000 | 03528nam a22005775i 4500 | ||
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001 | 978-3-319-76255-5 | ||
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007 | cr nn 008mamaa | ||
008 | 180302s2018 sz | s |||| 0|eng d | ||
020 |
_a9783319762555 _9978-3-319-76255-5 |
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024 | 7 |
_a10.1007/978-3-319-76255-5 _2doi |
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_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. |
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300 |
_aXIII, 155 p. 38 illus., 24 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aStudies in Systems, Decision and Control, _x2198-4190 ; _v150 |
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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 |
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650 | 0 |
_aTraffic engineering. _915334 |
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650 | 0 |
_aComputational intelligence. _97716 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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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 |
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700 | 1 |
_aCaccetta, Louis. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _954511 |
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710 | 2 |
_aSpringerLink (Online service) _954512 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783319762548 |
776 | 0 | 8 |
_iPrinted edition: _z9783319762562 |
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_iPrinted edition: _z9783030094447 |
830 | 0 |
_aStudies in Systems, Decision and Control, _x2198-4190 ; _v150 _954513 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-76255-5 |
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912 | _aZDB-2-SXE | ||
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