scholarly journals AI-Based Decision System to Evaluate Scenarios of Response to the COVID-19 Pandemic. Evidence From Barcelona and Manhattan

2021 ◽  
Author(s):  
Didier Grimaldi ◽  
Hugo Arboleda ◽  
Karla Esqui
Keyword(s):  
New York ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Urban Areas ◽  
Large Scale ◽  
Social Consequences ◽  
Local Economy ◽  
Effective Response ◽  
Medium Businesses

Abstract In 2020, the COVID-19 outbreak has had severe economic and social consequences all over the planet. Traditional national health systems have been unable to predict and to provide a decision support system able to coordinate an effective response to the outbreak and to stop the rapid spread of this disease. In this current manuscript, we have decided to focus our analysis on Small and Medium Businesses (SMBs). While some SMBs have survived, many others, particularly in urban areas, have had to shut down as a direct decision of government restrictions. Our study presents a decision support system based on Artificial Intelligence which helps governments to prioritize the closures of SMBs located in a city. Indeed, the decision to shut down may vary according to the relative danger that the business premises represent as a social gathering point and its benefits for the local economy. In this vein, we analyse 3 different scenarios which assume different financial and social costs. The visualization of the results on a city map provides additional value for the decision-making process. The Urban Decision Support System is tested by two case studies: Barcelona and New York City. This research has implications for practitioners to support their decision to close-down the economies in the event of another large-scale outbreak. It has also research implications as new evidence that data analytics could be an additional and valuable source of information for decision support processes.

scholarly journals A Decision Support System for Public Funding of Experimental Development in Energy Research

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1357 ◽  
Author(s):  
Simon Hirzel ◽  
Tim Hettesheimer ◽  
Peter Viebahn ◽  
Manfred Fischedick
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Public Funding ◽  
Evaluation Process ◽  
Decision Makers ◽  
Market Failures ◽  
Energy Research ◽  
Experimental Development

New energy technologies may fail to make the transition to the market once research funding has ended due to a lack of private engagement to conclude their development. Extending public funding to cover such experimental developments could be one way to improve this transition. However, identifying promising research and development (R&D) proposals for this purpose is a difficult task for the following reasons: Close-to-market implementations regularly require substantial resources while public budgets are limited; the allocation of public funds needs to be fair, open, and documented; the evaluation is complex and subject to public sector regulations for public engagement in R&D funding. This calls for a rigorous evaluation process. This paper proposes an operational three-staged decision support system (DSS) to assist decision-makers in public funding institutions in the ex-ante evaluation of R&D proposals for large-scale close-to-market projects in energy research. The system was developed based on a review of literature and related approaches from practice combined with a series of workshops with practitioners from German public funding institutions. The results confirm that the decision-making process is a complex one that is not limited to simply scoring R&D proposals. Decision-makers also have to deal with various additional issues such as determining the state of technological development, verifying market failures or considering existing funding portfolios. The DSS that is suggested in this paper is unique in the sense that it goes beyond mere multi-criteria aggregation procedures and addresses these issues as well to help guide decision-makers in public institutions through the evaluation process.

Decision support system for corrective maintenance in large-scale photovoltaic systems

2021 ◽  
Author(s):  
Andreas Livera ◽  
Marios Theristis ◽  
Alexios Charalambous ◽  
Joshua S. Stein ◽  
George E. Georghiou
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Photovoltaic Systems ◽  
Corrective Maintenance

scholarly journals Decision support system for urban flood management

2005 ◽  
Vol 7 (1) ◽  
pp. 3-15 ◽  
Author(s):  
A. J. Abebe ◽  
R. K. Price
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Data Acquisition ◽  
Support System ◽  
Urban Areas ◽  
Flood Management ◽  
Urban Flood ◽  
Flood Warning ◽  
Forecast Models

This paper presents the development of a decision support system (DSS) for flood warning and instantiation of restoration activities in two urban areas, the Liguria Region in Italy and the Greater Athens catchment in Greece, with the potential of extension to other locations with similar flooding problems. The tool is designed to work at the centre of a set of meteorological and hydrologic/hydraulic forecast models together with telemetric data acquisition networks. The study reveals the complexity and uncertainty involved in managing flooding in the study areas. Issues about the validity and extended benefits of the system are also discussed.

Decision Support System for Wind Farm Installation Using Bipolar Analysis

2015 ◽  
pp. 1-21 ◽  
Author(s):  
Yasmina Bouzarour-Amokrane ◽  
Ayeley P. Tchangani ◽  
François Pérès
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Wind Energy ◽  
Support System ◽  
Large Scale ◽  
Wind Farm ◽  
Negative Impact ◽  
Life Quality ◽  
Energy Projects ◽  
And Performance

The necessity to control and reduce the negative impact of human activities on environment and life quality along with technology progress in renewable energy in general and wind energy in particular render it possible today to consider wind energy projects on a large scale. Developing wind energy on a large scale however raises other problems such as choosing an adequate site to settle a wind farm where many other issues such technical feasibility and performance levels, visual pollution, economic and social concerns, etc. must be addressed. Such decisions usually involve many parameters and necessitate the collaboration of many stakeholders. In this context, this chapter proposes an approach based on the concept of bipolar analysis through Benefit Opportunity Cost and Risk (BOCR) analysis, which permits one to address correctly a Group Decision-Making Problem (GDMP) to build a decision support system in order to assist the wind farm installation process.

Pragmatism in Decision Support System Research

2021 ◽  
pp. 1471-1493
Author(s):  
Mohammad Tafiqur Rahman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Large Scale ◽  
Humanitarian Logistics ◽  
Knowledge Based ◽  
Relief Distribution ◽  
Distribution Process ◽  
Complex Knowledge

Decision making on relief distribution is a complex multidisciplinary task in humanitarian logistics. It incorporates decision makers from different but related problem areas. The failure to perform assigned decision-making tasks in any area makes the entire system unstable and delays the relief distribution process. An organized, well-planned, and practical decision support system (DSS) can assist practitioners in making rapid decisions on delivering relief items. Hence, DSS researchers in humanitarian logistics require rigorous thinking, close and critical analysis, and the identification of challenges to conduct research or validate the generated knowledge properly. To perform such complex knowledge-based tasks, the philosophical understanding of DSS in the humanitarian context is necessary. After analyzing the commonly used philosophical paradigms, this research identifies the pragmatic approach as the adequate support for solving decision-making problems in relief distribution during large-scale disasters.

An Integrated Decision Support System for Intercropping

2010 ◽  
Vol 2 (3) ◽  
pp. 51-66 ◽  
Author(s):  
A. S. Sodiya ◽  
A. T. Akinwale ◽  
K. A. Okeleye ◽  
J. A. Emmanuel
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Crop Production ◽  
Large Scale ◽  
Weather Condition ◽  
Agricultural Practice ◽  
Pests And Diseases ◽  
General Decline ◽  
Fertility Protection

Intercropping, which is the agricultural practice of growing two or more crops in the same land area, is not currently yielding adequate results in Africa. Despite the advantages of intercropping like improved soil fertility, protection against pests and diseases and eventual increase in farm yield, this farming practice is faced with challenges—inadequate planning, bad crop management and lack of required intercropping expertise. Consequently, this has resulted in inadequate reward for farmers and a general decline in crop production. In this regard, the authors present an Intelligent and Integrated Intercropping Decision Support System for Intercropping (IDSS-I) for improved crop production. The design adopts a forecasting component that provides farmers with the estimated yield and income depending on the size of land, soil type and weather condition. Although the implementation was carried out using JAVA and SQL, usability testing revealed 85% acceptance of the tool among the contacted 10 large scale farmers. It was also confirmed that the system provided 95% diagnosis information for 90% common Africa crop diseases.

scholarly journals An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

2020 ◽  
Vol 12 (2) ◽  
pp. 259 ◽  
Author(s):  
Małgorzata Sztubecka ◽  
Marta Skiba ◽  
Maria Mrówczyńska ◽  
Anna Bazan-Krzywoszańska
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Decision Support ◽  
Geographic Information Systems ◽  
Decision Support System ◽  
Support System ◽  
Urban Areas ◽  
Renewable Energy Sources ◽  
Decision Makers ◽  
Energy Sources

Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.

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