A multicriteria analysis decision support system

One of the major accompaniments of the globalization is the rapid growing of urban areas. At the end of the 1970th only 38% of world lived in cities, this number increased to more than 50% by 2008. In 2030 two third of all people worldwide are expected to live in cities, many of them in megacities. Urban sprawl is a major environmental concern affecting cities and urban. Urban sprawl depends on the socio-economic situation in the cities. Thus, reducing migration, sustainable handling of the limited resources and smart growth are acknowledged as key tasks for urban planning. Coping with these tasks requires precise and adaptive planning instruments. The presented study is part of the research project GAUS (Gaining Additional Urban Space) aiming at inventorying the open space available in urban environments and, moreover, providing flexible multi-criteria spatial decision support system for its development. The method is based on VHR (Very high resolution) optical satellite data (QuickBird (QB) and IKONOS (IK)) which is applied on three study areas: Berlin, Istanbul, and Ruhr Area. Object-based image analysis is applied to map land cover and land use and derive metrics describing urban form and inner-urban structure on multiple scales. The workflow has been standardized and leads to comparable results across different test sites and datasets. In intersection with available GIS (Geographical Information System) and local ancillary data, the outputs of image analysis serve as input for a multi-criteria spatial decision support system. Flexible multi-criteria spatial decision support (MC-SDSS) tool has been created by using MATLAB (Matrix Laboratory) software and its tools (Mapping toolbox etc.). Users can change their weights and parameters with this tool for their different study areas. Urban planners can use final suitability maps of this tool. Thus complex decisions are supported by numerical calculation and spatial visualization in order to come to objective solutions. This work contribute to close the gap between remote sensing methods and applied urban planning. © 2011 IEEE.

ABSTRACTWith the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.

A proposed Multi Criteria Analysis decision support tool for international environmental policy issues: a pilot application to emissions control in the international aviation sector

Part 1 Theoretical and conceptual issues: spatial multicriteria decision analysis developing web-based GIS/MCE - improving access to data and spatial decision support tools spatial dimensions in multicriteria analysis. Part 2 Locational decision-making and analysis: equitable approaches to location problems application of a multiple criteria trade-off approach to spatial decision-making GDSS in highway planning - their use and adoption urban land management in transition economies - a decision support prototype for Shanghai the strategic identification of suitable land for low income housing - a case study from South Africa South Africa patterns of inequality and reform relating to socio-economic status and access to services. Part 3 Systemic decision-making and analysis: apres moi le deluge - multicriteria problems in multiregional water resources decision-making distributed knowledge, distributed processing, distributed users - emerging methodologies and case based reasoning for decision-making using traditional environmental knowledge and GIS geographic tools for decision-making in watershed management GIS-approach for land suitability assessment in developing countries - the case study of forest development project in Mexico exploring the potential of multi-criteria spatial decision support systems - a system for sustainable land-use planning and design.

BackgroundIn the energy transition framework towards decarbonization, green hydrogen, obtained through water electrolysis powered by renewable energy, is gaining importance. In order to pave the way to its production and trade, it is required to assess its main advantages and challenges, which are not only energy-related but involve also techno-economic, social, environmental, and geopolitical aspects. In line with this, the current article aims to provide a Multi-criteria Spatial Decision Support System to investigate the suitability of North African countries with respect to the production of solar-based hydrogen and its potential trade, and to apply it to Tunisia, as one of the most promising countries for becoming a competitive hydrogen exporting leader. Combining the Analytic Hierarchy Process and the Geographical Information System, this study focuses on evaluating the land suitability for solar hydrogen production at a country level, serving as the foundation for a methodology applicable across the entire North African region. After defining ten different criteria, these are spatially analysed and then prioritized according to different experts’ preferences, so that a final suitability map is obtained. The added value of the study is the inclusion of social and geopolitical criteria in this kind of assessments, often focused on techno-economic parameters alone.ResultsThe suitability map allows to classify the majority of the Tunisian areas as moderately or highly suitable, even if the most favourable areas in terms of availability of resources are often negatively influenced by the geopolitical or economic assessment. The sensitivity analysis has also proved the high suitability of Tunisia, with no areas assessed as very low suitable even if the different criteria are extremized.ConclusionsAmong the several influencing factors addressing the suitability for green hydrogen uptake, this article makes it possible to explore the social and geopolitical externalities, as well as the environmental and techno-economic dimensions. Even if stakeholders’ preferences affect the final results, the sensitivity analysis makes it possible to test their robustness. Supporting the adoption of new clean technologies towards the carbon-neutrality target, the methodological framework could be applied for other countries and also tailored on other specific technological pathways.

The Decision Support Systems (DSS) were widely applied to the field of Multi-Criteria Decision Aid (MCDA). In fact, the most popular MCDA aggregating procedures are those supported by a DSS. Our literature review reveals that the MultiCriteria Decision Support Systems(MCDSS) have applied in several fields such as : Production and Operation Management , Finance, Education, Human Resources, Real Estate and Multi Media. The aim of this paper is to provide a state-of-the Art and a classification by field of MCDSS applications.

Every design engineer has an experience of facing common problems of short lead times, clear specification, sufficient design criteria, material costing, environmental regulations, and product reliability constraints. However, for maintaining sustainable growth of a company, a new product should be time-in-market that keeping business growth strategy. Under such environment, it is clear that more comprehensive multi-criteria decision support systems are required to assist the designer to develop green product to achieve a goal in short period of time. This highlights the need for a more effective smart system that will collect, analyze and provide solution to design engineer. Overall, a variety of artificial intelligent (AI), multi-criteria decision support (MCDS) system, expert system, design for environment (DfE) methods are available, however they limited in their application to solve the life cycle assessment (LCA) problems and design problems in early stage of design with uncertain data in early stage of design. By examination the research focus, this study aims an understanding of environmental impact and how far from design criteria. Furthermore, investigating the LCA of green product and generating fundamental research information on their environmental impact and product reliability. With the availability of faster and more user-friendly computers and sophistical software, it has been possible to use simulation modeling to investigate the parameter effectively. The significance of this research is that development of framework by using LCA, theory of Inventive of Problems solving (TRIZ) to measure and examine product life cycle assessment, inventive problems solver.

Almost all decisions people make are based on multiple factors or criteria. Decision makers generally pursue multiple, and often conflicting, objectives. A feasible solution that is optimum with respect to all such objectives or decision criteria almost never exists, and a satisfactorily compromised solution is generally sought. Multi-criteria decision analysis as a field of research deals with problems, theory and solution approaches directly involving multiple decision criteria. This minitrack focuses on solution approaches, technology, and systems that support decision-making under consideration of multiple decision criteria.

Multiple Criteria Decision Analysis/Making (MCDA/MCDM) is constantly increasing its presence in various scientific fields but also in new fields of its application. This expansion became possible mainly due to the integration and implementation of its methodologies in the Decision Support Systems (DSS). This enabled their successful applications in many new fields resulting in their further expansion as Multi-Criteria Decision Support Systems (MCDSS). Their combination with Artificial Intelligence (intelligent agents, recommender systems, expert systems) in general and especially in recent years with the field of Machine Learning and Data Mining has led to the development of Intelligent Multi-Criteria Decision Support Systems (IMCDSS) with enormous application in analytics. At the same time, MCDA was used to solve various problems and to develop new methodologies in the field of Machine Learning. The aim of this speech is to present on the one hand the theoretical issues and methodologies of the cooperation of the Multi-Criteria DSS with the Artificial Intelligence and on the other hand to present a series of such IMCDSS and their applications in various fields such as marketing, health, defense, agricultural, etc.

Intelligent multicriteria decision support: Overview and perspectives

A number of multicriteria decisions must be made during construction investment processes. A number of support systems for multicriteria tasks of construction investment processes are available. Part of them is reviewed in this article. Effectiveness of the construction investment process is greatly influenced by an appropriate formulation of a construction contract. In order to formulate effective construction contract, multicriteria techniques for evaluation and comparison of the contracts must be created. Beside technical, organisational and economic aspects of construction, legal aspects of a construction contract must be also analysed in order to create such techniques. Therefore legal decision‐making systems are also reviewed in the article. A conclusion can be made from the review that decision‐making systems for construction contracts are not available currently. One of the main tasks in the creation of multicriteria support systems is the formation of a multicriteria indicator system. In this paper the structure of construction contracts are analysed and a hierarchical model of a construction contract is presented. Construction contracts have to be made evaluated and compared to this model. On the basis of this model, significance of indicators should be determined and a multicriteria decision support system should be created in further research stages.

This paper presents a fuzzy multicriteria group decision making approach for evaluating and selecting information systems projects. The inherent subjectiveness and imprecision of the evaluation process is modeled by using linguistic terms characterized by triangular fuzzy numbers. A new algorithm based on the concept of the degree of dominance is developed to avoid the complex and unreliable process of comparing fuzzy numbers usually required in fuzzy multicriteria decision making. A multicriteria decision support system is proposed to facilitate the evaluation and selection process. An information systems project selection problem is presented to demonstrate the effectiveness of the approach.KeywordsInformation systems project selectionMulticriteria decision makingFuzzy numbersDecision support systems

Decider: A fuzzy multi-criteria group decision support system

The recruitment of permanent Lecturers for higher education is a major challenge for all universities. Given the absence of defined hiring criteria, this article will suggest some and support the recruitment committees at the Ministry of Higher Education in Madagascar. Three methods will be implemented in a decision support system. Each recruitment committee member will assess the importance of each criterion. The Rank Sum weight method is used to determine the weightings of the criteria. The Combined Compromise for Ideal Solution (CoCoFISo) method ranks the candidates. The Minimum of rank (MIRA) method will aggregate the several rankings of candidates. Three fundamental criteria were selected: experience in university teaching, time elapsed since obtaining the teacher's opinion, and number of research publications. These criteria were assessed according to their importance by six members of the recruitment committee. The weights assigned to the criteria vary from one criterion to another and from one decision-maker to another. Applying these weights to the CoCoFISo method resulted in six rankings of the candidates. The rankings of the candidates varied from one decision-maker to another, except for the candidate in first place and the candidate in last place. The MIRA method successfully merged these six rankings into a final ranking for each candidate. The Kendall rank correlation coefficient between the MIRA method and the six rankings obtained using the CoCoFISo method varies between 0.80 and 0.97, thus confirming the validity of the rankings. This new technique will solve the challenge of hiring temporary lecturers as permanent lecturers in Madagascar universities, thanks to the transparency of the process, and will assist the Ministry of Higher Education in its decision-making.

This chapter presents the Decision Support System (DSS) developed within the CLIC project. Involving different stakeholders in a step by-step procedure, this tool supports transparent and informed decision making on the adaptive reuse of cultural heritage. The description of the methodology will be complemented by two practical boxes, in which the implementation of the tool in the Italian city of Salerno and the Croatian city of Rijeka will be briefly presented.