Academic literature on the topic 'Decision-making management system'
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Journal articles on the topic "Decision-making management system"
Do, Myungsik. "Development of the Decision-Making System for National Highway Pavement Management." Journal of the Korean Society of Civil Engineers 34, no. 2 (2014): 645. http://dx.doi.org/10.12652/ksce.2014.34.2.0645.
Full textKOZLOVA, Valeria. "ECONOMIC DIAGNOSTICS IN THE SYSTEM OF INFORMATION SUPPORT FOR MANAGEMENT DECISION-MAKING." Herald of Khmelnytskyi National University. Economic sciences 312, no. 6(2) (December 29, 2022): 196–201. http://dx.doi.org/10.31891/2307-5740-2022-312-6(2)-33.
Full textZubovа, Lyudmila V., Eduard Viktorovich Korovin, Alexey Sergeevich Smirnov, Vladimir N. Kuzmin, and Andrey Valerievich Kurakov. "Development of Problem-Oriented Management and Decision-Making System and Optimization of Economic and Social Systems." Webology 18, SI05 (October 30, 2021): 436–51. http://dx.doi.org/10.14704/web/v18si05/web18239.
Full textGibney, Lisa A., Scott E. Hansen, and Walter E. Wright, CEM. "Emergency management: Consequence management decision making." Journal of Emergency Management 2, no. 4 (October 1, 2004): 36. http://dx.doi.org/10.5055/jem.2004.0043.
Full textAda, Şükrü, and Mohsen Ghaffarzadeh. "Decision Making Based On Management Information System and Decision Support System." European Researcher 93, no. 4 (March 15, 2015): 260–69. http://dx.doi.org/10.13187/er.2015.93.260.
Full textBalaban, Edward, Stephen B. Johnson, and Mykel J. Kochenderfer. "Unifying System Health Management and Automated Decision Making." Journal of Artificial Intelligence Research 65 (August 7, 2019): 487–518. http://dx.doi.org/10.1613/jair.1.11366.
Full textYu., Tararico, and Lukashuk V. "Intellectual decision-making technology in agricultural production." Artificial Intelligence 27, jai2022.27(1) (June 20, 2022): 219–28. http://dx.doi.org/10.15407/jai2022.01.219.
Full textChen, Yi Lin. "Risk Decision-Making System in Manufacture Enterprise Management." Advanced Materials Research 694-697 (May 2013): 3592–95. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.3592.
Full textAwulor, Rita Ifeyinwa, Rhino Obi-Mallam, and Nnnena Mary Chukwu. "Enhancing organisational decision-making through management information system." Journal of Global Social Sciences 3, no. 11 (September 1, 2022): 115–33. http://dx.doi.org/10.31039/jgss.v3i11.71.
Full textBarr, Thomas R. "Critical Decision Making: The Decision to Deploy a Clinical Management System." Journal of Oncology Practice 1, no. 2 (July 2005): 71–74. http://dx.doi.org/10.1200/jop.2005.1.2.71.
Full textDissertations / Theses on the topic "Decision-making management system"
Костюченко, Надія Миколаївна, Надежда Николаевна Костюченко, Nadiia Mykolaivna Kostiuchenko, Денис Олегович Смоленніков, Денис Олегович Смоленников, and Denys Olehovych Smolennikov. "Institutional decision-making in environmental management system." Thesis, Вид-во СумДУ, 2010. http://essuir.sumdu.edu.ua/handle/123456789/8144.
Full textRezgui, Abdelkerim [Verfasser]. "Decision Evaluation System : Towards Sustainable Decision-Making / Abdelkerim Rezgui." Aachen : Shaker, 2018. http://d-nb.info/1188550578/34.
Full textTang, Yu-wen S. M. Massachusetts Institute of Technology. "Tradespace as a decision making tool in bioprocess design." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107362.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 80-83).
The field of systems engineering upholds that fundamental engineering principles exist and are applicable across different domains and contexts. In this thesis, a state-of-the art decision and design evaluation method developed for aerospace systems, Multi-Attribute Tradespace Exploration (MATE) is complemented with Design of Experiments (DoE) and applied for the first time to a bioprocess design problem. The implementation of DoE was necessary due to the high complexity of bioprocess systems, where a design variable (or a reasonably small number of design variables) cannot be easily identified to explain a given attribute of the product or process. DoE not only allows the identification of design variables that most influence a given attribute, but also allows the development of Single-Utility-Functions facilitating the incorporation of the Multi- Utility component of the MATE method. The proposed new MATE-DoE method was implemented in two case studies to assess its applicability; namely bio-production of DHA and bio-production of a lipase enzyme. Based on published DoE experimental results, utility functions and cost estimations were carried out to develop a Tradespace. The resulting Tradespace demonstrates: (a) the possible implementation of the proposed method, (b) that the use of Tradespace complements the traditional bioprocess development practice by allowing decision makers to choose an architecture that optimizes for more than one objective (multi-objective), (c) that the proposed method takes into consideration the complex decision making process of customers (multi-attribute), and (d) that simultaneous comparison analysis to competitors and market standards are possible using the method. While the method was proven to be applicable, it is relatively complex and the number of experiments and market data required might prevent its broad implementation. Also, potential errors and misleading results might result from inaccurate input data. Special attention and effort need to be put in accurate Single-Utility Function (SUF) weight designation to avoid this problem. The importance of assessing the complete bioprocess, as opposed to individual unit operations, is highlighted. Finally, further studies to develop "rules of thumb" in order to simplify the proposed MATE-DoE method is suggested.
by Yu-wen Tang.
S.M. in Engineering and Management
Devine, Paul (Paul S. ). "Reliability improvement project decision making : water cooling system redesign." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/35114.
Full textIncludes bibliographical references (p. 66).
Deciding on which reliability & performance improvement projects to launch or to reject has historically been an extremely challenging responsibility of Teradyne management. Incorrect decisions can lead to major customer dissatisfaction, which may subsequently lead to loss of market share. Teradyne Engineering and Marketing team have been trying to develop a tool that would assist in their reliability improvement project decision making. The challenge is the dynamic aspects of the reliability improvement projects. Like most engineering projects, reliability improvement projects have variables such as internal workforce, productivity, skill sets, customer expectations and many others that are in constant motion. These variables make the assessment of reliability projects extremely difficult in a static framework. This research will incorporate these key variables into a dynamic framework to help assess individual reliability improvement projects.
by Paul Devine.
S.M.
Chacon, Vince. "Executive decision making processes and outcomes : structure and robustness." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29536.
Full textIncludes bibliographical references (p. 48).
Uncertainty in the decision making environment complicates the decision making process because future events may change the effect of a particular decision or series of decisions. This thesis explores the possibility of applying robust engineering design techniques to the decision making process in order to limit the effects of changing circumstances. The intent is to identify solutions that will reduce the variation in the outcome of decisions that are made across many projects by analyzing projects that have been executed at the Dryden Flight Research Center (DFRC) over the past several years. A framework to relate past performance to match the requirements of experiments in a Design of Experiments (DOE) analysis is developed. The approach views factors that are considered in making decisions as controllable elements and factors that unexpectedly affect the outcome of the decisions as noise. The resulting framework is then organized such that the data can be analyzed using the Taguchi approach to DOE, which has been successfully used for analyzing engineering design and manufacturing processes. The analysis approach considers the robustness of the outcome based on the factors used to make the decisions about the various projects that have been conducted at DFRC over the past six years. The decision process performance is analyzed and recommendation are made to improve the performance of the decision making process at DFRC. The analysis indicates that projects providing large increases in technical knowledge were the most influential in reducing the effects from changes in budget and staffing resources that were beyond the control of the decision makers.
by Vince Chacon.
S.M.
Nilekar, Shirish K. "A system-oriented analysis of team decision making in data rich environments." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/90698.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 78-80).
The information processing view of organizations [1] and subsequent works highlight the primary role of information processing in the effective functioning of markets and organizations. With the current wave of "big data" and related technologies, data-oriented decision making is being widely discussed [2] as a means of using this vast amount of available data for better decisions which can lead to improved business results. The focus of many of these studies is at the organization level. However, decisions are made by teams of individuals and this is a complex socio-technical process. The quality of a decision depends on many factors including technical capabilities for data analysis and human factors like team dynamics, cognitive capabilities of the individuals and the team. In this thesis, we developed a systems theory based framework for decision making and identified four socio technical factors viz., data analytics, data sensing, power distribution, and conflict level which affect the quality of decisions made by teams. We then conducted "thought experiments" to investigate the relative contribution of each of these factors to the quality of decisions. Our experiments and subsequent analyses show that while improved data analytics does result in better decisions, human factors have an out-sized contribution to the quality of decisions, even in data rich environments. Moreover, when the human factors in a team improve, the predictability of the positive impacts due to improvements in technical capabilities of the team also increases.
by Shirish K. Nilekar.
S.M. in Engineering and Management
Wang, Ming-hua. "A knowledge-based system approach for project management decision-making support." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340476.
Full textQueiroz, Vieira Turnell M. de F. "A decision making aid system based on a small microprocessor." Thesis, University of Bradford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379855.
Full textXu, Hua S. M. Massachusetts Institute of Technology. "A system approach to augment clinical decision-making using machine learning." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121803.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 76-80).
This thesis helps find limits within which automated methods can support and surpass the capabilities of medical professionals and the limits beyond which these methods are not yet adequate. This will inform later exploration about (a) what improvements in data collection, interpretation, and visualization will enhance technology's capacity and (b) what changes clinicians can make to improve their decision making-augmented or not. This thesis includes (a) describing clinical decisions, informed by literature and clinical case studies and (b) reviewing current capabilities of machine methods. This led to (c) a test experiment-how to use data about a particular condition (e.g. in-hospital mortality rate prediction) from a particular source (the MIMIC III data base). The results will help define current limits on augmenting clinical decisions and establish direction for future work including more demanding experiments.
Artificial Intelligence (AI) includes Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, Speech Recognition, and Robotics. As an important branch of Al, ML builds statistical models to learn from sample data, known as "training", identifies patterns, and makes predictions based on new data, known as "inference." In this way, ML is useful in rationalizing and predicting in uncertain environments, with minimum human interventions. Decision making is central to the healthcare practice, with many decisions made under conditions of uncertainty. Clinicians must integrate a huge variety of data while pressured to decrease diagnostic uncertainties and risks to patients. Deciding what information to gather, which test to order, how to interpret and integrate this information to draw diagnostic conclusions, and which treatments to give are essential.
In typical situations, clinicians evaluate patient symptoms and potential disease patterns, confirmed by a variety of tests, and they initiate treatments based on their experience and customary practice. This is complicated when multiple illnesses coexist, the illness may be rare, the information may be conflicting, or prior interventions may affect the presenting symptoms.
by Hua Xu.
S.M. in Engineering and Management
S.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Program
Barton, John Edward Built Environment Faculty of Built Environment UNSW. "A spatial decision support system for the management of public housing." Awarded by:University of New South Wales, 2007. http://handle.unsw.edu.au/1959.4/35209.
Full textBooks on the topic "Decision-making management system"
Nadia, Nedjah, and Macedo Mourelle Luiza de, eds. Real-world multi-objective system engineering. New York: Nova Science, 2005.
Find full textMancini, Daniela. Accounting Information Systems for Decision Making. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Find full textBrailsford, Sally, Leonid Churilov, and Brian Dangerfield, eds. Discrete-Event Simulation and System Dynamics for Management Decision Making. Chichester, UK: John Wiley & Sons Ltd, 2014. http://dx.doi.org/10.1002/9781118762745.
Full textWilimowska, Zofia, Leszek Borzemski, Jerzy Świątek, and Adam Grzech. Information systems architecture and technology: System analysis in decision aided problems. Wrocław: Wrocław University of Technology, 2009.
Find full textFlood, Robert L. Liberating systems theory. New York: Plenum Press, 1990.
Find full textManaging with systems thinking: Making dynamics work for you in business decision making. London: McGraw-Hill, 1994.
Find full textWang, Ming-hua. A knowledge-based system approach for project management decision-making support. [s.l.]: typescript, 1997.
Find full textW, Morecroft John D., Sanchez Ron, and Heene Aimé, eds. Systems perspectives on resources, capabilities, and management processes. Amsterdam: Pergamon, 2002.
Find full textV, Gheorghe Adrian, and SpringerLink (Online service), eds. Quality Decision Management - The Heart of Effective Futures-Oriented Management: A Primer for Effective Decision-Based Management. Dordrecht: Springer Netherlands, 2009.
Find full textCoplin, William D. Power persuasion: A surefire system to get ahead in business. Reading, Mass: Addison-Wesley, 1985.
Find full textBook chapters on the topic "Decision-making management system"
Lenhard, Raymond E. "Data Management in Clinical Decision Making." In A Clinical Information System for Oncology, 22–38. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3638-2_2.
Full textMartin, Arnaud, Pascale Zarate, and Guy Camillieri. "A Multi-Criteria Recommender System Based on Users’ Profile Management." In Multiple Criteria Decision Making, 83–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39292-9_5.
Full textPohl, Edward. "System Reliability." In Decision Making in Systems Engineering and Management, 227–72. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470926963.ch8.
Full textDelgado-Álvarez, Carlos A., and Yris Olaya-Morales. "Modeling Disaster Operations Management Problems with System Dynamics." In Decision-making in Humanitarian Operations, 223–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91509-8_10.
Full textDriscoll, Patrick J., and Paul Kucik. "System Life Cycle." In Decision Making in Systems Engineering and Management, 65–93. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470926963.ch3.
Full textGröwe-Kuska, Nicole, Krzysztof C. Kiwiel, Matthias P. Nowak, Werner Römisch, and Isabel Wegner. "Power Management in a Hydro-Thermal System under Uncertainty by Lagrangian Relaxation." In Decision Making Under Uncertainty, 39–70. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4684-9256-9_3.
Full textJablonský, Josef. "Decision Support System for Management of Patient Nutrition: An Interactive AHP/Goal Programming Approach." In Multiple Criteria Decision Making, 135–48. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2918-6_11.
Full textGuzmán Cortés, Diana Carolina, Leonardo José González Rodríguez, and Carlos Franco. "Collaborative Strategies for Humanitarian Logistics with System Dynamics and Project Management." In Decision-making in Humanitarian Operations, 249–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91509-8_11.
Full textKanatas, P., I. Travlos, A. Tataridas, and I. Gazoulis. "Decision-Making and Decision Support System for a Successful Weed Management." In Information and Communication Technologies for Agriculture—Theme III: Decision, 159–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-84152-2_8.
Full textJakubeit, N., M. Rajabalinejad, A. J. J. Braaksma, and L. A. M. van Dongen. "Collaborative Decision-Making Challenges in the Dutch Railway System." In Complex Systems Design & Management, 193. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34843-4_18.
Full textConference papers on the topic "Decision-making management system"
Yong Sun, Lin Ma, Warwick Robinson, and Colin Fidge. "Using decision trees in economizer repair decision making." In 2010 Prognostics and System Health Management Conference (PHM). IEEE, 2010. http://dx.doi.org/10.1109/phm.2010.5414571.
Full textRosanty, Elvira Soufyani, Halina Mohamed Dahlan, and Ab Razak Che Hussin. "Multi-Criteria Decision Making for Group Decision Support System." In 2012 International Conference on Information Retrieval & Knowledge Management (CAMP). IEEE, 2012. http://dx.doi.org/10.1109/infrkm.2012.6205015.
Full textIntezari, Ali, David J. Pauleen, and Nazim Taskin. "The DIKW Hierarchy and Management Decision-Making." In 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE, 2016. http://dx.doi.org/10.1109/hicss.2016.520.
Full textAlfajri, I., A. Ali, and N. N. Al-Zaid. "Information Management System to Improve Decision Making Efficiency." In SPE Kuwait Oil and Gas Show and Conference. Society of Petroleum Engineers, 2015. http://dx.doi.org/10.2118/175406-ms.
Full textTang, Hong, and Lindu Zhao. "Knowledge Management System of Intercity Emergency Decision Making." In 2009 WRI World Congress on Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/wcse.2009.232.
Full textWei-bing Liu and Min Li. "Dynamic decision making in social security system." In 2011 International Conference on Management Science and Industrial Engineering (MSIE). IEEE, 2011. http://dx.doi.org/10.1109/msie.2011.5707509.
Full text"Session 6.2 — Decision-making & maintainability." In 2012 Prognostics and System Health Management Conference (PHM). IEEE, 2012. http://dx.doi.org/10.1109/phm.2012.6228924.
Full textYaohui Zhang, Xiaohai Han, Shixin Zhang, and Shaohua Wang. "Decision-making methods of condition-based maintenance." In 2015 Prognostics and System Health Management Conference (PHM). IEEE, 2015. http://dx.doi.org/10.1109/phm.2015.7380098.
Full textGorelov, M. A. "Decision Making with Abundance of Information." In 2020 13th International Conference Management of large-scale system development (MLSD). IEEE, 2020. http://dx.doi.org/10.1109/mlsd49919.2020.9247780.
Full textMikštienė, Ruta, and Violeta Keršulienė. "Legal decision support system application possibility in corporate governance." In Business and Management 2016. VGTU Technika, 2016. http://dx.doi.org/10.3846/bm.2016.39.
Full textReports on the topic "Decision-making management system"
Barber, D. S., D. L. Brockman, and L. D. Buxton. Integrated Services Management System (ISMS): A management and decision making tool. Office of Scientific and Technical Information (OSTI), October 1995. http://dx.doi.org/10.2172/120884.
Full textKappes, Sandra F., Simon S. Kim, Patrick J. Tanner, Roddy J. Williams, and Louis F. Cohn. Employing Expert System Technologies to Real Property Management Decision making. Fort Belvoir, VA: Defense Technical Information Center, July 1990. http://dx.doi.org/10.21236/ada226176.
Full textBuyak, Bogdan B., Ivan M. Tsidylo, Victor I. Repskyi, and Vitaliy P. Lyalyuk. Stages of Conceptualization and Formalization in the Design of the Model of the Neuro-Fuzzy Expert System of Professional Selection of Pupils. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2669.
Full textLagutin, Andrey, and Tatyana Sidorina. SYSTEM OF FORMATION OF PROFESSIONAL AND PERSONAL SELF-GOVERNMENT AMONG CADETS OF MILITARY INSTITUTES. Science and Innovation Center Publishing House, December 2020. http://dx.doi.org/10.12731/self-government.
Full textSeale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.
Full textCallaghan, Caitlin, Matthew Bigl, Brandon Booker, Kyle Elliott, Paulina Lintsai, Marissa Torres, Kathryn Trubac, and Jacqueline Willan. Energy Atlas—mapping energy-related data for DoD lands in Alaska : Phase 1—assembling the data and designing the tool. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42226.
Full textCallaghan, Caitlin, Matthew Bigl, Brandon Booker, Kyle Elliott, Paulina Lintsai, Marissa Torres, Kathryn Trubac, and Jacqueline Willan. Energy Atlas—mapping energy-related data for DoD lands in Alaska : Phase 1—assembling the data and designing the tool. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42226.
Full textRuiz de Gauna, Itziar, Anil Markandya, Laura Onofri, Francisco (Patxi) Greño, Javier Warman, Norma Arce, Alejandra Navarrete, et al. Economic Valuation of the Ecosystem Services of the Mesoamerican Reef, and the Allocation and Distribution of these Values. Inter-American Development Bank, May 2021. http://dx.doi.org/10.18235/0003289.
Full textBrinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor-Mirghani. Adapting to Learn and Learning to Adapt: Practical Insights from International Development Projects. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.
Full textLempert, Robert J., Michelle Miro, and Diogo Prosdocimi. A DMDU Guidebook for Transportation Planning Under a Changing Climate. Edited by Benoit Lefevre and Ernesto Monter Flores. Inter-American Development Bank, February 2021. http://dx.doi.org/10.18235/0003042.
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