Recent Developments in Applied Mathematics and Multi-Criteria Decision-Making Methods
This special issue belongs to the section “Logic“.
Special Issue Information
Dear Colleagues,
In modern applied mathematics, breakthroughs led by the new advancements in mathematics have significantly increased the analytical power used to solve complex real-world problems across engineering, management, energy systems, sustainability, and logistics. At the same time, Multi-Criteria Decision-Making (MCDM) algorithms were introduced as fundamental tools that resolve decision problems about conflicting, uncertain, or dynamic criteria. Based on an established philosophy in mathematics, MCDM has been successfully applied in engineering problem-solving, energy system design, and logistics. This Special Issue combines state-of-the-art work to combine advanced applied mathematical modelling with classical and modern MCDM approaches, such as AHP, ANP, TOPSIS, PROMETHEE, VIKOR, DEMATEL, BWM, MARCOS, and integrated hybrid models.
The objective of this Special Issue is to highlight new theoretical progress, novel algorithms, applications, and applicability studies of MCDM, in terms of sustainability assessment, energy transition, supply chain optimization, environmental management, risk evaluation, and digital transformation. Contributions involving mathematical optimization, uncertainty modelling, artificial intelligence, simulation, and life cycle thinking with MCDM are especially encouraged.
Therefore, this Special Issue aims to provide a broad-based interdisciplinary platform that extends the current body of work by linking relatively recent mathematical tools with decision-analytic paradigms. We welcome original work, methodological advances, and high-quality review papers.
Dr. Seyed Pendar Toufighi
Prof. Dr. Jan Vang
Guest Editors
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Keywords
- multi-criteria decision-making (MCDM)
- applied mathematics
- optimization models
- decision analysis
- sustainability assessment
- life cycle assessment (LCA)
- energy systems
- supply chain and logistics
- uncertainty modelling
- mathematical modelling
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