Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many arti...

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Bibliographic Details
Other Authors: Pamučar, Dragan (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
IoT
AHP
DEA
SFA
Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems. 
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653 |a underground mines 
653 |a mining methods 
653 |a expert knowledge 
653 |a failure mode and effects analysis 
653 |a solar panel systems 
653 |a step-wise weight assessment ratio analysis 
653 |a grey relational analysis 
653 |a Z-number theory 
653 |a B2C e-commerce factors 
653 |a website 
653 |a MCDM 
653 |a Fuzzy AHP 
653 |a TOPSIS-Grey 
653 |a China 
653 |a IoT 
653 |a platform selection 
653 |a multi criteria decision analysis (MCDA) 
653 |a AHP 
653 |a PROMETHEE-II 
653 |a Industry 4.0 
653 |a data envelopment analysis 
653 |a conjoint analysis 
653 |a experimental design 
653 |a criteria importance 
653 |a weight restrictions 
653 |a subjective and objective teacher efficiency 
653 |a multi-objective planning 
653 |a reverse supply chain 
653 |a robust optimization 
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653 |a meta-heuristic algorithm 
653 |a steel making industry 
653 |a fuzzy PIPRECIA 
653 |a fuzzy EDAS 
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653 |a transport policy 
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653 |a DMAIC 
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653 |a text mining 
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653 |a Prospective MADM 
653 |a Latent Semantic Analysis (LSA) 
653 |a SIMUS 
653 |a decision tree 
653 |a transport plan 
653 |a Laplace's criterion 
653 |a Hurwitz's criterion 
653 |a q-rung orthopair fuzzy numbers 
653 |a q-rung orthopair fuzzy prioritized weighted average operator 
653 |a q-rung orthopair fuzzy prioritized weighted geometric operator 
653 |a green supply chain management 
653 |a fuzzy theory 
653 |a sustainable development 
653 |a SCOR model 
653 |a FAHP 
653 |a PROMETHEE II 
653 |a textile and garments industry 
653 |a sustainable supplier selection 
653 |a MCDA 
653 |a efficiency 
653 |a DEA 
653 |a SFA 
653 |a classification 
653 |a dimensionality reduction 
653 |a q-ROFNs 
653 |a Einstein operators 
653 |a prioritized aggregation operators 
653 |a multi-criteria group decision making 
653 |a hazardous materials 
653 |a vehicle route model (VRP) 
653 |a uncertainty theory 
653 |a chance constrained programming model 
653 |a hybrid intelligent algorithm 
653 |a linear Diophantine fuzzy set 
653 |a linear Diophantine fuzzy soft rough set 
653 |a soft rough linear Diophantine fuzzy set 
653 |a upper reduct and lower reduct 
653 |a core set 
653 |a multi-criteria decision making 
653 |a q-Rung orthopair fuzzy sets 
653 |a geometric aggregation operators based on generalized and group-generalized parameters 
653 |a water loss management 
653 |a decision making 
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653 |a the COMET method 
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653 |a chance constraint 
653 |a Yangtze River Economic Belt 
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653 |a normalization 
653 |a entropy 
653 |a decision-making methods 
653 |a multi-criteria problems 
653 |a evolutionary algorithms 
653 |a machine learning 
653 |a fuzzy logic 
653 |a uncertain data 
653 |a consistency weights 
653 |a fuzzy preference relation (FPR) 
653 |a hesitant fuzzy preference relation (HFPR) 
653 |a Łukasiewicz consistency 
653 |a normal hesitant fuzzy preference relation (NHFPR) 
653 |a multiple criteria decision-making (MCDM) 
653 |a outsourcing provider 
653 |a DEMATEL 
653 |a CRITIC 
653 |a TOPSIS 
653 |a comparison measure 
653 |a representation 
653 |a disjoint 
653 |a multiplicative preference relation (MPR) 
653 |a group decision-making (GDM) 
653 |a incomplete fuzzy preference relation (IFPR) 
653 |a TL-consistency 
653 |a cubic m-polar fuzzy set 
653 |a Dombi's operations 
653 |a cubic m-polar fuzzy aggregation operators with P-order (R-order) 
653 |a SIR technique 
653 |a complex networks 
653 |a social networks 
653 |a viral marketing 
653 |a information propagation 
653 |a crisp probability 
653 |a interval probability 
653 |a influence diagrams 
653 |a circuit breakers 
653 |a granular computing 
653 |a interval-valued 
653 |a intuitionistic fuzzy set 
653 |a multiple granulation 
653 |a ordered information system 
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856 4 0 |a www.oapen.org  |u https://directory.doabooks.org/handle/20.500.12854/76785  |7 0  |z DOAB: description of the publication