IJRTI
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 11

Issue Published : 118

Article Submitted : 21504

Article Published : 8513

Total Authors : 22399

Total Reviewer : 805

Total Countries : 158

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: Use of Machine Learning Algorithm Models to Optimize the Truck Dispatch System in Opencast Mines
Authors Name: SATYAM CHOUDHURY , Prof. H.K.Naik
Download E-Certificate: Download
Author Reg. ID:
IJRTI_182634
Published Paper Id: IJRTI2206226
Published In: Volume 7 Issue 6, June-2022
DOI: http://doi.one/10.1729/Journal.30915
Abstract: In surface mining operations, the dumper haulage system contributes the most in total operating cost of any mine. It is estimated that an average mining company spends around 50% to 60% in this truck haulage system only. So utmost priority should be given to keep up an effective haulage framework. So, to reduce the cost of operation the dumpers must be allocated and dispatched efficiently. The haulage systems should be designed in such a manner that the availability, performance and utilization of the dumper and shovel are maximized, which ultimately yield in high production and reduction of operating cost. So, in this paper to enhance the productivity of truck haulage system an attempt is made to minimize the cycle time of dumpers and allocate an optimized number of dumpers to one shovel so that the idle time of dumpers can be minimized. In determining the cycle, time of dumpers predicting the travelling time in different situation is given utmost importance. For the machine learning models are used which help in predicting the travelling time in different atmospheric situation of the mine. This approach of integrating the machine learning methods in minimizing the cycle time will provide a proper estimation of performance measure, truck scheduling and finally an optimized truck dispatch system.
Keywords: opencast mine, truck dispatch system, dumpers, shovels, cycle time, scheduling, overall equipment effectiveness, machine learning, optimization.
Cite Article: "Use of Machine Learning Algorithm Models to Optimize the Truck Dispatch System in Opencast Mines", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.1530 - 1543, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206226.pdf
Downloads: 000205279
ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2206226
Registration ID:182634
Published In: Volume 7 Issue 6, June-2022
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.30915
Page No: 1530 - 1543
Country: Keonjhar, Odisha, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2206226
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2206226
Share Article:

Click Here to Download This Article

Article Preview
Click Here to Download This Article

Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI.ONE
How to Get DOI?

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Join RMS/Earn 300

IJRTI

WhatsApp
Click Here

Indexing Partner