52 research outputs found
A Multicriteria Intelligence Aid Methodology Using MCDA, Artificial Intelligence, and Fuzzy Sets Theory
No full textIntelligence is increasingly relevant today in both military and business intelligence contexts. Business executives, military, and governments have more large datasets and meet difficulties in anticipating threat/competitor future decisions. Decision anticipation is desirable because it will enhance situation understanding and then will limit the surprise effect and favor more appropriate reactions and decision-making. Generating and evaluating competitor/threat actions is a very challenging problem because of the uncertainty, incompleteness, and ambiguity associated with it. This paper extends the multicriteria decision aid (MCDA) methodology to the context of intelligence analysis and proposes the main pillars of a novel methodology called “Multicriteria Intelligence Aid” (MCIA). More specifically, this paper addresses how can we adapt MCDA to the context of intelligence analysis and how can we use existent methods and techniques from MCDA, artificial intelligence, and fuzzy sets theory to build this methodology. The paper presents the MCIA steps, which consist of (i) structuring the competitor/threat decision problem, (ii) handling imperfect data, (iii) modeling the analyst’s risk attitude, and (iv) aggregating the performance of the generated potential actions. An illustration of the methodology is provided in the military context. Results show that the novel methodology is applicable and provides interesting and valuable results.</jats:p
A multi-criteria multi-period approach for energy project selection in sustainable development context
No full textA TOPSIS multi-criteria multi-period approach for selecting projects in sustainable development context
No full text
Making Decisions in a Sustainable Development Context: A State-of-the-Art Survey and Proposal of a Multi-period Single Synthesizing Criterion Approach
No full textA hybrid Delphi multi-criteria sorting approach for polypharmacy evaluations
No full textWith the intensification of chronical disease within older people, concurrent use of different drugs (polypharmacy) is becoming increasingly frequent. However, there is no established manner to determine whether polypharmacy is appropriate or not. We propose an original method of classifying polypharmacy using a Delphi survey results and multi-criteria decision-aid methods. To do this, we provided clinicians with a list of drugs that could be potentially prescribed to the typical elderly person suffering from three diseases (diabetes, chronic obstructive pulmonary disease, and heart failure). Clinicians expressed their opinions on a 5-point Likert scale, allowing for hesitation between two or more answers. They evaluated risks, benefits, and impacts of each drug on the patient’s quality of life. We then aggregated these evaluations in order to obtain, for each drug, a multi-criteria evaluation vector representing the collective opinion of the clinicians consulted. Subsequently, ELECTRE TriC and ELECTRE Tri multi-criteria sorting methods were used to evaluate and assign the polypharmacy to one of the following three categories: appropriate, more or less appropriate, or not appropriate. The proposed approach is innovative and enables the integration of a variety of conflicting criteria in the evaluation of polypharmacy quality. It also allows clinicians to express their opinion, and their hesitation where relevant, linguistically.
A Competitive Intelligence Solution to Predict Competitor Action Using K-modes Algorithm and Rough Set Theory
No full textAbstractWe will focus in this paper on the competitive intelligence problem which deals with the competitive environment of a company. Our purpose is to predict and anticipate the action of its competitor. We are talking here about a context of reasoning under uncertainty. All existed works define the concept of competitive intelligence and propose a scheme for the competitive intelligence process and its stages, but there is no work, at the best of our knowledge, that touched the practical aspect of the field or developed a complete competitive intelligence solution that can be delivered to the decision maker, which makes the originality of our work. To motivate the research, we will address a competitive practical case in the field of telecommunications. In this paper we propose a competitive intelligence solution composed by two steps: actions association using k-modes algorithm which has the capability to deal with nominal data, and actions generation using rough set theory which has the capability to deal with inexact data and drive rules from it
A linguistic multi-criteria classification approach for the evaluation of polypharmacy quality.
No full textWith the intensification of chronical disease within older people, concurrent use of different drugs (polypharmacy) is becoming increasingly frequent. However, there is no established manner to determine whether polypharmacy is appropriate or not. We propose an original method of classifying polypharmacy using multi-criteria decision-aid methods. To do this, we provided clinicians with a list of drugs that could be potentially prescribed to the typical elderly person suffering from three diseases (diabetes, chronic obstructive pulmonary disease, and heart failure). Clinicians expressed their opinion on a 5-point Likert scale, allowing for hesitation between two or more answers. They evaluated risks, benefits, and impacts of each drug on the patient’s quality of life. We then aggregated these evaluations in order to obtain, for each drug, a multi-criteria evaluation vector representing the collective opinion of the clinicians consulted. Subsequently, ELECTRE Tri-C and ELECTRE Tri multi-criteria methods were used to evaluate and assign the polypharmacy to one of the following three categories: appropriate, more or less appropriate, or inappropriat
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