LEADER 05508nam 2200673 450 001 9910808645303321 005 20200520144314.0 010 $a1-119-00538-8 010 $a1-119-01522-7 035 $a(CKB)3710000000218298 035 $a(EBL)1765113 035 $a(SSID)ssj0001399540 035 $a(PQKBManifestationID)11779235 035 $a(PQKBTitleCode)TC0001399540 035 $a(PQKBWorkID)11458398 035 $a(PQKB)11405507 035 $a(OCoLC)891381663 035 $a(MiAaPQ)EBC1765113 035 $a(Au-PeEL)EBL1765113 035 $a(CaPaEBR)ebr10907547 035 $a(CaONFJC)MIL639380 035 $a(OCoLC)887507394 035 $a(PPN)192309129 035 $a(EXLCZ)993710000000218298 100 $a20140822h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplications of combinatorial optimization /$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 (449 p.) 225 1 $aMathematics and Statistics Series 300 $aDescription based upon print version of record. 311 $a1-322-08129-8 311 $a1-84821-658-0 320 $aIncludes bibliographical references and index at the end of each chapters. 327 $aCover; Title Page; Copyright; Contents; Preface; Chapter 1: Airline Crew Pairing Optimization; 1.1. Introduction; 1.2. Definition of the problem; 1.2.1. Constructing subnetworks; 1.2.2. Pairing costs; 1.2.3. Model; 1.2.4. Case without resource constraints; 1.3. Solution approaches; 1.3.1. Decomposition principles; 1.3.2. Column generation, master problem and subproblem; 1.3.3. Branching methods for finding integer solutions; 1.4. Solving the subproblem for column generation; 1.4.1. Mathematical formulation; 1.4.2. General principle of effective label generation 327 $a1.4.3. Case of one single resource: the bucket method1.4.4. Case of many resources: reduction of the resource space; 1.4.4.1. Reduction principle; 1.4.4.2. Approach based on the Lagrangian relaxation; 1.4.4.3. Approach based on the surrogate relaxation; 1.5. Conclusion; 1.6. Bibliography; Chapter 2: The Task Allocation Problem; 2.1. Presentation; 2.2. Definitions and modeling; 2.2.1. Definitions; 2.2.2. The processors; 2.2.3. Communications; 2.2.4. Tasks; 2.2.5. Allocation types; 2.2.5.1. Static allocation; 2.2.5.2. Dynamic allocation; 2.2.5.3. With or without pre-emption 327 $a2.2.5.4. Task duplication2.2.6. Allocation/scheduling; 2.2.7. Modeling; 2.2.7.1. Modeling costs; 2.2.7.2. Constraints; 2.2.7.3. Objectives of the allocation; 2.2.7.3.1. Minimizing the execution duration; 2.2.7.3.2. Minimizing the global execution and communication cost; 2.2.7.3.3. Load balancing; 2.3. Review of the main works; 2.3.1. Polynomial cases; 2.3.1.1. Two-processor cases; 2.3.1.2. Tree case; 2.3.1.3. Other structures; 2.3.1.4. Restrictions on the processors or the tasks; 2.3.1.5. Minmax objective; 2.3.2. Approximability; 2.3.3. Approximate solution; 2.3.3.1. Heterogenous processors 327 $a2.3.3.2. Homogenous processors2.3.4. Exact solution; 2.3.5. Independent tasks case; 2.4. A little-studied model; 2.4.1. Model; 2.4.2. A heuristic based on graphs; 2.4.2.1. Transformation of the problem; 2.4.2.2. Modeling; 2.4.2.3. Description of the heuristic; 2.5. Conclusion; 2.6. Bibliography; Chapter 3: A Comparison of Some Valid Inequality Generation Methods for General 0-1 Problems; 3.1. Introduction; 3.2. Presentation of the various techniques tested; 3.2.1. Exact separation with respect to a mixed relaxation; 3.2.2. Approximate separation using a heuristic 327 $a3.2.3. Restriction + separation + relaxed lifting (RSRL)3.2.4. Disjunctive programming and the lift and project procedure; 3.2.5. Reformulation-linearization technique (RLT); 3.3. Computational results; 3.3.1. Presentation of test problems; 3.3.2. Presentation of the results; 3.3.3. Discussion of the computational results; 3.4. Bibliography; Chapter 4: Production Planning; 4.1. Introduction; 4.2. Hierarchical planning; 4.3. Strategic planning and productive system design; 4.3.1. Group technology; 4.3.2. Locating equipment; 4.4. Tactical planning and inventory management 327 $a4.4.1. A linear programming model for medium-term planning 330 $aCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization.Concepts of Combinatorial Optimization, is divided into three parts:- On the complexity of combinatorial optimization problems, presenting basics about worst-case and randomi 410 0$aOregon State monographs.$pMathematics and statistics series. 606 $aCombinatorial optimization 615 0$aCombinatorial optimization. 676 $a519.64 702 $aPaschos$b Vangelis Th 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910808645303321 996 $aApplications of combinatorial optimization$94022730 997 $aUNINA