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Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining. / Daduna, Hans; Krenzler, Ruslan; Ritter, Robert et al.
In: Performance Evaluation, Vol. 119, 03.2018, p. 5-26.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Daduna, H, Krenzler, R, Ritter, R & Stoyan, D 2018, 'Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining', Performance Evaluation, vol. 119, pp. 5-26. https://doi.org/10.1016/j.peva.2017.12.002

APA

Daduna, H., Krenzler, R., Ritter, R., & Stoyan, D. (2018). Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining. Performance Evaluation, 119, 5-26. https://doi.org/10.1016/j.peva.2017.12.002

Vancouver

Daduna H, Krenzler R, Ritter R, Stoyan D. Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining. Performance Evaluation. 2018 Mar;119:5-26. Epub 2017 Dec 19. doi: 10.1016/j.peva.2017.12.002

Bibtex

@article{5648747307504ae49e4d9e2fabfb27cb,
title = "Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining",
abstract = "We investigate a fundamental model from open-pit mining which is a cyclic system consisting of an (unreliable) shovel, trucks travelling loaded, unloading facility, and trucks travelling back empty. The interaction of these subsystems determines the mean number of trucks loaded per time unit — the capacity of the shovel, which is a fundamental quantity of interest. To determine this capacity we need the stationary probability that the shovel is idle. Because an exact analysis of the performance of the system is out of reach, besides of simulations there are various approximation algorithms proposed in the literature, which stem from computer science and can be characterized as general purpose algorithms. We propose for solving the special problem under mining conditions an extremely simple alternative algorithm. Comparison with several general purpose algorithms shows that for realistic situations in the open-pit mining application the special algorithm outperforms the precision of general purpose algorithms. This holds even if the general purpose algorithms incorporate more details of the underlying models than our simple algorithm, which is based on a strongly reduced model. The comparison and assessment is done with extensive simulations on a level of detail which the general purpose algorithms are able to cover. We discuss the application of our proposed algorithms to other applications. It turns out that our algorithms are analogues to Norton's Theorem for a large class of general transportation systems.",
keywords = "Mathematics, Queues, Algorithms, Heuristic methods, Mining, Queues, Algorithms, Heuristic methods, Long-run idle times, Transport, Engineering, Mining, Transport, Long-run idle times",
author = "Hans Daduna and Ruslan Krenzler and Robert Ritter and Dietrich Stoyan",
year = "2018",
month = mar,
doi = "10.1016/j.peva.2017.12.002",
language = "English",
volume = "119",
pages = "5--26",
journal = "Performance Evaluation",
issn = "0166-5316",
publisher = "Elsevier B.V.",

}

RIS

TY - JOUR

T1 - Heuristic approximation and computational algorithms for closed networks

T2 - A case study in open-pit mining

AU - Daduna, Hans

AU - Krenzler, Ruslan

AU - Ritter, Robert

AU - Stoyan, Dietrich

PY - 2018/3

Y1 - 2018/3

N2 - We investigate a fundamental model from open-pit mining which is a cyclic system consisting of an (unreliable) shovel, trucks travelling loaded, unloading facility, and trucks travelling back empty. The interaction of these subsystems determines the mean number of trucks loaded per time unit — the capacity of the shovel, which is a fundamental quantity of interest. To determine this capacity we need the stationary probability that the shovel is idle. Because an exact analysis of the performance of the system is out of reach, besides of simulations there are various approximation algorithms proposed in the literature, which stem from computer science and can be characterized as general purpose algorithms. We propose for solving the special problem under mining conditions an extremely simple alternative algorithm. Comparison with several general purpose algorithms shows that for realistic situations in the open-pit mining application the special algorithm outperforms the precision of general purpose algorithms. This holds even if the general purpose algorithms incorporate more details of the underlying models than our simple algorithm, which is based on a strongly reduced model. The comparison and assessment is done with extensive simulations on a level of detail which the general purpose algorithms are able to cover. We discuss the application of our proposed algorithms to other applications. It turns out that our algorithms are analogues to Norton's Theorem for a large class of general transportation systems.

AB - We investigate a fundamental model from open-pit mining which is a cyclic system consisting of an (unreliable) shovel, trucks travelling loaded, unloading facility, and trucks travelling back empty. The interaction of these subsystems determines the mean number of trucks loaded per time unit — the capacity of the shovel, which is a fundamental quantity of interest. To determine this capacity we need the stationary probability that the shovel is idle. Because an exact analysis of the performance of the system is out of reach, besides of simulations there are various approximation algorithms proposed in the literature, which stem from computer science and can be characterized as general purpose algorithms. We propose for solving the special problem under mining conditions an extremely simple alternative algorithm. Comparison with several general purpose algorithms shows that for realistic situations in the open-pit mining application the special algorithm outperforms the precision of general purpose algorithms. This holds even if the general purpose algorithms incorporate more details of the underlying models than our simple algorithm, which is based on a strongly reduced model. The comparison and assessment is done with extensive simulations on a level of detail which the general purpose algorithms are able to cover. We discuss the application of our proposed algorithms to other applications. It turns out that our algorithms are analogues to Norton's Theorem for a large class of general transportation systems.

KW - Mathematics

KW - Queues

KW - Algorithms

KW - Heuristic methods

KW - Mining

KW - Queues

KW - Algorithms

KW - Heuristic methods

KW - Long-run idle times

KW - Transport

KW - Engineering

KW - Mining

KW - Transport

KW - Long-run idle times

UR - http://www.scopus.com/inward/record.url?scp=85039908485&partnerID=8YFLogxK

U2 - 10.1016/j.peva.2017.12.002

DO - 10.1016/j.peva.2017.12.002

M3 - Journal articles

VL - 119

SP - 5

EP - 26

JO - Performance Evaluation

JF - Performance Evaluation

SN - 0166-5316

ER -

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

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Research output: Journal contributions › Journal articles › Research › peer-review

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Daduna, H. & Krenzler, R., 2020, Measurement, Modelling and Evaluation of Computing Systems : 20th International GI/ITG Conference, MMB 2020, Saarbrücken, Germany, March 16–18, 2020, Proceedings. Hermanns, H. (ed.). Cham: Springer Verlag, p. 118-135 18 p. (Lecture Notes in Computer Science ; vol. 12040 LNCS)(Programming and Software Engineering; vol. 12040).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Efficient Order Picking Methods in Robotic Mobile Fulfillment Systems

Xie, L., Thieme, N., Krenzler, R. & Li, H., 2019, 31 p.

Research output: Working paper › Working papers

DOI

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