LEADER 05440nam 2200637Ia 450 001 9910782283703321 005 20230617040932.0 010 $a1-281-92816-X 010 $a9786611928162 010 $a981-277-546-3 035 $a(CKB)1000000000537900 035 $a(EBL)1681544 035 $a(OCoLC)879025359 035 $a(SSID)ssj0000135826 035 $a(PQKBManifestationID)11155019 035 $a(PQKBTitleCode)TC0000135826 035 $a(PQKBWorkID)10063462 035 $a(PQKB)10103988 035 $a(MiAaPQ)EBC1681544 035 $a(WSP)00005283 035 $a(Au-PeEL)EBL1681544 035 $a(CaPaEBR)ebr10255854 035 $a(CaONFJC)MIL192816 035 $a(EXLCZ)991000000000537900 100 $a20030922d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDecision making and programming$b[electronic resource] /$fV.V. Kolbin ; translated from Russian by V.M. Donets 210 $aRiver Edge, N.J. $cWorld Scientific$dc2003 215 $a1 online resource (757 p.) 300 $aDescription based upon print version of record. 311 $a981-238-379-4 320 $aIncludes bibliographical references (p. 733-745). 327 $aCONTENTS; INTRODUCTION; Chapter 1 SOCIAL CHOICE PROBLEMS; 1.1. INDIVIDUAL PREFERENCE AGGREGATION; 1.1.1. Individual Preference Aggregation under Certainty; 1.1.2. Individual Preference Aggregation under Uncertainty; 1.1.3. Decision-making under Fuzzy Preference Relation on the Set of Alternatives; 1.2. COLLECTIVE PREFERENCE AGGREGATION; 1.2.1. The Procedures Using the Scale as the Auxiliary Collective Structure; 1.2.2. The Procedures Taking into Account Individual Utility Alternatives; 1.2.3. The Procedures with Exclusion of a Part of Alternatives 327 $a1.2.4. The Procedure with the Aggregating Rule Altered1.2.5. Collective Preference Aggregation; 1.3. MANIPULATION; 1.3.1. Dictation policy; 1.3.2. Methods of group manipulation; 1.3.3. Manipulation theorems and proofs; 1.4. EXAMPLES AND ALGORITHMS FOR PREFERENCE AGGREGATION; 1.4.1. Examples and Algorithm for Preference Aggregation Subject to Criterion Convolution; 1.4.2. Examples and Algorithm for Preference Aggregation in Terms of a Set of Attributes; 1.4.3. The Examples Using the Aggregating Rules during Collective Decision Making (Voting Rules); Chapter 2 VECTOR OPTIMIZATION 327 $a 2.1. DEFINITION OF UNIMPROVABLE POINTS 2.2. OPTIMIZATION OF THE HIERARCHICAL SEQUENCE OF QUALITY CRITERIA; 2.3. TRADEOFFS; I. Uniformity principles; II. Fair concession principles; III. Other optimality principles; 2.4. THE LINEAR CONVOLUTION OF CRITERIA IN MULTICRITERIA OPTIMIZATION PROBLEMS; 2.4.1. The linear convolution of criteria in multicriteria optimization problems; 2.4.2. Properties of linear convolution; 2.4.3. A geometric interpretation of linear convolution; 2.4.4. Bicriterial problems; 2.5. SOLVABILITY OF THE VECTOR PROBLEM BY THE LINEAR CRITERIA CONVOLUTION ALGORITHM 327 $a2.5.1. Test for solvability2.5.2. Solvability of trajectory problems; 2.5.3. The reduction algorithm for the solvable problem; 2.6. THE LOGICAL CRITERION VECTOR CONVOLUTION IN THE PARETO SET APPROXIMATION PROBLEM; 2.6.1. The regular case; 2.6.2. The convex case; 2.6.3. The linear case; 2.7. COMPUTATIONAL RESEARCH ON LINEAR CRITERIA CONVOLUTION IN MULTICRITERIA DISCRETE PROGRAMMING; 2.7.1 Computational complexity of multicriteria discrete optimization problems; 2.7.2. A computational experiment; 2.7.3. A problem-solving algorithm; 2.7.4. The results of computational experiment 327 $aChapter 3 INFINITE-VALUED PROGRAMMING PROBLEMS 3.1. BASIC NOTIONS AND PROPOSITIONS; 3.2. JUSTIFICATION OF NUMERICAL METHODS FOR SOLVING INFINITE-VALUED PROGRAMMING PROBLEMS; 3.3. NUMERICAL METHODS OF SOLUTION; 3.4. SEPARABLE INFINITE-VALUED PROGRAMMING PROBLEMS; 3.4.1. Existence conditions for solutions in separable infinite-valued problems; 3.4.2. Some methods for solving separable infinite-dimensional problems; Chapter 4 STOCHASTIC PROGRAMMING; 4.1. STOCHASTIC PROGRAMMING MODELS; 4.2. STOCHASTIC PROGRAMMING METHODS; 4.3. SOLUTION ALGORITHMS FOR STOCHASTIC PROGRAMMING PROBLEMS 327 $a4.3.1. Solution of a two-stage linear stochastic programming problem 330 $a The problem of selection of alternatives or the problem of decision making in the modern world has become the most important class of problems constantly faced by business people, researchers, doctors and engineers. The fields that are almost entirely focused on conflicts, where applied mathematics is successfully used, are law, military science, many branches of economics, sociology, political science, and psychology. There are good grounds to believe that medicine and some branches of biology and ethics can also be included in this list. Modern applied mathematics can produce solutions to 606 $aDecision making$xMathematical models 606 $aComputer programming$xDecision making 615 0$aDecision making$xMathematical models. 615 0$aComputer programming$xDecision making. 676 $a519.7 700 $aKolbin$b V. V$g(Vi?acheslav Viktorovich),$f1941-$01563179 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782283703321 996 $aDecision making and programming$93831371 997 $aUNINA