LEADER 01214nam a2200253 i 4500 001 991001019469707536 008 050512s2005 it 101 0 ita d 035 $ab13307903-39ule_inst 040 $aDip.to Studi Giuridici$bita 082 0 $a325.73 245 00$aGestione comune delle frontiere e contrasto all'immigrazione clandestina in Europa :$bcommissione parlamentare di controllo sull'attuazione dell'accordo di Schengen, di vigilanza sull'attivitą di Europol, di controllo e vigilanza in materia di immigrazione 260 $aRoma :$bCamera dei Deputati,$c2005 300 $aviii, 514 p. ;$c30 cm 440 0$aIndagini conoscitive e documentazioni legislative ;$v19 500 $aAtti parlamentari. - XIV Legislatura 650 4$aImmigrazione$zEuropa 710 1 $aCamera dei Deputati 710 1 $aSenato della Repubblica 907 $a.b13307903$b02-04-14$c12-05-05 912 $a991001019469707536 945 $aLE027 325.73 GFI01.01$g1$i2027000100231$lle027$og$pE20.00$q-$rl$s- $t0$u8$v10$w8$x0$y.i14066300$z12-05-05 996 $aGestione comune delle frontiere e contrasto all'immigrazione clandestina in Europa$91103981 997 $aUNISALENTO 998 $ale027$b12-05-05$cm$da $e-$fita$git $h0$i0 LEADER 01715oam 2200481Ia 450 001 9910696995803321 005 20230902161722.0 035 $a(CKB)5470000002382929 035 $a(OCoLC)227911067 035 $a(EXLCZ)995470000002382929 100 $a20080513d2006 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModel of high-energy-density battery based on SiC Schottky diodes$b[electronic resource] /$fby Yves Ngu, Marc Litz, and Bruce Geil 210 1$aAdelphi, MD :$cArmy Research Laboratory,$d[2006] 215 $a1 online resource (iv, 14 pages) $cillustrations 225 1 $aARL-TR ;$v3981 300 $aTitle from PDF title screen (ARL, viewed Dec. 13, 2010). 300 $a"October 2006." 320 $aIncludes bibliographical references. 410 0$aARL-TR (Aberdeen Proving Ground, Md.) ;$v3981. 517 3 $aModel of high energy density battery based on silicon carbide (SiC) Schottky diodes 606 $aEnergy storage$xResearch 606 $aSilicon carbide$xResearch$zUnited States 606 $aNuclear batteries$xResearch$zUnited States 615 0$aEnergy storage$xResearch. 615 0$aSilicon carbide$xResearch 615 0$aNuclear batteries$xResearch 700 $aNgu$b Yves$01395458 701 $aLitz$b Marc$01395459 701 $aGeil$b Bruce$01395460 712 02$aU.S. Army Research Laboratory. 801 0$bDTICE 801 1$bDTICE 801 2$bOCLCQ 801 2$bGPO 906 $aBOOK 912 $a9910696995803321 996 $aModel of high-energy-density battery based on SiC Schottky diodes$93454018 997 $aUNINA LEADER 05764nam 2200781 a 450 001 9910818726303321 005 20240313235046.0 010 $a9781118731598 010 $a111873159X 010 $a9781118731567 010 $a1118731565 010 $a9781118731550 010 $a1118731557 035 $a(CKB)2560000000103972 035 $a(EBL)1215812 035 $a(OCoLC)851160934 035 $a(SSID)ssj0000971676 035 $a(PQKBManifestationID)11617479 035 $a(PQKBTitleCode)TC0000971676 035 $a(PQKBWorkID)10939448 035 $a(PQKB)11356999 035 $a(MiAaPQ)EBC1215812 035 $a(Au-PeEL)EBL1215812 035 $a(CaPaEBR)ebr10720726 035 $a(CaONFJC)MIL499150 035 $a(OCoLC)823380606 035 $a(FINmELB)ELB179442 035 $a(Perlego)999949 035 $a(EXLCZ)992560000000103972 100 $a20130326d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMetaheuristics for production scheduling /$fedited by Bassem Jarboui, Patrick Siarry, Jacques Teghem ; series editor, Jean-Paul Bourrie?res 205 $a1st ed. 210 $aLondon $cISTE ;$aHoboken, N.J. $cJohn Wiley and Sons Inc.$d2013 215 $a1 online resource (529 p.) 225 0$aAutomation-control and industrial engineering series 300 $aDescription based upon print version of record. 311 08$a9781848214972 311 08$a1848214979 320 $aIncludes bibliographical references and index. 327 $aCover; Title Page; Contents; Introduction and Presentation; Chapter 1. An Estimation of Distribution Algorithm for SolvingFlow Shop Scheduling Problems with Sequence-dependent FamilySetup Times; 1.1. Introduction; 1.2. Mathematical formulation; 1.3. Estimation of distribution algorithms; 1.3.1. Estimation of distribution algorithms proposed in the literature; 1.4. The proposed estimation of distribution algorithm; 1.4.1. Encoding scheme and initial population; 1.4.2. Selection; 1.4.3. Probability estimation; 1.5. Iterated local search algorithm; 1.6. Experimental results; 1.7. Conclusion 327 $a1.8. BibliographyChapter 2. Genetic Algorithms for Solving Flexible Job ShopScheduling Problems; 2.1. Introduction; 2.2. Flexible job shop scheduling problems; 2.3. Genetic algorithms for some related sub-problems; 2.4. Genetic algorithms for the flexible job shop problem; 2.4.1. Codings; 2.4.2. Mutation operators; 2.4.3. Crossover operators; 2.5. Comparison of codings; 2.6. Conclusion; 2.7. Bibliography; Chapter 3. A Hybrid GRASP-Differential Evolution Algorithmfor Solving Flow Shop Scheduling Problemswith No-Wait Constraints; 3.1. Introduction; 3.2. Overview of the literature 327 $a3.2.1. Single-solution metaheuristics3.2.2. Population-based metaheuristics; 3.2.3. Hybrid approaches; 3.3. Description of the problem; 3.4. GRASP; 3.5. Differential evolution; 3.6. Iterative local search; 3.7. Overview of the NEW-GRASP-DE algorithm; 3.7.1. Constructive phase; 3.7.2. Improvement phase; 3.8. Experimental results; 3.8.1. Experimental results for the Reeves and Heller instances; 3.8.2. Experimental results for the Taillard instances; 3.9. Conclusion; 3.10. Bibliography 327 $aChapter 4. A Comparison of Local Search Metaheuristicsfor a Hierarchical Flow Shop Optimization Problemwith Time Lags4.1. Introduction; 4.2. Description of the problem; 4.2.1. Flowshop with time lags; 4.2.2. A bicriteria hierarchical flow shop problem; 4.3. The proposed metaheuristics; 4.3.1. A simulated annealing metaheuristics; 4.3.2. The GRASP metaheuristics; 4.4. Tests; 4.4.1. Generated instances; 4.4.2. Comparison of the results; 4.5. Conclusion; 4.6. Bibliography; Chapter 5. Neutrality in Flow Shop Scheduling Problems:Landscape Structure and Local Search; 5.1. Introduction 327 $a5.2. Neutrality in a combinatorial optimization problem5.2.1. Landscape in a combinatorial optimization problem; 5.2.2. Neutrality and landscape; 5.3. Study of neutrality in the flow shop problem; 5.3.1. Neutral degree; 5.3.2. Structure of the neutral landscape; 5.4. Local search exploiting neutrality to solve the flow shop problem; 5.4.1. Neutrality-based iterated local search; 5.4.2. NILS on the flow shop problem; 5.5. Conclusion; 5.6. Bibliography; Chapter 6. Evolutionary Metaheuristic Based on GeneticAlgorithm: Application to Hybrid Flow Shop Problemwith Availability Constraints 327 $a6.1. Introduction 330 $a This book describes the potentialities of metaheuristics for solving production scheduling problems and the relationship between these two fields.For the past several years, there has been an increasing interest in using metaheuristic methods to solve scheduling problems. The main reasons for this are that such problems are generally hard to solve to optimality, as well as the fact that metaheuristics provide very good solutions in a reasonable time. The first part of the book presents eight applications of metaheuristics for solving various mono-objective scheduling problems. The sec 410 0$aISTE 606 $aProduction scheduling$xData processing 606 $aProduction scheduling$xComputer programs 615 0$aProduction scheduling$xData processing. 615 0$aProduction scheduling$xComputer programs. 676 $a670 701 $aJarboui$b Bassem$01635770 701 $aSiarry$b Patrick$0860327 701 $aTeghem$b Jacques$056402 701 $aBourrieres$b Jean-Paul$01607738 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910818726303321 996 $aMetaheuristics for production scheduling$93976728 997 $aUNINA