Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/215
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dc.contributor.authorBurt, Christina N. ; author. ;en_US
dc.contributor.authorCaccetta, Louis. ; author. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-28T08:50:14Z-
dc.date.available2020-04-28T08:50:14Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/215-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319762548 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis unique book presents innovative and state-of-the-art computational models for determining the optimal truck–loader selection and allocation strategy for use in large and complex mining operations. The authors provide comprehensive information on the methodology that has been developed over the past 50 years, from the early ad hoc spreadsheet approaches to today’s highly sophisticated and accurate mathematical-based computational models. The authors’ approach is motivated and illustrated by real case studies provided by our industry collaborators. The book is intended for a broad audience, ranging from mathematicians with an interest in industrial applications to mining engineers who wish to utilize the most accurate, efficient, versatile and robust computational models in order to refine their equipment selection and allocation strategy. As materials handling costs represent a significant component of total costs for mining operations, applying the optimization methodology developed here can substantially improve their competitiveness. ;en_US
dc.description.statementofresponsibilityby Christina N. Burt, Louis Caccetta.en_US
dc.description.tableofcontentsIntroduction -- Literature Review -- Match Factor Extensions -- Case Studies -- Single Location Equipment Selection -- Multiple Locations Equipment Selection -- Utilisation-based Equipment Selection -- Accurate Costing of Mining Equipment -- Future Research Directions. ;en_US
dc.format.extentXI, 155 p. 38 illus., 24 illus. in color. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesStudies in Systems, Decision and Control, ; 2198-4182 ; ; 150 ;en_US
dc.relation.ispartofseriesStudies in Systems, Decision and Control, ; 2198-4182 ; ; 150 ;en_US
dc.relation.haspart9783319762555.pdfen_US
dc.subjectEngineeringen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputational intelligence. ;en_US
dc.subjectTransportation engineering. ;en_US
dc.subjectTraffic engineering. ;en_US
dc.subjectEngineeringen_US
dc.subjectTransportation Technology and Traffic Engineering. ;en_US
dc.subjectComputational Intelligence. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.titleEquipment Selection for Mining: With Case Studiesen_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcTA1001-TA1280 ;en_US
dc.classification.lcHE331-HE380 ;en_US
dc.classification.dc629.04 ; 23 ;en_US
Appears in Collections:مهندسی مدیریت ساخت

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorBurt, Christina N. ; author. ;en_US
dc.contributor.authorCaccetta, Louis. ; author. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-28T08:50:14Z-
dc.date.available2020-04-28T08:50:14Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/215-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319762548 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis unique book presents innovative and state-of-the-art computational models for determining the optimal truck–loader selection and allocation strategy for use in large and complex mining operations. The authors provide comprehensive information on the methodology that has been developed over the past 50 years, from the early ad hoc spreadsheet approaches to today’s highly sophisticated and accurate mathematical-based computational models. The authors’ approach is motivated and illustrated by real case studies provided by our industry collaborators. The book is intended for a broad audience, ranging from mathematicians with an interest in industrial applications to mining engineers who wish to utilize the most accurate, efficient, versatile and robust computational models in order to refine their equipment selection and allocation strategy. As materials handling costs represent a significant component of total costs for mining operations, applying the optimization methodology developed here can substantially improve their competitiveness. ;en_US
dc.description.statementofresponsibilityby Christina N. Burt, Louis Caccetta.en_US
dc.description.tableofcontentsIntroduction -- Literature Review -- Match Factor Extensions -- Case Studies -- Single Location Equipment Selection -- Multiple Locations Equipment Selection -- Utilisation-based Equipment Selection -- Accurate Costing of Mining Equipment -- Future Research Directions. ;en_US
dc.format.extentXI, 155 p. 38 illus., 24 illus. in color. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesStudies in Systems, Decision and Control, ; 2198-4182 ; ; 150 ;en_US
dc.relation.ispartofseriesStudies in Systems, Decision and Control, ; 2198-4182 ; ; 150 ;en_US
dc.relation.haspart9783319762555.pdfen_US
dc.subjectEngineeringen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputational intelligence. ;en_US
dc.subjectTransportation engineering. ;en_US
dc.subjectTraffic engineering. ;en_US
dc.subjectEngineeringen_US
dc.subjectTransportation Technology and Traffic Engineering. ;en_US
dc.subjectComputational Intelligence. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.titleEquipment Selection for Mining: With Case Studiesen_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcTA1001-TA1280 ;en_US
dc.classification.lcHE331-HE380 ;en_US
dc.classification.dc629.04 ; 23 ;en_US
Appears in Collections:مهندسی مدیریت ساخت

Files in This Item:
File Description SizeFormat 
9783319762555.pdf3.04 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBurt, Christina N. ; author. ;en_US
dc.contributor.authorCaccetta, Louis. ; author. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-28T08:50:14Z-
dc.date.available2020-04-28T08:50:14Z-
dc.date.issued2018en_US
dc.identifier.urihttp://localhost/handle/Hannan/215-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319762548 ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis unique book presents innovative and state-of-the-art computational models for determining the optimal truck–loader selection and allocation strategy for use in large and complex mining operations. The authors provide comprehensive information on the methodology that has been developed over the past 50 years, from the early ad hoc spreadsheet approaches to today’s highly sophisticated and accurate mathematical-based computational models. The authors’ approach is motivated and illustrated by real case studies provided by our industry collaborators. The book is intended for a broad audience, ranging from mathematicians with an interest in industrial applications to mining engineers who wish to utilize the most accurate, efficient, versatile and robust computational models in order to refine their equipment selection and allocation strategy. As materials handling costs represent a significant component of total costs for mining operations, applying the optimization methodology developed here can substantially improve their competitiveness. ;en_US
dc.description.statementofresponsibilityby Christina N. Burt, Louis Caccetta.en_US
dc.description.tableofcontentsIntroduction -- Literature Review -- Match Factor Extensions -- Case Studies -- Single Location Equipment Selection -- Multiple Locations Equipment Selection -- Utilisation-based Equipment Selection -- Accurate Costing of Mining Equipment -- Future Research Directions. ;en_US
dc.format.extentXI, 155 p. 38 illus., 24 illus. in color. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesStudies in Systems, Decision and Control, ; 2198-4182 ; ; 150 ;en_US
dc.relation.ispartofseriesStudies in Systems, Decision and Control, ; 2198-4182 ; ; 150 ;en_US
dc.relation.haspart9783319762555.pdfen_US
dc.subjectEngineeringen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputational intelligence. ;en_US
dc.subjectTransportation engineering. ;en_US
dc.subjectTraffic engineering. ;en_US
dc.subjectEngineeringen_US
dc.subjectTransportation Technology and Traffic Engineering. ;en_US
dc.subjectComputational Intelligence. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.titleEquipment Selection for Mining: With Case Studiesen_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcTA1001-TA1280 ;en_US
dc.classification.lcHE331-HE380 ;en_US
dc.classification.dc629.04 ; 23 ;en_US
Appears in Collections:مهندسی مدیریت ساخت

Files in This Item:
File Description SizeFormat 
9783319762555.pdf3.04 MBAdobe PDFThumbnail
Preview File