Simulation-based robust optimization for complex truck-shovel systems in surface coal mines

Sai Srinivas Nageshwaraniyer, Young Jun Son, Sean Dessureault

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

A robust simulation-based optimization approach is proposed for truck-shovel systems in surface coal mines to maximize the expected value of revenue obtained from customer trains. To this end, a large surface coal mine in North America is considered as case study, and a highly detailed simulation model of that mine is constructed in Arena. Factors encountered in material handling operations that may affect the robustness of revenue are then classified into 1) controllable, 2) uncontrollable and 3) constant categories. Historical production data of the mine is used to derive probability distributions for the uncontrollable factors. Then, Response Surface Methodology is applied to derive an expression for the variance of revenue under the influence of controllable and uncontrollable factors. The resulting variance expression is applied as a constraint to the mathematical formulation for optimization using OptQuest. Finally, coal production is observed under variation in number of trucks and down events.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 Winter Simulation Conference - Simulation
Subtitle of host publicationMaking Decisions in a Complex World, WSC 2013
Pages3522-3532
Number of pages11
DOIs
StatePublished - 2013
Event2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013 - Washington, DC, United States
Duration: Dec 8 2013Dec 11 2013

Publication series

NameProceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013

Other

Other2013 43rd Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013
CountryUnited States
CityWashington, DC
Period12/8/1312/11/13

ASJC Scopus subject areas

  • Modeling and Simulation

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