05682nam 2200733 450 991013216450332120200903223051.01-118-98424-21-118-98426-91-118-98425-0(CKB)3710000000239192(EBL)1784143(OCoLC)890981687(SSID)ssj0001375499(PQKBManifestationID)11746476(PQKBTitleCode)TC0001375499(PQKBWorkID)11360274(PQKB)10296377(MiAaPQ)EBC1784143(Au-PeEL)EBL1784143(CaPaEBR)ebr10930298(CaONFJC)MIL646253(PPN)189474521(EXLCZ)99371000000023919220140926h20142014 uy 0engur|n|---|||||txtccrGraph-related optimization and decision support systems /Saoussen Krichen, Jouhaina ChaouachiLondon, England ;Hoboken, New Jersey :ISTE :Wiley,2014.©20141 online resource (186 p.)Focus Computer Engineering Series,2051-249XDescription based upon print version of record.1-322-14998-4 1-84821-743-9 Includes bibliographical references and index.Cover page; Half-Title page; Title page; Copyright page; Contents; List of Tables; List of Figures; List of Algorithms; Introduction; 1: Basic Concepts in Optimization and Graph Theory; 1.1. Introduction; 1.2. Notation; 1.3. Problem structure and variants; 1.4. Features of an optimization problem; 1.5. A didactic example; 1.6. Basic concepts in graph theory; 1.6.1. Degree of a graph; 1.6.2. Matrix representation of a graph; 1.6.3. Connected graphs; 1.6.4. Itineraries in a graph; 1.6.5. Tree graphs; 1.6.6. The bipartite graphs; 1.7. Conclusion; 2: Knapsack Problems; 2.1. Introduction2.2. Graph modeling of the knapsack problem2.3. Notation; 2.4. 0-1 knapsack problem; 2.5. An example; 2.6. Multiple knapsack problem; 2.6.1. Mathematical model; 2.6.2. An example; 2.7. Multidimensional knapsack problem; 2.7.1. Mathematical model; 2.7.2. An example; 2.8. Quadratic knapsack problem; 2.8.1. Mathematical model; 2.8.2. An example; 2.9. Quadratic multidimensional knapsack problem; 2.9.1. Mathematical model; 2.9.2. An example; 2.10. Solution approaches for knapsack problems; 2.10.1. The greedy algorithm; 2.10.2. A genetic algorithm for the KP; 2.10.2.1. Solution encoding2.10.2.2. Crossover2.10.2.3. Mutation; 2.11. Conclusion; 3: Packing Problems; 3.1. Introduction; 3.2. Graph modeling of the bin packing problem; 3.3. Notation; 3.4. Basic bin packing problem; 3.4.1. Mathematical modeling of the BPP; 3.4.2. An example; 3.5. Variable cost and size BPP; 3.5.1. Mathematical model; 3.5.2. An example; 3.6. Vector BPP; 3.6.1. Mathematical model; 3.6.2. An example; 3.7. BPP with conflicts; 3.7.1. Mathematical model; 3.7.2. An example; 3.8. Solution approaches for the BPP; 3.8.1. The next-fit strategy; 3.8.2. The first-fit strategy; 3.8.3. The best-fit strategy3.8.4. The minimum bin slack3.8.5. The minimum bin slack'; 3.8.6. The least loaded heuristic; 3.8.7. A genetic algorithm for the bin packing problem; 3.8.7.1. Solution encoding; 3.8.7.2. Crossover; 3.8.7.3. Mutation; 3.9. Conclusion; 4: Assignment Problem; 4.1. Introduction; 4.2. Graph modeling of the assignment problem; 4.3. Notation; 4.4. Mathematical formulation of the basic AP; 4.4.1. An example; 4.5. Generalized assignment problem; 4.5.1. An example; 4.6. The generalized multiassignment problem; 4.6.1. An example; 4.7. Weighted assignment problem4.8. Generalized quadratic assignment problem4.9. The bottleneck GAP; 4.10. The multilevel GAP; 4.11. The elastic GAP; 4.12. The multiresource GAP; 4.13. Solution approaches for solving the AP; 4.13.1. A greedy algorithm for the AP; 4.13.2. A genetic algorithm for the AP; 4.13.2.1. Solution encoding; 4.13.2.2. Crossover; 4.13.2.3. Mutation; 4.14. Conclusion; 5: The Resource Constrained Project Scheduling Problem; 5.1. Introduction; 5.2. Graph modeling of the RCPSP; 5.3. Notation; 5.4. Single-mode RCPSP; 5.4.1. Mathematical modeling of the SM-RCPSP; 5.4.2. An example of an SM-RCPSP5.5. Multimode RCPSP Constrained optimization is a challenging branch of operations research that aims to create a model which has a wide range of applications in the supply chain, telecommunications and medical fields. As the problem structure is split into two main components, the objective is to accomplish the feasible set framed by the system constraints. The aim of this book is expose optimization problems that can be expressed as graphs, by detailing, for each studied problem, the set of nodes and the set of edges. This graph modeling is an incentive for designing a platform that integrates all optimizatioFocus series in computer engineering.Constrained optimizationDifferential equations, PartialTelecommunicationConstrained optimization.Differential equations, Partial.Telecommunication.519.690-0190C3590C2705C90mscKrichen Saoussen912683Chaouachi JouhainaMiAaPQMiAaPQMiAaPQBOOK9910132164503321Graph-related optimization and decision support systems2286204UNINA00962nam a22002411i 450099100179269970753620031216165648.0040407s1982 it a||||||||||||||||ita b12824197-39ule_instARCHE-080381ExLDip.to Scienze StoricheitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.386Caterina, Giulio376604Il collegamento Reno-Meno-Danubio :una nuova idrovia per l'Europa /Giulio CaterinaSalerno :Istituto di geografia dell'Universita,198262 p. :ill. ;24 cmIdrovia Reno-Meno-Danubio.b1282419702-04-1416-04-04991001792699707536LE009 GEOG.14.413-4212009000159075le009-E0.00-l- 00000.i1337517916-04-04Collegamento Reno-Meno-Danubio299549UNISALENTOle00916-04-04ma -itait 3105715nam 2201405z- 450 9910367736703321202102123-03921-965-0(CKB)4100000010106350(oapen)https://directory.doabooks.org/handle/20.500.12854/61361(oapen)doab61361(EXLCZ)99410000001010635020202102d2019 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierTropical Forest Ecology and Management for the AnthropoceneMDPI - Multidisciplinary Digital Publishing Institute20191 online resource (242 p.)3-03921-964-2 This Special Issue looks forward as well as backward to best analyze the forest conservation challenges of the Caribbean. This is made possible by 75 years of research and applications by the United States Department of Agriculture, International Institute of Tropical Forestry (the Institute) of Puerto Rico. It transforms Holocene-based scientific paradigms of the tropics into Anthropocene applications and outlooks of wilderness, managed forests, and urban environments. This volume showcases how the focus of the Institute's programs is evolving to support sustainable tropical forest conservation despite uncertain conditions. The manuscripts showcased here highlight the importance of shared stewardship and a long-term, hands-on approach to conservation, research programs, and novel organizations intended to meet contemporary conservation challenges. Policies relevant to the Anthropocene, as well as the use of experiments to anticipate future responses of tropical forests to global warming, are reexamined in these pages. Urban topics include how cities can co-produce new knowledge to spark sustainable and resilient transformations. Long-term results and research applications of topics such as soil biota, migratory birds, tropical vegetation, substrate chemistry, and the tropical carbon cycle are also described in the volume. Moreover, the question of how to best use land on a tropical island is addressed. This volume is intended to be of interest to all actors involved in long-term sustainable forest management and research in light of the historical lessons and future directions that may come out of a better understanding of tropical cities and forests in the Anthropocene epoch.?13C?15Nadaptive managementallometryAmerican tropicsand N/P ratiosannual cycleAnthropocenebasal areabiomassC/NC/PCa/Al relationshipCaribbeancarry over effectscitiesclimate changecommunicationsconservationcontemporary conservationdisturbancedry tropical forestsEl Yunque National Forestelement concentrationelement concentration in leaf litterelevationexperimentsforest inventory dataForest Servicegeospatial analysesgradientsGuánicahumid tropical forestshurricaneidiom of co-productionintroduced speciesinvertebratesknowledge co-productionknowledge infrastructuresknowledge systemsknowledge systems analysisland use governanceland use planninglandscape conservationlarge-scalelatitudeleadershipleaf C and N densitiesleaf mass per arealitterlong-termlong-term ecological researchLong-Term Ecological ResearchLuquillo Experimental Forestmanipulationsmature forestsmicrobiotan/aN/P ratiosnaturalized speciesNearctic-Neotropicalnetwork governancenitrogen fixing treesnovel forestsphotosynthetic nitrogen use-efficiencyPuerto Ricosecondary forestssoil biotasoil organic carbonspecies compositionspecies dominancestoichiometry of leaf litterstrategic teamssuccessiontree plantationstreestropicaltropical agriculturetropical deforestationtropical foresttropical forest areaTropical Forest Conservationtropical forest managementTropical Forest ManagementTropical Forestry Researchtropical foreststropical karstU.S. Forest Service Planning Rulevisionvolume expansion factorswoodGonzález de Jesús Grizelleauth1880512Lugo ArielauthBOOK9910367736703321Tropical Forest Ecology and Management for the Anthropocene4494513UNINA