• The Journal of Information Systems
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• v.5
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• pp.115-147
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• 1996

1. Introduction The new computing era started with the various computer technologies and services having been used in communication and automation area since 1980's. We call that era information technology(IT) era. In such era, especially communication plays very important roles in every aspect. So Schoderbek named that era the ege of c2. Therefore, communition became widely used in organizations. Now the majority of organizations have computer-aided communication capabilities that facilitate access to people and information, both within and outside organization. So one objective of this study is to assess the effects of these changes in data communication on decision making. Decision making is the essence of management and is too important to organizational success. This dissertation has three basic objectives: 1)to clarify the concept of data communication, who influences on decision making, and the concept of decision types, managerial and operational, may be affected differently by data communication 2)to investigate whether the effects of data communication upon decision making may be organizational variables. 3)to verify that business and decision types may affect different impact on decision making.2. Hypotheses Four attributes are selected to make hypotheses from the information attributes presented by famous scholars. They are as follows. ①effectiveness ②routinization ③communication easiness ④timeliness Hypotheses are developed according to these attributes, which are chosen from the literature study and theory H1 : Data communication is positively related to the effectiveness of DM H2 : Data communication is positively related to the routinization of DM H3 : Data communication is positively related to the communication easiness of DM H4 : Data communication is positively related to the timeliness of information for DM3. Methodology After pilot study, data are collected from the decision makers in 200 companies located at Seoul and the metropolitan area. A random sample of 174 employees sent back their questionnaires(response rate of 87%). Among them, 151 questionnaires was useful to the analysis of this study(useful rate of 75.5%).4. Conclusion and Discussion Among four proposed hypotheses, all hypotheses are fully supported. They are as follows. 1)effectiveness 2)routinization 3)communication easiness 4)timeliness. So, first objective of this study is proved. Namely, to clarify that the effects of data communication upon DM is fully supported. But they are different from the decision types. Second one is not apparently verfied. i.e. the effect of data communication on the decision variables is not moderated by organizational variables. Third is inspected. The effects of data communication differs from the industry and decision types evidently. This study has many limitations to generalize the statistical results. Since the definition of data communication has broad meanings in reality. So allare not contained in this research. Another restrict in this study is like this. Decision types are usually divided into three types-operational, managerial, strategic DM. But in this study, strategic DM is left out.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.513-524
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• 2006

The ammunition allocation problem is a Multi-objective optimization(MOO) problem, maximizing fill-rate of multiple user troops and minimizing transportation time. Recent studies attempted to solve this problem by the prior preference articulation approach such as goal programming. They require that all the preference information of decision makers(DM) should be extracted prior to solving the problem. However, the prior preference information is difficult to implement properly in a rapidly changing state of war. Moreover they have some limitations such as heavy cognitive effort required to DM. This paper proposes a new ammunition allocation model based on more reasonable assumptions and uses an interactive MOO method to the ammunition allocation problem to overcome the limitations mentioned above. In particular, this article uses the GDF procedure, one of the well-known interactive optimization methods in the MOO liter-ature, in solving the ammunition allocation problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5229-5252
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• 2016

With proliferation of diverse network access technologies, users demands are also increasing and service providers are offering a Quality of Service (QoS) to satisfy their customers. In roaming, a mobile node (MN) traverses number of available networks in the heterogeneous wireless networks environment and a single operator is not capable to fulfill the demands of user. It is crucial task for MN for selecting a best network from the list of networks at any time anywhere. A MN undergoes a network selection situation frequently when it is becoming away from the home network. Multiple Attribute Group Decision (MAGD) method will be one of the best ways for selecting target network in heterogeneous wireless networks (4G). MAGD network selection process is predominantly dependent on two steps, i.e., attribute weight, decision maker's (DM's) weight and aggregation of opinion of DMs. This paper proposes Multi-Attribute Intuitionistic Fuzzy Group Decision Method (MAIFGDM) using TOPSIS for the selection of the suitable candidate network. It is scalable and is able to handle any number of networks with large set of attributes. This is a method of lower complexity and is useful for real time applications. It gives more accurate result because it uses Intuitionistic Fuzzy Sets (IFS) with an additional parameter intuitionistic fuzzy index or hesitant degree. MAIFGDM is simulated in MATLAB for its evaluation. A comparative study of MAIFDGM is also made with TOPSIS and Fuzzy-TOPSIS in respect to decision delay. It is observed that MAIFDGM have low values of decision time in comparison to TOPSIS and Fuzzy-TOPSIS methods.

• IE interfaces
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• v.21 no.1
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• pp.75-84
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• 2008

It is a difficult task for decision makers(DMs) to choose an appropriate release plan which balances the conflicts between water storage and hydro-electric energy generation in a multi-reservoir operation problem. In this study, we proposed a DEA-based ranking procedure by which the DM can rank the potential alternatives and select the best solution among the Pareto-optimal solutions. The proposed procedure can resolve the problem of mix inefficiency that may cause errors in measuring the efficiency of alternatives. We applied the proposed procedure to the multi-reservoir operation problem for the Geum-River basin and could choose the best efficient solution from the Pareto-set which were generated by the Coordinated Multi-Reservoir Operating Model.

• Journal of Information Processing Systems
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• v.8 no.1
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• pp.21-54
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• 2012

As we know every software development process is pretty large and consists of different modules. This raises the idea of prioritizing different software modules so that important modules can be tested by preference. In the software testing process, it is not possible to test each and every module regressively, which is due to time and cost constraints. To deal with these constraints, this paper proposes an approach that is based on the fuzzy multi-criteria approach for prioritizing several software modules and calculates optimal time and cost for software testing by using fuzzy logic and the fault tolerance approach.

• Journal of Communications and Networks
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• v.12 no.6
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• pp.568-576
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• 2010

The paper presents a control architecture aimed at implementing bandwidth optimization combined with call admission control (CAC) over a digital video broadcasting (DVB) return channel satellite terminal (RCST) under quality of service (QoS) constraints. The approach can be applied in all cases where traffic flows, coming from a terrestrial portion of the network, are merged together within a single DVB flow, which is then forwarded over the satellite channel. The paper introduces the architecture of data and control plane of the RCST at layer 2. The data plane is composed of a set of traffic buffers served with a given bandwidth. The control plane proposed in this paper includes a layer 2 resource manager (L2RM), which is structured into decision makers (DM), one for each traffic buffer of the data plane. Each DM contains a virtual queue, which exactly duplicates the corresponding traffic buffer and performs the actions to compute the minimum bandwidth need to assure the QoS constraints. After computing the minimum bandwidth through a given algorithm (in this view the paper reports some schemes taken in the literature which may be applied), each DM communicates this bandwidth value to the L2RM, which allocates bandwidth to traffic buffers at the data plane. Real bandwidth allocations are driven by the information provided by the DMs. Bandwidth control is linked to a CAC scheme, which uses current bandwidth allocations and peak bandwidth of the call entering the network to decide admission. The performance evaluation is dedicated to show the efficiency of the proposed combined bandwidth allocation and CAC.

• The Journal of Bigdata
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• v.5 no.1
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• pp.11-27
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• 2020

In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.