A5058649133- Multi-Criteria Decision Making
- Rough Sets and Fuzzy Logic
- Fuzzy Systems and Optimization
- Advanced Multi-Objective Optimization Algorithms
- Bayesian Modeling and Causal Inference
- Economic and Environmental Valuation
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Efficiency Analysis Using DEA
- Optimization and Mathematical Programming
- Data Management and Algorithms
- Advanced Statistical Methods and Models
- Sustainable Supply Chain Management
- Data Mining Algorithms and Applications
- Transportation Planning and Optimization
- Risk and Safety Analysis
- Soil and Land Suitability Analysis
- Cognitive Science and Mapping
- Global Energy Security and Policy
- Consumer Market Behavior and Pricing
- Complex Systems and Decision Making
- Aviation Industry Analysis and Trends
- Qualitative Comparative Analysis Research
- Environmental Impact and Sustainability
- Quality Function Deployment in Product Design
Poznań University of Technology
2015-2024
Decision making is a complex task that involves multitude of perspectives, constraints, and variables. Multiple Criteria Analysis (MCDA) process has been used for several decades to support decision making. It includes series steps systematically help Maker(s) (DM(s)) stakeholders in structuring problem, identifying their preferences, building recommendation consistent with those preferences. Over the last decades, many studies have demonstrated conduct MCDA how select an method. Until now,...
We present a new methodology to lead the selection of Multiple Criteria Decision Analysis (MCDA) methods. It is implemented in Methods Selection Software (MCDA-MSS), decision support system that helps analysts answer recurring question science: "Which most suitable method (or subset MCDA methods) should be used for given Decision-Making Problem (DMP)?". The MCDA-MSS provides guidance decision-making processes and choose among an extensive collection (>200) These are assessed according...
Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with recommendation concerning set alternatives (items, actions) evaluated from multiple points view, called criteria. This paper aims at drawing attention Machine Learning (ML) community upon recent advances in representative MCDA methodology, Robust Ordinal Regression (ROR). ROR learns by examples order to rank alternatives, thus considering similar problem as Preference...
Indicator-based approaches are suitable to assess multi-dimensional problems. In order compare a set of alternatives, one strategy is normalize individual indicators common scale and aggregate them into comprehensive score. This study proposes the Electricity Supply Resilience Index (ESRI), which measure nation's electricity supply resilience. Starting from an initial derived through structured selection process, ESRI calculated for 140 countries worldwide. To account robustness resulting...
The learning of predictive models for data-driven decision support has been a prevalent topic in many fields. However, construction that would capture interactions among input variables is challenging task. In this paper, we present new preference approach multiple criteria sorting with potentially interacting criteria. It employs an additive piecewise-linear value function as the basic model, which augmented components handling interactions. To construct such model from given set assignment...
Over the past few decades, strategies to perform energy systems analysis have evolved into multiple criteria-based frameworks. However, there still remains a lack of guidance on how select most suitable Multiple Criteria Decision Analysis (MCDA) method. These methods provide different decision recommendations for Makers, including ranking, sorting, choice, and clustering alternatives (e.g., technologies or scenarios) under evaluation. They deal with variety data typologies preferences, lead...