One of the challenging problems for surface mining operation optimization is choosing the optimal truck and loader fleet. We refer to this problem as the equipment selection problem (ESP). In this paper, we describe the ESP in the context of surface mining and discuss related problems and applications. Within the scope of both the ESP and related problems, we outline modeling and solution approaches. Using operations research literature as a guide, we conclude by pointing to future research directions to improve both the modeling and solution outcomes for practical applications of this problem.
%0 Journal Article
%1 burt_equipment_2014
%A Burt, Christina N.
%A Caccetta, Lou
%D 2014
%J INFORMS Journal on Applied Analytics
%K Equipment Flow Method Mining Multicommodity Network Productivity Selection Shovel-Truck Surface
%N 2
%P 143--162
%R 10.1287/inte.2013.0732
%T Equipment Selection for Surface Mining: A Review
%U https://pubsonline.informs.org/doi/abs/10.1287/inte.2013.0732
%V 44
%X One of the challenging problems for surface mining operation optimization is choosing the optimal truck and loader fleet. We refer to this problem as the equipment selection problem (ESP). In this paper, we describe the ESP in the context of surface mining and discuss related problems and applications. Within the scope of both the ESP and related problems, we outline modeling and solution approaches. Using operations research literature as a guide, we conclude by pointing to future research directions to improve both the modeling and solution outcomes for practical applications of this problem.
@article{burt_equipment_2014,
abstract = {One of the challenging problems for surface mining operation optimization is choosing the optimal truck and loader fleet. We refer to this problem as the equipment selection problem (ESP). In this paper, we describe the ESP in the context of surface mining and discuss related problems and applications. Within the scope of both the ESP and related problems, we outline modeling and solution approaches. Using operations research literature as a guide, we conclude by pointing to future research directions to improve both the modeling and solution outcomes for practical applications of this problem.},
added-at = {2023-08-17T00:23:35.000+0200},
author = {Burt, Christina N. and Caccetta, Lou},
biburl = {https://www.bibsonomy.org/bibtex/233c1066e898ffc6d85a0e5a56d36606c/glonga},
doi = {10.1287/inte.2013.0732},
interhash = {279859130b168d509e7fd071b694469a},
intrahash = {33c1066e898ffc6d85a0e5a56d36606c},
issn = {2644-0865},
journal = {INFORMS Journal on Applied Analytics},
keywords = {Equipment Flow Method Mining Multicommodity Network Productivity Selection Shovel-Truck Surface},
month = apr,
number = 2,
pages = {143--162},
shorttitle = {Equipment {Selection} for {Surface} {Mining}},
timestamp = {2023-08-17T00:23:35.000+0200},
title = {Equipment {Selection} for {Surface} {Mining}: {A} {Review}},
url = {https://pubsonline.informs.org/doi/abs/10.1287/inte.2013.0732},
urldate = {2022-02-26},
volume = 44,
year = 2014
}