A Genetic Algorithm for Truck Dispatching in Mining

Wesley  Cox; Timothy Noel French; Mark Reynolds; Ronald Lyndon While

doi:10.29007/n11t

A Genetic Algorithm for Truck Dispatching in Mining

Wesley Cox, Timothy Noel French, Mark Reynolds, Ronald Lyndon While

School of Physics, Mathematics and Computing

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

Abstract

We apply genetic algorithms (GAs) to evolve cyclic finite automata for scheduling the dispatch of trucks in mines. The GA performs well generally, and on problems which include one-lane roads, the GA was able to find solutions that utilised shovels very well, with low contention and using fewer trucks than both the widely-used linear programming DISPATCH algorithm, and commonly-used greedy heuristics. The GA provides significant cost-savings, or production increases, on problems where alternative algorithms do not adapt well.

Original language	English
Title of host publication	GCAI 2017 - 3rd Global Conference on Artificial Intelligence
Editors	Christoph Benzmüller, Christine Lisetti, Martin Theobald
Place of Publication	USA
Publisher	EasyChair
Pages	93–107
DOIs	https://doi.org/10.29007/n11t
Publication status	Published - 2017
Event	3rd Global Conference on Artificial Intelligence - Miami, United States Duration: 18 Oct 2017 → 22 Oct 2017 http://easychair.org/smart-program/GCAI2017/

Publication series

Name	EPiC Series in Computing
Volume	50
ISSN (Print)	2398-7340

Conference

Conference	3rd Global Conference on Artificial Intelligence
Country/Territory	United States
City	Miami
Period	18/10/17 → 22/10/17
Internet address	http://easychair.org/smart-program/GCAI2017/

Access to Document

10.29007/n11t

Cite this

@inproceedings{d2b0777722174f2d8b2d5077cf2b228b,

title = "A Genetic Algorithm for Truck Dispatching in Mining",

abstract = "We apply genetic algorithms (GAs) to evolve cyclic finite automata for scheduling the dispatch of trucks in mines. The GA performs well generally, and on problems which include one-lane roads, the GA was able to find solutions that utilised shovels very well, with low contention and using fewer trucks than both the widely-used linear programming DISPATCH algorithm, and commonly-used greedy heuristics. The GA provides significant cost-savings, or production increases, on problems where alternative algorithms do not adapt well.",

author = "Wesley Cox and French, {Timothy Noel} and Mark Reynolds and While, {Ronald Lyndon}",

year = "2017",

doi = "10.29007/n11t",

language = "English",

series = "EPiC Series in Computing",

publisher = "EasyChair",

pages = "93–107",

editor = "Benzm{\"u}ller, {Christoph } and Lisetti, {Christine } and Theobald, {Martin }",

booktitle = "GCAI 2017 - 3rd Global Conference on Artificial Intelligence",

note = "3rd Global Conference on Artificial Intelligence ; Conference date: 18-10-2017 Through 22-10-2017",

url = "http://easychair.org/smart-program/GCAI2017/",

}

Cox, W, French, TN , Reynolds, M & While, RL 2017, A Genetic Algorithm for Truck Dispatching in Mining. in C Benzmüller, C Lisetti & M Theobald (eds), GCAI 2017 - 3rd Global Conference on Artificial Intelligence. EPiC Series in Computing, vol. 50, EasyChair, USA, pp. 93–107, 3rd Global Conference on Artificial Intelligence, Miami, Florida, United States, 18/10/17. https://doi.org/10.29007/n11t

A Genetic Algorithm for Truck Dispatching in Mining. / Cox, Wesley ; French, Timothy Noel ; Reynolds, Mark et al.
GCAI 2017 - 3rd Global Conference on Artificial Intelligence. ed. / Christoph Benzmüller; Christine Lisetti; Martin Theobald. USA: EasyChair, 2017. p. 93–107 (EPiC Series in Computing; Vol. 50).

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - A Genetic Algorithm for Truck Dispatching in Mining

AU - Cox, Wesley

AU - French, Timothy Noel

AU - Reynolds, Mark

AU - While, Ronald Lyndon

PY - 2017

Y1 - 2017

N2 - We apply genetic algorithms (GAs) to evolve cyclic finite automata for scheduling the dispatch of trucks in mines. The GA performs well generally, and on problems which include one-lane roads, the GA was able to find solutions that utilised shovels very well, with low contention and using fewer trucks than both the widely-used linear programming DISPATCH algorithm, and commonly-used greedy heuristics. The GA provides significant cost-savings, or production increases, on problems where alternative algorithms do not adapt well.

AB - We apply genetic algorithms (GAs) to evolve cyclic finite automata for scheduling the dispatch of trucks in mines. The GA performs well generally, and on problems which include one-lane roads, the GA was able to find solutions that utilised shovels very well, with low contention and using fewer trucks than both the widely-used linear programming DISPATCH algorithm, and commonly-used greedy heuristics. The GA provides significant cost-savings, or production increases, on problems where alternative algorithms do not adapt well.

UR - http://easychair.org/smart-program/GCAI2017/index.html

UR - https://easychair.org/publications/volume/GCAI_2017

U2 - 10.29007/n11t

DO - 10.29007/n11t

M3 - Conference paper

T3 - EPiC Series in Computing

SP - 93

EP - 107

BT - GCAI 2017 - 3rd Global Conference on Artificial Intelligence

A2 - Benzmüller, Christoph

A2 - Lisetti, Christine

A2 - Theobald, Martin

PB - EasyChair

CY - USA

T2 - 3rd Global Conference on Artificial Intelligence

Y2 - 18 October 2017 through 22 October 2017

ER -

A Genetic Algorithm for Truck Dispatching in Mining

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