavy loss in the rate of return, would attract investors. To extract only desired assets among all available assets, we need knowledge that identifies investors' preference for the risk of the assets. This study suggests two-layer decision-making rules capable of identifying an investor's risk preference and an architecture applying them to a quantitative portfolio model based on risk and expected return. Our knowledge-based portfolio system is to build an investor's preference-oriented portfolio. The empirical tests using the data from Korean capital markets show the results that our model contributes significantly to the construction of a better portfolio in the perspective of an investor's benefit/cost ratio than that produced by the existing portfolio models.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.310-320
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• 1999

A mixed model assembly line (MMAL) is a special type of production line where a variety of product models similar in product characteristics are assembled. Determining the model sequence is an important problem for the efficient use of MMALs. This paper considers interactive multiobjective decision making problems for MMAL sequencing. Evolution program is employed as an underlying framework. In this study, a way of approximating the linear utility function is first studied. To improve its search efficiency to the solution space preferred by a decision maker, some modifications of a standard evolution program are made: operating several subpopulations instead of a single population and merging two or more subpopulations to a single subpopulation, and using a Pareto pool. Extensive computational experiments are carried out to verify the performance of the proposed approach. The computational results show that our approach is promising in solution quality.

• Management Science and Financial Engineering
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• v.21 no.1
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• pp.19-24
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• 2015

As decision makers do not make optimal decisions in practice despite the existence of optimal solutions in many models, there has been a rising interest in behavioral operations management recently. In this study, we aim for a comparative study to analyze the inventory decisions in Korea, America, and China, by conducting the same newsvendor experiment in Korea and compare the results with those of previous studies. From the comparative analysis, some national characteristics in decision-making processes have been observed but there is lowly significant difference in order quantities among the three groups. Korean students show lower level of understanding in demand distributions and tendencies of anchoring on the mean demand and being risk-averse. The finding that individuals make their own decisions differently based on their different behaviors suggests that we need to consider individual approach in analyzing human decision-making processes rather than adapting aggregate approach.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.63-68
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• 1999

The purpose of this study is to design the framework of DSS (Decision Support System) for drought mitigation with internet and web interface. The users who apart from central government always connect to its main server via iternet. And web interface and browser operable models make it possible to analze data related with drought and planning for drought mitigation . Thereby, it need to build a database system , which manages data update from the users, and develop java-applet programs to assist decision making. The framework of DSS might be nicely adapted to the planning and decision making for the agricultural drought migigation.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.78-89
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• 2008

The need for holistic modeling efforts for returns that capture the extended closed loop supply chain (CLSC) system at strategic as well as operational level has been clearly recognized by the industry and academia. Strategic decision-makers need comprehensive models that can guide them in efficient decision-making to increase the profitability of the entire forward and return chain. Therefore, determination of a near optimal design configuration, which includes the environmental, economical and technological capability factors, is important in strategic decision-making effort that affect the profitability of the closed loop supply chain. In this paper, we adopted an improved system dynamics methodology to tackle strategic issues that affect various performance measures, like market, time/cost, environment etc., for closed loop supply chains. After studying real life implementation issues in CLSC design, we presented guidelines for the PBM (Participative Business Modeling) methodology and presented its extension for the strategic dynamic system modeling of return chains. Finally, we demonstrated the measurement of operational performance by extending SD (system dynamic) application to closed loop supply chain management.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.257-281
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• 2024

In real life, a decision-maker can assign multiple values for pairwise comparison with a certain confidence level. Studies incorporating multi-choice parameters in multi-criteria decision-making methods are lacking in the literature. So, In this work, an extension of the Best-Worst Method (BWM) with multi-choice pairwise comparisons and multi-choice confidence parameters has been proposed. This work incorporates an extension to the original BWM with multi-choice uncertainty and confidence level. The BWM presumes the Decision-Maker to be fully confident about preference criteria vectors best to others & others to worst. In the proposed work, we consider uncertainty by giving decision-makers freedom to have multiple choices for preference comparison and having a corresponding confidence degree for each choice. This adds one more parameter corresponding to the degree of confidence of each choice to the already existing MCDM, i.e. multi-choice BWM and yields acceptable results similar to other studies. Also, the consistency ratio remained low within the acceptable range. Two real-life case studies are presented to validate our study on proposed models.