LEADER 05556nam 2200685 a 450 001 9910139244703321 005 20230803023901.0 010 $a1-118-73159-X 010 $a1-118-73156-5 010 $a1-118-73155-7 035 $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(EXLCZ)992560000000103972 100 $a20130326d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aMetaheuristics for production scheduling$b[electronic resource] /$fedited by Bassem Jarboui, Patrick Siarry, Jacques Teghem ; series editor, Jean-Paul Bourrie?res 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 $a1-84821-497-9 320 $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$0860326 701 $aSiarry$b Patrick$0860327 701 $aTeghem$b Jacques$056402 701 $aBourrieres$b Jean-Paul$0860328 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139244703321 996 $aMetaheuristics for production scheduling$91919644 997 $aUNINA