LEADER 04720nam 2200613 450 001 9910828951403321 005 20200520144314.0 010 $a1-119-13678-4 010 $a1-119-13676-8 010 $a1-119-13677-6 035 $a(CKB)3710000000596062 035 $a(EBL)4405837 035 $a(MiAaPQ)EBC4405837 035 $a(Au-PeEL)EBL4405837 035 $a(CaPaEBR)ebr11155983 035 $a(CaONFJC)MIL897664 035 $a(OCoLC)939864920 035 $a(PPN)242965024 035 $a(EXLCZ)993710000000596062 100 $a20160607h20162016 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aMetaheuristics for vehicle routing problems /$fNacima Labadie, Christian Prins, Caroline Prodhon 210 1$aLondon, England ;$aHoboken, New Jersey :$ciSTE :$cWiley,$d2016. 210 4$dİ2016 215 $a1 online resource (197 p.) 225 0 $aComputer Engineering Series. Metaheuristics Set ;$vVolume 3 300 $aDescription based upon print version of record. 311 $a1-84821-811-7 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Copyright; Contents; Notations and Abbreviations; Notations; Abbreviations related to problems; Abbreviations related to methods; Introduction; Chapter 1. General Presentation of Vehicle Routing Problems; 1.1. Logistics management and combinatorial optimization; 1.1.1. History of logistics; 1.1.2. Logistics as a science; 1.1.3. Combinatorial optimization; 1.2. Vehicle routing problems; 1.2.1. Problems in transportation optimization; 1.2.2. Vehicle routing problems in other contexts; 1.2.3. Characteristics of vehicle routing problems; 1.2.3.1. Components 327 $a1.2.3.2. Constraints1.2.3.3. Objectives; 1.2.4. The capacitated vehicle routing problem; 1.2.4.1. Mathematical model; 1.2.4.2. Solution methods; 1.3. Conclusion; Chapter 2. Simple Heuristics and Local Search Procedures; 2.1. Simple heuristics; 2.1.1. Constructive heuristics; 2.1.2. Two-phase methods; 2.1.3. Best-of approach and randomization; 2.2. Local search; 2.2.1. Principle; 2.2.2. Classical moves; 2.2.3. Feasibility tests; 2.2.4. General approach from Vidal et al.; 2.2.5. Multiple neighborhoods; 2.2.6. Very constrained problems; 2.2.7. Acceleration techniques; 2.2.8. Complex moves 327 $a2.3. ConclusionChapter 3. Metaheuristics Generating a Sequence of Solutions; 3.1. Simulated annealing (SA); 3.1.1. Principle; 3.1.2. Simulated annealing in vehicle routing problems; 3.2. Greedy randomized adaptive search procedure: GRASP; 3.2.1. Principle; 3.2.2. GRASP in vehicle routing problems; 3.3. Tabu search; 3.3.1. Principle; 3.3.2. Tabu search in vehicle routing problems; 3.4. Variable neighborhood search; 3.4.1. Principle; 3.4.2. Variable neighborhood search in vehicle routing problems; 3.5. Iterated local search; 3.5.1. Principle 327 $a3.5.2. Iterated local search in vehicle routing problems3.6. Guided local search; 3.6.1. Principle; 3.6.2. Guided local search in vehicle routing problems; 3.7. Large neighborhood search; 3.7.1. Principle; 3.7.2. Large neighborhood search in vehicle routing problems; 3.8. Transitional forms; 3.8.1. Evolutionary local search principle; 3.8.2. Application to vehicle routing problems; 3.9. Selected examples; 3.9.1. GRASP for the location-routing problem; 3.9.2. Granular tabu search for the CVRP; 3.9.3. Adaptive large neighborhood search for the pickup and delivery problem with time windows 327 $a3.10. ConclusionChapter 4. Metaheuristics Based on a Set of Solutions; 4.1. Genetic algorithm and its variants; 4.1.1. Genetic algorithm; 4.1.2. Memetic algorithm; 4.1.3. Memetic algorithm with population management; 4.1.4. Genetic algorithm and its variants in vehicle routing problems; 4.2. Scatter search; 4.2.1. Scatter search principle; 4.2.2. Scatter search in vehicle routing problems; 4.3. Path relinking; 4.3.1. Principle; 4.3.2. Path relinking in vehicle routing problems; 4.4. Ant colony optimization; 4.4.1. Principle; 4.4.2. ACO in vehicle routing problems 327 $a4.5. Particle swarm optimization 606 $aTransportation problems (Programming) 606 $aMathematical optimization 615 0$aTransportation problems (Programming) 615 0$aMathematical optimization. 676 $a388.310285 700 $aLabadie$b Nacima$01685167 702 $aPrins$b Christian 702 $aProdhon$b Caroline 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910828951403321 996 $aMetaheuristics for vehicle routing problems$94057072 997 $aUNINA LEADER 01619nam 2200385z- 450 001 9910346918603321 005 20210212 010 $a1000014763 035 $a(CKB)4920000000101336 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/60253 035 $a(oapen)doab60253 035 $a(EXLCZ)994920000000101336 100 $a20202102d2010 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSulfur-tolerant natural gas reforming for fuel-cell applications 210 $cKIT Scientific Publishing$d2010 215 $a1 online resource (II, 144 p. p.) 311 08$a3-86644-459-1 330 $aAn attractive simplification of PEM-FC systems operated with natural gas would be the use of a sulfur tolerant reforming catalyst, but such a catalyst has not been available thus far. In this work it is demonstrated that a tailor made rhodium catalyst retains useful activity for typical sulfur levels in the feed. A brief economic comparison showed however that this alternative process is still less economical than the traditional process employing removal of sulfur components by adsorption. 606 $aBiotechnology$2bicssc 610 $afuel cells 610 $ahydrogen 610 $anatural gas 610 $areforming 610 $asulfur 615 7$aBiotechnology 700 $aHennings$b Ulrich$4auth$01301538 906 $aBOOK 912 $a9910346918603321 996 $aSulfur-tolerant natural gas reforming for fuel-cell applications$93025964 997 $aUNINA