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Journal articles on the topic 'Design Decision Support System'

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Published: 4 June 2021

Last updated: 6 February 2022

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1

Saha, G. K., and Sandeep Kumar. "Clinical Decision Support System Design Issues." International Journal of Applied Research on Information Technology and Computing 4, no. 1 (2013): 42. http://dx.doi.org/10.5958/j.0975-8089.4.1.004.

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Sun, Xiaoqian, Volker Gollnick, Yongchang Li, and Eike Stumpf. "Intelligent Multicriteria Decision Support System for Systems Design." Journal of Aircraft 51, no. 1 (January 2014): 216–25. http://dx.doi.org/10.2514/1.c032296.

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3

Barnett, Barbara J. "Perspectives on Decision Analysis for Decision Support System Design." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 16 (October 1993): 1101. http://dx.doi.org/10.1177/154193129303701613.

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This symposium addresses the characterization of human decision making within a complex environment for the purpose of developing improved decision support systems. All of the work presented in this symposium was conducted under a Navy research program entitled “Tactical Decision Making Under Stress” (TADMUS). The overall objective of the TADMUS program is to improve tactical decision making of anti-air warfare (AAW) crew members within the Aegis cruiser's combat information center (CIC) under conditions of stress and uncertainty. The unique aspect of this effort is that each presentation addresses decision making behavior, within a single domain, from a different perspective. The goal of each effort is to characterize some aspect of expert decision making performance within the AAW task environment, and to make recommendations for the resulting decision support system design based upon these characterizations. The result is a multi-faceted, human-centered approach to information organization and interface display design for a decision support system.

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Peng, Guang Yu. "Marketing Decision and Decision Support System Design Based on Web." Advanced Materials Research 850-851 (December 2013): 1048–51. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.1048.

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This paper analyzes the DSS characteristics about the marketing under the internet as well as the influencing factors of the market decisions, Studying the decision-making functions of marketing decision support system DSS. It proposed the marketing DSS design, logical structure and its implementation based on a data warehouse as the center, online analysis processing and data mining as a means.

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Gonçalves, José M., and Luis S. Pereira. "Decision Support System for Surface Irrigation Design." Journal of Irrigation and Drainage Engineering 135, no. 3 (June 2009): 343–56. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0000004.

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ALY, A. A., and M. SUBRAMANIAM. "Design of an FMS decision support system." International Journal of Production Research 31, no. 10 (October 1993): 2257–73. http://dx.doi.org/10.1080/00207549308956856.

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Li, Ni, Runxiang Tan, Zhanpeng Huang, Chao Tian, and Guanghong Gong. "Agile Decision Support System for Aircraft Design." Journal of Aerospace Engineering 29, no. 2 (March 2016): 04015044. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000514.

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8

Ho, J. K. K., and D. Sculli. "System complexity and the design of decision support systems." Systems Practice 8, no. 5 (October 1995): 505–16. http://dx.doi.org/10.1007/bf02281958.

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9

Jarupathirun, Suprasith, and Fatemeh “Mariam” Zahedi. "Dialectic decision support systems: System design and empirical evaluation." Decision Support Systems 43, no. 4 (August 2007): 1553–70. http://dx.doi.org/10.1016/j.dss.2006.03.002.

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Khatun, Madhury, and Shah J. Miah. "Design Methods for Decision Support Systems." International Journal of Business Intelligence Research 9, no. 1 (January 2018): 38–52. http://dx.doi.org/10.4018/ijbir.2018010103.

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This article reviews methods to the development of a decision support system (DSS) solution for small business owners/managers. The main objective of designing the DSS artefact is to support the strategic decision-making for achieving competitive advantages in the business-to-consumer (B2C) e-commerce environment. Many researchers in the DSS domain utilised various methods to design of information systems (IS) artefact, mostly intended for large businesses. Researchers have paid much attention to the business environment as a knowledge source for DSS design and development for the small business strategic decision support needs. User-centred design (UCD) principles were adopted for DSS development. Prior a novel DSS development, multiple case studies were carried out for understanding the user needs and system requirements. Also, knowledge was sourced from the external business environment via the analysis of small business website features against their overseas competitors. The findings suggested developing a DSS solution for small business needs.

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Luong, L., J. He, K. Abhary, and L. Qiu. "A decision support system for cellular manufacturing system design." Computers & Industrial Engineering 42, no. 2-4 (April 2002): 457–70. http://dx.doi.org/10.1016/s0360-8352(02)00032-3.

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12

Septiana, Yosep, Dede Kurniadi, Asri Mulyani, and Wiyoga Baswardono. "Design of decision support system for blood analysis." MATEC Web of Conferences 197 (2018): 03018. http://dx.doi.org/10.1051/matecconf/201819703018.

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Blood analysis is a medical check by identifying blood that gives an explanation of health and is one way to detect certain conditions. Blood type determination is one of the scopes in a blood analysis. This research aims to design a decision support system for blood analysis Solver-oriented for the determination of blood type with ABO system (blood type system A, B, AB, and O) and Rhesus based on Immunohematology Concept theory. The system development method used is Big-Bang Model, with an object-oriented design approach. The result of this research is a software that can be used by health laboratories to assist the process of determining the blood type. With the use of decision support systems information technology-based, the results of the analysis of blood group determination will be more accurate to minimize the occurrence of errors in the determination of blood type.

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13

Harit, Santhanam, G. Don Taylor, and C. Ray Asfahl. "A decision support system for container design in production systems." Integrated Manufacturing Systems 8, no. 4 (August 1997): 195–207. http://dx.doi.org/10.1108/09576069710182018.

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14

Dargam, Fátima C. C., and Erhard W. Perz. "A decision support system for power plant design." European Journal of Operational Research 109, no. 2 (September 1998): 310–20. http://dx.doi.org/10.1016/s0377-2217(98)00059-9.

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15

Lewis, L. F., and K. S. Keleman. "Issues in Group Decision Support System (GDSS) design." Journal of Studies in International Education 14, no. 6 (January 1, 1988): 347–54. http://dx.doi.org/10.1177/102831538801400605.

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Syaripuddin, Undang, Cepy Slamet, Hendri Prabowo, and Muhammad Ali Ramdhani. "Design of decision support system for santri admission." MATEC Web of Conferences 197 (2018): 03010. http://dx.doi.org/10.1051/matecconf/201819703010.

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Pesantren requires some new qualified students through selection process for deciding academic and behavioural fairness. In this context, it is reasonable to utilize an application of a Decision Support System (DSS) to accelerate the student admission mechanisms since the existing process is unable to serve as reliable and objective procedures. The aim of this study is to design a specific computerized application that can be used to capture data of some potential students and recording relevant data for pesantren. Using a structured approach for the system design method, selection-based value is analysed by a Multi Attribute Utility Theory. The study is resulting a capable DSS application to identify students who have proper criteria and build a measurable data documentation.

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17

G. J. Wilmes, D. L. Martin, and R. J. Supalla. "Decision Support System for Design of Center Pivots." Transactions of the ASAE 37, no. 1 (1994): 165–75. http://dx.doi.org/10.13031/2013.28067.

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18

Vasconcellos, J. "A decision support system for floating platform design." Ocean Engineering 26, no. 9 (October 1999): 865–89. http://dx.doi.org/10.1016/s0029-8018(98)00008-0.

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19

Wollover, David. "Design-To-Cost Decision Support System (DTC-DSS)." Journal of Cost Analysis & Management 1, no. 1 (January 1999): 3–33. http://dx.doi.org/10.1080/15411656.1999.10462397.

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20

Liang, Ting-Peng, and Christopher V. Jones. "Design of a Self-evolving Decision Support System." Journal of Management Information Systems 4, no. 1 (June 1987): 59–82. http://dx.doi.org/10.1080/07421222.1987.11517786.

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21

Olewnik, Andrew, and Kemper Lewis. "A decision support framework for flexible system design." Journal of Engineering Design 17, no. 1 (January 2006): 75–97. http://dx.doi.org/10.1080/09544820500274019.

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22

Kim, Duck Young, and Paul Xirouchakis. "CO2DE: a decision support system for collaborative design." Journal of Engineering Design 21, no. 1 (February 2010): 31–48. http://dx.doi.org/10.1080/09544820802132444.

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23

Floyd Lewis, L., and Ken S. Keleman. "Issues in Group Decision Support System (GDSS) design." Journal of Information Science 14, no. 6 (December 1988): 347–54. http://dx.doi.org/10.1177/016555158801400605.

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24

Liu, Elim, Shih-Wen Hsiao, and Shih-Wei Hsiao. "A decision support system for product family design." Information Sciences 281 (October 2014): 113–27. http://dx.doi.org/10.1016/j.ins.2014.04.039.

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25

Ramesh, Balasubramaniam, and Kishore Sengupta. "Multimedia in a design rationale decision support system." Decision Support Systems 15, no. 3 (November 1995): 181–96. http://dx.doi.org/10.1016/0167-9236(94)00060-6.

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26

Syah, Arridha Zikra. "TSUKAMOTO FUZZY METHOD IN DECISSON SUPPORT SYSTEM DESIGN." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 7, no. 1 (December 21, 2020): 83–88. http://dx.doi.org/10.33330/jurteksi.v7i1.780.

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Abstract: PT Pinus Merah Abadi is one of the distributor companies in Indonesia engaged in selling snacks such as snacks and wafers with the Nabati brand. Every month the company offers increased incentives for its employees with predetermined conditions or targets. But in the process, the incentive calculation is managed manually using criteria by the General Affair Personnel who then data the results of the manual calculation are sent to the central office to obtain the disbursement of funds. Sometimes the results of these decisions are too rigid. The method used to solve this problem is the RAD method and each stage is adjusted accordingly based on the Tsukamoto fuzzy algorithm. From this study, an application of decision support systems was obtained that could support decision making to increase incentives that were more appropriate in human consideration. Keywords: decison support system design; incentive calculation; tsukamoto method Abstrak: PT Pinus Merah Abadi merupakan salah satu perusahaan distributor di Indonesia yang bergerak di bidang penjualan makanan ringan seperti snack dan wafer dengan merk nabati. Setiap bulan perusahaan menawarkan insentif yang meningkat bagi karyawannya dengan kondisi atau target yang telah ditentukan. Namun dalam prosesnya, perhitungan insentif dikelola secara manual menggunakan kriteria oleh personil urusan umum yang kemudian data hasil perhitungan manual dikirim ke kantor pusat untuk mendapatkan pencairan dana. Terkadang hasil dari keputusan ini terlalu kaku. Metode yang digunakan untuk memecahkan masalah ini adalah metode RAD dan dalam setiap tahap disesuaikan sesuai berdasarkan algoritma fuzzy Tsukamoto. Dari studi ini, sebuah aplikasi dari sistem pendukung keputusan diperoleh yang dapat mendukung pengambilan keputusan untuk meningkatkan insentif yang lebih tepat dalam pertimbangan manusia. Kata kunci: metode tsukamoto; peningkatan insentif, perancangan sistem pendukung keputusan

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27

Kiyani, Shamim, Sanaz Abasi, Zahra Koohjani, and Azam Aslani. "Technical Requirement of Clinical Decision Support system for Diabetic Patients." Frontiers in Health Informatics 9, no. 1 (May 3, 2020): 31. http://dx.doi.org/10.30699/fhi.v9i1.217.

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Introduction: Diabetes is a public health problem which is originating an increment in the demand for health services. There is an obvious gap exists between actual clinical practice and optimal patient care, Clinical decision support systems (CDSSs) have been promoted as a promising approach that targets safe and effective diabetes management. The purpose of this article is reviewing diabetes decision support systems based on system design metrics, type and purpose of decision support systems. Materials and Methods: The literature search was performed in peer reviewed journals indexed in PubMed by keywords such as medical decision making, clinical decision support systems, Reminder systems, diabetes, interface, interaction, information to 2019. This article review the diabetes decision support systems based on system design metrics (interface, interaction, and information), type and purpose of decision support system. Results: 32 of the 35 articles were decision support systems that provided specific warnings, reminders, a set of physician guidelines, or other recommendations for direct action. The most important decisions of the systems were support for blood glucose control and insulin dose adjustment, as well as 13 warning and reminder articles. Of the 35 articles, there were 21 user interface items (such as simplicity, readability, font sizes and ect), 23 interaction items (such as Fit, use selection tools, facilitate ease of use and ect. ) and 31 item information items (such as Content guidance, diagnostic support and concise and ect ).Discussion: This study identified important aspects of designing decision support system, It can be applied not only to diabetic patients but also to other decision support systems.Conclusion: Most decision support systems take into account a number of design criteria; system designers can look at design aspects to improve the efficiency of these systems. Decision support system evaluation models can also be added to the factors under consideration.

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28

de Graaf, P. M. A., G. C. van den Eijkel, H. J. L. M. Vullings, and B. A. J. M. de Mol. "A decision-driven design of a decision support system in anesthesia." Artificial Intelligence in Medicine 11, no. 2 (October 1997): 141–53. http://dx.doi.org/10.1016/s0933-3657(97)00026-2.

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29

Malmborg, Charles J. "A decision support system for automated guided vehicle system design." Applied Mathematical Modelling 16, no. 4 (April 1992): 170–80. http://dx.doi.org/10.1016/0307-904x(92)90055-8.

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30

Ramos, Juan, Lorena Bearzotti, Enrique Milani, Federico Woscoff, Mariana Gorsky, Carlos M. Carlett, Ma R. Galli, and Omar Chiotti. "Decision support systems generator for industrial companies Module I: Product design support system." Computers & Industrial Engineering 35, no. 1-2 (October 1998): 303–6. http://dx.doi.org/10.1016/s0360-8352(98)00080-1.

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31

Jiang, Ai De, Hong Yu Duan, Tai Yu Liu, and Shu Yan Wu. "Data Mining in Animal Husbandry Decision Support System." Advanced Materials Research 926-930 (May 2014): 2678–81. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2678.

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This paper based on the actual needs of the current animal husbandry information management, with reference to the actual development of the situation and the various existing information decision support system for animal husbandry management, animal husbandry production in the existing management information systems, management information systems and other prairie feed business management information system as the basic data sources in order to achieve integrated management of farm animal husbandry information and data mining objectives by using data mining techniques to construct a set of B / S structure of decision support systems. Considering some of today's more popular data mining methods, combined with the actual situation of animal husbandry decision support system to determine the practicality of the system using a prototype development methodology and life cycle the Combination. This development based on a detailed design plan, dividing each module to determine the functional modules. Each functional module with independent design principles, gives details of systems analysis and design features of each module, developed based on data mining animal husbandry decision support system.

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32

Wilson, David J., and Ronald L. Droste. "Design Considerations for Watershed Management Decision Support Systems." Water Quality Research Journal 35, no. 2 (May 1, 2000): 163–88. http://dx.doi.org/10.2166/wqrj.2000.011.

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Abstract Increasing attention is being paid to the management of water resources on a watershed basis, necessitating a cross-disciplinary approach to data collection and analysis. Traditional assessments of water quality and quantity are being joined by assessments of biology, botany, geomorphology, and anthropological subjects such as economic valuation. This integrated problem domain calls for a reassessment of the information technology tools designed to support the management process. With a comprehensive requirements analysis pulled from a survey of water resource practitioners, the functions necessary for design of a contemporary watershed management decision support system (WMDSS) are outlined and assessed in light of current tools in use today. Following a systems engineering methodology, the WMDSS requirements are analyzed and ranked in order of priority. This yields a ranking for development of tool and information functional groups to support the following assessment types: surface water quality, surface levels and flows, integration, groundwater flows/levels, rainfall/runoff modelling and time series analysis. Functional analysis then provides the architecture and data flows necessary to meet system requirements. The WMDSS functional analysis is concluded with a recommended architecture for design of such a system. This sets the foundation for follow-on work in production and validation of the system.

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33

Sebastian, Patrick, and Yann Ledoux. "Decision support systems in preliminary design." International Journal on Interactive Design and Manufacturing (IJIDeM) 3, no. 4 (September 18, 2009): 223–26. http://dx.doi.org/10.1007/s12008-009-0077-5.

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34

Lin, Nan, and He Long Yu. "Research on Database Design of Corn Decision Support System." Applied Mechanics and Materials 263-266 (December 2012): 1457–60. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1457.

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This research is about the application of decision support system in agriculture. The subject of study is the corn cultivated in Jilin province, northeast of China, and the research synthesizes expertise and experience on corn cultivation, plant protection, soil and fertilizer, and agriculture ecology from experts, integrating computer technology, principle of decision support system with corn production knowledge. The research also concerns decision support system for corn fertilization and diagnosis of insect disease and weed harming. Database is designed as the basis of decision support system, including conceptual design with object definition language, logic design and physical design. Further design for the relevant system is also inquired into.

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35

Booty, W. G., D. C. L. Lam, I. W. S. Wong, and P. Siconolfi. "Design and implementation of an environmental decision support system." Environmental Modelling & Software 16, no. 5 (July 2001): 453–58. http://dx.doi.org/10.1016/s1364-8152(01)00016-0.

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36

Chau-Hung, Wang, and Lin Tsai-Duan. "The Cognitive Based Design Process in Decision Support System." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 1 (July 2000): 281. http://dx.doi.org/10.1177/154193120004400178.

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37

Churchill, Cameron J., and Brian W. Baetz. "Development of Decision Support System for Sustainable Community Design." Journal of Urban Planning and Development 125, no. 1 (March 1999): 17–35. http://dx.doi.org/10.1061/(asce)0733-9488(1999)125:1(17).

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38

Miller, David C., and James F. Davis. "Process Design Decision Support System for Developing Process Chemistry." Industrial & Engineering Chemistry Research 39, no. 8 (August 2000): 2954–69. http://dx.doi.org/10.1021/ie990713u.

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39

Sawar, Mohammad Jamil. "Design and development of a medical decision Support System." Journal of Decision Systems 5, no. 3-4 (January 1996): 219–48. http://dx.doi.org/10.1080/12460125.1996.10511690.

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40

Buede, Dennis M., Gerald Yates, and Carl A. Weaver. "Concept design of a program manager's decision support system." IEEE Transactions on Systems, Man, and Cybernetics SMC-15, no. 4 (July 1985): 457–68. http://dx.doi.org/10.1109/tsmc.1985.6313411.

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41

Daas, Dave, Toine Hurkmans, Sietse Overbeek, and Harry Bouwman. "Developing a decision support system for business model design." Electronic Markets 23, no. 3 (December 29, 2012): 251–65. http://dx.doi.org/10.1007/s12525-012-0115-1.

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42

Ning, Bo, Jiafeng Xu, Yangming Hu, Zhanguo Ma, Man Bi, and Lingbo Liu. "An expert decision support system for sandstone acidizing design." Journal of Petroleum Exploration and Production Technology 9, no. 3 (December 10, 2018): 1927–41. http://dx.doi.org/10.1007/s13202-018-0588-3.

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43

Calori, G., G. Finzi, and C. Tonezzer. "A decision support system for air quality network design." Environmental Monitoring and Assessment 33, no. 2 (November 1994): 101–14. http://dx.doi.org/10.1007/bf00548592.

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44

Özdemirel, Nur Evin, and Ahmet ЦSatir. "Design of a decision support system for detailed scheduling." Information & Management 12, no. 5 (May 1987): 247–56. http://dx.doi.org/10.1016/0378-7206(87)90003-6.

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45

Gianni, Marco, Vittorio Bucci, and Alberto Marinò. "System simulation as decision support tool in ship design." Procedia Computer Science 180 (2021): 754–63. http://dx.doi.org/10.1016/j.procs.2021.01.323.

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46

Bulger, Dan, and Harold Hunt. "The forest management decision support system project." Forestry Chronicle 67, no. 6 (December 1, 1991): 622–28. http://dx.doi.org/10.5558/tfc67622-6.

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The focus of a decision support system is much different from Management Information Systems (MIS) and data-based "decision support systems". Decision support systems, as defined by the authors, focus on decisions and decision makers, and on information. Technology is treated as a tool and data as the raw material. In many traditional systems the focus is on the technology, and the data is the "information", while decision makers are, to some extent, externalized.The purpose of the Forest Management Decision Support System (FMDSS) project is to develop a set of software tools for creating forest management decision support systems. This set of tools will be used to implement a prototype forest management decision support system for the Plonski forest, near Kirkland Lake, Ontario.There are three critical ingredients in building the FMDSS, these are: (1) knowledge of the decision making process, (2) knowledge of the forest, and (3) the functionality of underlying support technology. The growing maturity of the underlying technology provides a tremendous opportunity to develop decision support tools. However, a significant obstacle to building FMDSS has been the diffuse nature of knowledge about forest management decision making processes, and about the forest ecosystem itself. Often this knowledge is spread widely among foresters, technicians, policy makers, and scientists, or is in a form that is not easily amenable to the decision support process. This has created a heavy burden on the project team to gather and collate the knowledge so that it could be incorporated into the function and design of the system. It will be difficult to gauge the success of this exercise until users obtain the software and begin to experiment with its use.

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47

Kultygin, Oleg P., and Irina Lokhtina. "Business intelligence as a decision support system tool." Journal of Applied Informatics 16, no. 91 (February 26, 2021): 52–58. http://dx.doi.org/10.37791/2687-0649-2021-16-1-52-58.

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The relevance of the topic considered in the article is to solve the problems of designing management decision support systems for enterprises based on business analytics technology. The research purpose is to analyze the applied methodologies during the design stage of the enterprise information system, to develop principles for using management decision support systems based on business intelligence. The problem statement is to analyze the technologies available on the market, which deal with business analyst systems, their potential use for decision support systems, and to identify the main stages of business analyst for enterprises. Business intelligence (BI) is information that can be obtained from data contained in the operational systems of a firm, enterprise, corporation, or from external sources. The BI can help the management of a company make the best decision in the chosen sphere of human activity faster, and, consequently, win the competition in the market for goods and services. A decision support system (DSS) which uses business intelligence, is an automated structure designed to assist professionals in making decisions in a complex environment and to objectively analyze a subject area. The decision support system is the result of the integration of management information systems and database management systems (DBMS). The internal development of BI is more cost-effective. The methods used are Structured Analysis and Design Technique and Object-oriented methods. The results of the research: the analysis of the possibilities was conducted and recommendations relating to the use of BI within DSS were given. Competition between BI software in business analysts reduces the cost of products created making them accessible to end-users – producers, traders and corporations.

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48

Cook, Gary J., and Monte R. Swain. "A Computerized Approach to Decision Process Tracing for Decision Support System Design." Decision Sciences 24, no. 5 (September 1993): 931–52. http://dx.doi.org/10.1111/j.1540-5915.1993.tb00497.x.

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49

Hirsch, Piotr, Michał Grochowski, and Kazimierz Duzinkiewicz. "Decision support system for design of long distance heat transportation system." Energy and Buildings 173 (August 2018): 378–88. http://dx.doi.org/10.1016/j.enbuild.2018.05.010.

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50

Ullman, David G., and Bruce D'Ambrosio. "Taxonomy for classifying engineering decision problems and support systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 9, no. 5 (November 1995): 427–38. http://dx.doi.org/10.1017/s0890060400002742.

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AbstractThe design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

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