LEADER 05623nam 2200697 450 001 9910132156103321 005 20200520144314.0 010 $a1-119-01519-7 010 $a1-119-00535-3 010 $a1-119-01516-2 035 $a(CKB)3710000000218296 035 $a(EBL)1765110 035 $a(SSID)ssj0001413322 035 $a(PQKBManifestationID)11821939 035 $a(PQKBTitleCode)TC0001413322 035 $a(PQKBWorkID)11416953 035 $a(PQKB)10294878 035 $a(OCoLC)891381639 035 $a(MiAaPQ)EBC1765110 035 $a(Au-PeEL)EBL1765110 035 $a(CaPaEBR)ebr10907543 035 $a(CaONFJC)MIL637338 035 $a(OCoLC)887507243 035 $a(PPN)192309145 035 $a(EXLCZ)993710000000218296 100 $a20140822h20142014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aParadigms of combinatorial optimization $eproblems and new approaches /$fedited by Vangelis Th. Paschos 205 $aRevised and updated second edition. 210 1$aLondon, [England] ;$aHoboken, New Jersey :$cISTE :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (815 p.) 225 1 $aMathematics and Statistics Series 300 $aDescription based upon print version of record. 311 $a1-84821-657-2 320 $aIncludes bibliographical references and index at the end of each chapters. 327 $aCover; Title Page; Copyright; Contents; Preface; PART I: Paradigmatic Problems; Chapter 1: Optimal Satisfiability; 1.1. Introduction; 1.2. Preliminaries; 1.2.1. Constraint satisfaction problems: decision and optimization versions; 1.2.2. Constraint types; 1.3. Complexity of decision problems; 1.4. Complexity and approximation of optimization problems; 1.4.1. Maximization problems; 1.4.2. Minimization problems; 1.5. Particular instances of constraint satisfaction problems; 1.5.1. Planar instances; 1.5.2. Dense instances; 1.5.3. Instances with a bounded number of occurrences 327 $a1.6. Satisfiability problems under global constraints 1.7. Conclusion; 1.8. Bibliography; Chapter 2: Scheduling Problems; 2.1. Introduction; 2.2. New techniques for approximation; 2.2.1. Linear programming and scheduling; 2.2.1.1. Single machine problems; 2.2.1.2. Problems with m machines; 2.2.2. An approximation scheme for P||Cmax; 2.3. Constraints and scheduling; 2.3.1. The monomachine constraint; 2.3.2. The cumulative constraint; 2.3.3. Energetic reasoning; 2.4. Non-regular criteria; 2.4.1. PERT with convex costs; 2.4.1.1. The equality graph and its blocks; 2.4.1.2. Generic algorithm 327 $a2.4.1.3. Complexity of the generic algorithm 2.4.2. Minimizing the early-tardy cost on one machine; 2.4.2.1. Special cases; 2.4.2.2. The lower bound; 2.4.2.3. The branch-and-bound algorithm; 2.4.2.4. Lower bounds in a node of the search tree; 2.4.2.5. Upper bound; 2.4.2.6. Branching rule; 2.4.2.7. Dominance rules; 2.4.2.8. Experimental results; 2.5. Bibliography; Chapter 3: Location Problems; 3.1. Introduction; 3.1.1. Weber's problem; 3.1.2. A classification; 3.2. Continuous problems; 3.2.1. Complete covering; 3.2.2. Maximal covering; 3.2.2.1. Fixed radius; 3.2.2.2. Variable radius 327 $a3.2.3. Empty covering 3.2.4. Bicriteria models; 3.2.5. Covering with multiple resources; 3.3. Discrete problems; 3.3.1. p-Center; 3.3.2. p-Dispersion; 3.3.3. p-Median; 3.3.3.1. Fixed charge; 3.3.4. Hub; 3.3.5. p-Maxisum; 3.4. On-line problems; 3.5. The quadratic assignment problem; 3.5.1. Definitions and formulations of the problem; 3.5.2. Complexity; 3.5.3. Relaxations and lower bounds; 3.5.3.1. Linear relaxations; 3.5.3.2. Semi-definite relaxations; 3.5.3.3. Convex quadratic relaxations; 3.6. Conclusion; 3.7. Bibliography; Chapter 4: Mini Max Algorithms and Games; 4.1. Introduction 327 $a4.2. Games of no chance: the simple cases 4.3. The case of complex no chance games; 4.3.1. Approximative evaluation; 4.3.2. Horizon effect; 4.3.3. ?-? pruning; 4.4. Quiescence search; 4.4.1. Other refinements of the Mini Max algorithm; 4.5. Case of games using chance; 4.6. Conclusion; 4.7. Bibliography; Chapter 5: Two-dimensional Bin Packing Problems; 5.1. Introduction; 5.2. Models; 5.2.1. ILP models for level packing; 5.3. Upper bounds; 5.3.1. Strip packing; 5.3.2. Bin packing: two-phase heuristics; 5.3.3. Bin packing: one-phase level heuristics 327 $a5.3.4. Bin packing: one-phase non-level heuristics 330 $aCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.Concepts of Combinatorial Optimization, is divided into three parts:- On the complexity of combinatorial optimization problems, presenting basics about worst-case and random 410 0$aOregon State monographs.$pMathematics and statistics series. 606 $aCombinatorial optimization 606 $aProgramming (Mathematics) 615 0$aCombinatorial optimization. 615 0$aProgramming (Mathematics) 676 $a519.64 702 $aPaschos$b Vangelis Th 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910132156103321 996 $aParadigms of combinatorial optimization$92259733 997 $aUNINA