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001 978-3-319-76255-5
003 DE-He213
005 20220801221155.0
007 cr nn 008mamaa
008 180302s2018 sz | s |||| 0|eng d
020 _a9783319762555
_9978-3-319-76255-5
024 7 _a10.1007/978-3-319-76255-5
_2doi
050 4 _aTA1001-1280
050 4 _aHE331-380
072 7 _aTNH
_2bicssc
072 7 _aTEC009020
_2bisacsh
072 7 _aTNH
_2thema
082 0 4 _a629.04
_223
245 1 0 _aEquipment Selection for Mining: With Case Studies
_h[electronic resource] /
_cedited by Christina N. Burt, Louis Caccetta.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXIII, 155 p. 38 illus., 24 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v150
505 0 _aIntroduction -- 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.
520 _aThis 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.
650 0 _aTransportation engineering.
_93560
650 0 _aTraffic engineering.
_915334
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aTransportation Technology and Traffic Engineering.
_932448
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aBurt, Christina N.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_954510
700 1 _aCaccetta, Louis.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_954511
710 2 _aSpringerLink (Online service)
_954512
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319762548
776 0 8 _iPrinted edition:
_z9783319762562
776 0 8 _iPrinted edition:
_z9783030094447
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v150
_954513
856 4 0 _uhttps://doi.org/10.1007/978-3-319-76255-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79372
_d79372