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007 cr |n|||||||||
008 130227s2013 enk o 000 0 eng d
040 _aQ2U
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019 _a1125397340
_a1136321543
020 _a9781118522509
_q(electronic bk.)
020 _a1118522508
_q(electronic bk.)
020 _a9781118522516
_q(electronic bk.)
020 _a1118522516
_q(electronic bk.)
020 _a9781299159532
_q(MyiLibrary)
020 _a1299159532
_q(MyiLibrary)
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035 _a(OCoLC)829426424
_z(OCoLC)1125397340
_z(OCoLC)1136321543
037 _a447203
_bMIL
050 4 _aT57.95
_b.D578 2013
072 7 _aBUS
_x061000
_2bisacsh
082 0 4 _a658.4/033
_223
049 _aMAIN
245 0 0 _aMulticriteria decision aid and artificial intelligence :
_blinks, theory and applications /
_cedited by Michael Doumpos and Evangelos Grigoroundis.
260 _aChichester, West Sussex, U.K. :
_bJohn Wiley,
_c2013.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
588 0 _aOnline resource; title from digital title page (viewed on Feb. 27, 2013).
505 0 0 _gMachine generated contents note:
_gpt. I
_tCONTRIBUTIONS OF INTELLIGENT TECHNIQUES IN MULTICRITERIA DECISION AIDING --
_g1.
_tComputational intelligence techniques for multicriteria decision aiding: An overview /
_rConstantin Zopounidis --
_g1.1.
_tIntroduction --
_g1.2.
_tMCDA paradigm --
_g1.2.1.
_tModeling process --
_g1.2.2.
_tMethodological approaches --
_g1.3.
_tComputational intelligence in MCDA --
_g1.3.1.
_tStatistical learning and data mining --
_g1.3.2.
_tFuzzy modeling --
_g1.3.3.
_tMetaheuristics --
_g1.4.
_tConclusions --
_tReferences --
_g2.
_tIntelligent decision support systems /
_rGloria Phillips-Wren --
_g2.1.
_tIntroduction --
_g2.2.
_tFundamentals of human decision making --
_g2.3.
_tDecision support systems --
_g2.4.
_tIntelligent decision support systems --
_g2.4.1.
_tArtificial neural networks for intelligent decision support --
_g2.4.2.
_tFuzzy logic for intelligent decision support --
_g2.4.3.
_tExpert systems for intelligent decision support --
_g2.4.4.
_tEvolutionary computing for intelligent decision support --
_g2.4.5.
_tIntelligent agents for intelligent decision support --
_g2.5.
_tEvaluating intelligent decision support systems --
_g2.5.1.
_tDetermining evaluation criteria --
_g2.5.2.
_tMulti-criteria model for IDSS assessment --
_g2.6.
_tSummary and future trends --
_tAcknowledgment --
_tReferences --
_gpt. II
_tINTELLIGENT TECHNOLOGIES FOR DECISION SUPPORT AND PREFERENCE MODELING --
_g3.
_tDesigning distributed multi-criteria decision support systems for complex and uncertain situations /
_rFrank Schultmann --
_g3.1.
_tIntroduction --
_g3.2.
_tExample applications --
_g3.3.
_tKey challenges --
_g3.4.
_tMaking trade-offs: Multi-criteria decision analysis --
_g3.4.1.
_tMulti-attribute decision support --
_g3.4.2.
_tMaking trade-offs under uncertainty --
_g3.5.
_tExploring the future: Scenario-based reasoning --
_g3.6.
_tMaking robust decisions: Combining MCDA and SBR --
_g3.6.1.
_tDecisions under uncertainty: The concept of robustness --
_g3.6.2.
_tCombining scenarios and MCDA --
_g3.6.3.
_tCollecting, sharing and processing information: A distributed approach --
_g3.6.4.
_tKeeping track of future developments: Constructing comparable scenarios --
_g3.6.5.
_tRespecting constraints and requirements: Scenario management --
_g3.6.6.
_tAssisting evaluation: Assessing large numbers of scenarios --
_g3.7.
_tDiscussion --
_g3.8.
_tConclusion --
_tAcknowledgment --
_tReferences --
_g4.
_tPreference representation with ontologies /
_rJoan Borras --
_g4.1.
_tIntroduction --
_g4.2.
_tOntology-based preference models --
_g4.3.
_tMaintaining the user profile up to date --
_g4.4.
_tDecision making methods exploiting the preference information stored in ontologies --
_g4.4.1.
_tRecommendation based on aggregation --
_g4.4.2.
_tRecommendation based on similarities --
_g4.4.3.
_tRecommendation based on rules --
_g4.5.
_tDiscussion and open questions --
_tAcknowledgments --
_tReferences --
_gpt. III
_tDECISION MODELS --
_g5.
_tNeural networks in multicriteria decision support /
_rThomas Hanne --
_g5.1.
_tIntroduction --
_g5.2.
_tBasic concepts of neural networks --
_g5.2.1.
_tNeural networks for intelligent decision support --
_g5.3.
_tBasics in multicriteria decision aid --
_g5.3.1.
_tMCDM problems --
_g5.3.2.
_tSolutions of MCDM problems --
_g5.4.
_tNeural networks and multicriteria decision support --
_g5.4.1.
_tReview of neural network applications to MCDM problems --
_g5.4.2.
_tDiscussion --
_g5.5.
_tSummary and conclusions --
_tReferences --
_g6.
_tRule-based approach to multicriteria ranking /
_rRoman Stowinski --
_g6.1.
_tIntroduction --
_g6.2.
_tProblem setting --
_g6.3.
_tPairwise comparison table --
_g6.4.
_tRough approximation of outranking and nonoutranking relations --
_g6.5.
_tInduction and application of decision rules --
_g6.6.
_tExploitation of preference graphs --
_g6.7.
_tIllustrative example --
_g6.8.
_tSummary and conclusions --
_tAcknowledgment --
_tReferences --
_tAppendix --
_g7.
_tAbout the application of evidence theory in multicriteria decision aid /
_rYves De Smet --
_g7.1.
_tIntroduction --
_g7.2.
_tEvidence theory: Some concepts --
_g7.2.1.
_tKnowledge model --
_g7.2.2.
_tCombination --
_g7.2.3.
_tDecision making --
_g7.3.
_tNew concepts in evidence theory for MCDA --
_g7.3.1.
_tFirst belief dominance --
_g7.3.2.
_tRBBD concept --
_g7.4.
_tMulticriteria methods modeled by evidence theory --
_g7.4.1.
_tEvidential reasoning approach --
_g7.4.2.
_tDS/AHP --
_g7.4.3.
_tDISSET --
_g7.4.4.
_tchoice model inspired by ELECTRE I --
_g7.4.5.
_tranking model inspired by Xu et al.'s method --
_g7.5.
_tDiscussion --
_g7.6.
_tConclusion --
_tReferences --
_gpt. IV
_tMULTIOBJECTIVE OPTIMIZATION --
_g8.
_tInteractive approaches applied to multiobjective evolutionary algorithms /
_rCarlos A. Coello Coello --
_g8.1.
_tIntroduction --
_g8.1.1.
_tMethods analyzed in this chapter --
_g8.2.
_tBasic concepts and notation --
_g8.2.1.
_tMultiobjective optimization problems --
_g8.2.2.
_tClassical interactive methods --
_g8.3.
_tMOEAs based on reference point methods --
_g8.3.1.
_tweighted distance metric --
_g8.3.2.
_tLight beam search combined with NSGA-II --
_g8.3.3.
_tControlling the accuracy of the Pareto front approximation --
_g8.3.4.
_tLight beam search combined with PSO --
_g8.3.5.
_tpreference relation based on a weighted distance metric --
_g8.3.6.
_tChebyshev preference relation --
_g8.4.
_tMOEAs based on value function methods --
_g8.4.1.
_tProgressive approximation of a value function --
_g8.4.2.
_tValue function by ordinal regression --
_g8.5.
_tMiscellaneous methods --
_g8.5.1.
_tDesirability functions --
_g8.6.
_tConclusions and future work --
_tAcknowledgment --
_tReferences --
_g9.
_tGeneralized data envelopment analysis and computational intelligence in multiple criteria decision making /
_rHirotaka Nakayama --
_g9.1.
_tIntroduction --
_g9.2.
_tGeneralized data envelopment analysis --
_g9.2.1.
_tBasic DEA models: CCR, BCC and FDH models --
_g9.2.2.
_tGDEA model --
_g9.3.
_tGeneration of Pareto optimal solutions using GDEA and computational intelligence --
_g9.3.1.
_tGDEA in fitness evaluation --
_g9.3.2.
_tGDEA in deciding the parameters of multi-objective PSO --
_g9.3.3.
_tExpected improvement for multi-objective optimization using GDEA --
_g9.4.
_tSummary --
_tReferences --
_g10.
_tFuzzy multiobjective optimization /
_rMasatoshi Sakawa --
_g10.1.
_tIntroduction --
_g10.2.
_tSolution concepts for multiobjective programming --
_g10.3.
_tInteractive multiobjective linear programming --
_g10.4.
_tFuzzy multiobjective linear programming --
_g10.5.
_tInteractive fuzzy multiobjective linear programming --
_g10.6.
_tInteractive fuzzy multiobjective linear programming with fuzzy parameters --
_g10.7.
_tInteractive fuzzy stochastic multiobjective linear programming --
_g10.8.
_tRelated works and applications --
_tReferences --
_gpt.
505 0 0 _tV
_tAPPLICATIONS IN MANAGEMENT AND ENGINEERING --
_g11.
_tMultiple criteria decision aid and agents: Supporting effective resource federation in virtual organizations /
_rNikolaos Matsatsinis --
_g11.1.
_tIntroduction --
_g11.2.
_tintuition of MCDA in multi-agent systems --
_g11.3.
_tResource federation applied --
_g11.3.1.
_tDescribing the problem in a cloud computing context --
_g11.3.2.
_tProblem modeling --
_g11.3.3.
_tAssessing agents' value function for resource federation --
_g11.4.
_tillustrative example --
_g11.5.
_tConclusions --
_tReferences --
_g12.
_tFuzzy analytic hierarchy process using type-2 fuzzy sets: An application to warehouse location selection /
_rCengiz Kahraman --
_g12.1.
_tIntroduction --
_g12.2.
_tMulticriteria selection --
_g12.2.1.
_tELECTRE method --
_g12.2.2.
_tPROMETHEE --
_g12.2.3.
_tTOPSIS --
_g12.2.4.
_tweighted sum model method --
_g12.2.5.
_tMulti-attribute utility theory --
_g12.2.6.
_tAnalytic hierarchy process --
_g12.3.
_tLiterature review of fuzzy AHP --
_g12.4.
_tBuckley's type-1 fuzzy AHP --
_g12.5.
_tType-2 fuzzy sets --
_g12.6.
_tType-2 fuzzy AHP --
_g12.7.
_tapplication: Warehouse location selection --
_g12.8.
_tConclusion --
_tReferences --
_g13.
_tApplying genetic algorithms to optimize energy efficiency in buildings /
_rEvangelos Grigoroudis --
_g13.1.
_tIntroduction --
_g13.2.
_tState-of-the-art review --
_g13.3.
_texample case study --
_g13.3.1.
_tBasic principles and problem definition --
_g13.3.2.
_tDecision variables --
_g13.3.3.
_tDecision criteria --
_g13.3.4.
_tDecision model --
_g13.4.
_tDevelopment and application of a genetic algorithm for the example case study --
_g13.4.1.
_tDevelopment of the genetic algorithm --
_g13.4.2.
_tApplication of the genetic algorithm, analysis of results and discussion --
_g13.5.
_tConclusions --
_tReferences --
_g14.
_tNature-inspired intelligence for Pareto optimality analysis in portfolio optimization /
_rGeorgios Dounias --
_g14.1.
_tIntroduction --
_g14.2.
_tLiterature review --
_g14.3.
_tMethodological issues --
_g14.4.
_tPareto optimal sets in portfolio optimization --
_g14.4.1.
_tPareto efficiency --
_g14.4.2.
_tMathematical formulation of the portfolio optimization problem --
_g14.5.
_tComputational results --
_g14.5.1.
_tExperimental setup --
_g14.5.2.
_tEfficient frontier --
_g14.6.
_tConclusion --
_tReferences.
520 _aPresents recent advances in both models and systems for intelligent decision making. Organisations often face complex decisions requiring the assessment of large amounts of data. In recent years Multicriteria Decision Aid (MCDA) and Artificial Intelligence (AI) techniques have been applied with considerable success to support decision making in a wide range of complex real-world problems. The integration of MCDA and AI provides new capabilities relating to the structuring of complex decision problems in static and distributed environments. These include the handlin.
504 _aIncludes bibliographical references and index.
590 _aJohn Wiley and Sons
_bWiley Online Library: Complete oBooks
650 0 _aMultiple criteria decision making.
650 0 _aArtificial intelligence.
650 6 _aD�ecision multicrit�ere.
650 6 _aIntelligence artificielle.
650 7 _aartificial intelligence.
_2aat
650 7 _aBUSINESS & ECONOMICS
_xStatistics.
_2bisacsh
650 7 _aArtificial intelligence
_2fast
650 7 _aMultiple criteria decision making
_2fast
700 1 _aDoumpos, Michael.
700 1 _aGrigoroudis, Evangelos.
758 _ihas work:
_aMulticriteria decision aid and artificial intelligence (Text)
_1https://id.oclc.org/worldcat/entity/E39PCGbgyX4W3D9hyKxwTRHX3P
_4https://id.oclc.org/worldcat/ontology/hasWork
776 0 8 _iPrint version:
_tMulticriteria Decision Aid and Artificial Intelligence : Links, Theory and Applications.
_dNew York : Wiley, c2013.
_w(OCoLC)820108791
_w(DLC) 2012040171
_z9781119976394.
856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781118522516
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