LEADER 05252nam 22007575 450 001 9910254351103321 005 20200704014833.0 010 $a3-319-44394-1 024 7 $a10.1007/978-3-319-44394-2 035 $a(CKB)3710000000909122 035 $a(DE-He213)978-3-319-44394-2 035 $a(MiAaPQ)EBC6304944 035 $a(MiAaPQ)EBC5586379 035 $a(Au-PeEL)EBL5586379 035 $a(OCoLC)960699177 035 $a(PPN)196323622 035 $a(EXLCZ)993710000000909122 100 $a20161004d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGrouping Genetic Algorithms $eAdvances and Applications /$fby Michael Mutingi, Charles Mbohwa 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIV, 243 p. 78 illus.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v666 311 $a3-319-44393-3 327 $aPart I: Introduction -- Exploring Grouping Problems in Industry -- Complicating Features in Grouping Problems -- Part II: Grouping Genetic Algorithms -- Crouping Genetic Algorithms -- Fuzzy Grouping Genetic Algorithms -- Research Applications -- Fleet Size and Mix Vehicle Routing -- Heterogeneous Vehicle Routing -- Bin Packing: Container-Loading Problems with Compartments -- Homecare Staff Scheduling -- Task Assignment in Home Healthcare Services -- Nursing-Care Task Assignment -- Cell-Manufacturing Systems Design -- Cutting Stock Problem -- Assembly-Line Balancing -- Job-Shop Scheduling -- Equal Piles Problem -- Advertisement Allocation -- Part IV: Conclusions -- Concluding Remarks -- Further Research Considerations. 330 $aThis book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms. Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v666 606 $aComputational intelligence 606 $aOperations research 606 $aDecision making 606 $aArtificial intelligence 606 $aIndustrial engineering 606 $aProduction engineering 606 $aManagement science 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aIndustrial and Production Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T22008 606 $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024 615 0$aComputational intelligence. 615 0$aOperations research. 615 0$aDecision making. 615 0$aArtificial intelligence. 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 0$aManagement science. 615 14$aComputational Intelligence. 615 24$aOperations Research/Decision Theory. 615 24$aArtificial Intelligence. 615 24$aIndustrial and Production Engineering. 615 24$aOperations Research, Management Science. 676 $a620 700 $aMutingi$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut$0992675 702 $aMbohwa$b Charles$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254351103321 996 $aGrouping Genetic Algorithms$92273105 997 $aUNINA