By the same authors

Evaluating hyperheuristics and local search operators for periodic routing problems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Standard

Evaluating hyperheuristics and local search operators for periodic routing problems. / Chen, Yujie; Mourdjis, Philip; Polack, Fiona; Cowling, Peter; Remde, Stephen.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9595 Springer-Verlag, 2016. p. 104-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9595).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Harvard

Chen, Y, Mourdjis, P, Polack, F, Cowling, P & Remde, S 2016, Evaluating hyperheuristics and local search operators for periodic routing problems. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9595, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9595, Springer-Verlag, pp. 104-120, 16th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2016, Porto, Portugal, 30/03/16. https://doi.org/10.1007/978-3-319-30698-8_8

APA

Chen, Y., Mourdjis, P., Polack, F., Cowling, P., & Remde, S. (2016). Evaluating hyperheuristics and local search operators for periodic routing problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9595, pp. 104-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9595). Springer-Verlag. https://doi.org/10.1007/978-3-319-30698-8_8

Vancouver

Chen Y, Mourdjis P, Polack F, Cowling P, Remde S. Evaluating hyperheuristics and local search operators for periodic routing problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9595. Springer-Verlag. 2016. p. 104-120. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-30698-8_8

Author

Chen, Yujie ; Mourdjis, Philip ; Polack, Fiona ; Cowling, Peter ; Remde, Stephen. / Evaluating hyperheuristics and local search operators for periodic routing problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9595 Springer-Verlag, 2016. pp. 104-120 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Bibtex - Download

@inproceedings{9adb6b32f76e4e94b2667a90cc24b44d,
title = "Evaluating hyperheuristics and local search operators for periodic routing problems",
abstract = "Meta-heuristics and hybrid heuristic approaches have been successfully applied to Periodic Vehicle Routing Problems (PVRPs). However, to be competitive, these methods require careful design of specific search strategies for each problem. By contrast, hyperheuristics use the performance of low level heuristics to automatically select and tailor search strategies. Hyperheuristics have been successfully applied to problem domains such as timetabling and production scheduling. In this study, we present a comprehensive analysis of hyperheuristic approaches to solving PVRPs. The performance of hyperheuristics is compared to published performance of state-of-the-art meta-heuristics.",
keywords = "Computational analysis, Hyperheuristic, PVRP",
author = "Yujie Chen and Philip Mourdjis and Fiona Polack and Peter Cowling and Stephen Remde",
year = "2016",
month = mar,
day = "15",
doi = "10.1007/978-3-319-30698-8_8",
language = "English",
isbn = "9783319306971",
volume = "9595",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "104--120",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
note = "16th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2016 ; Conference date: 30-03-2016 Through 01-04-2016",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Evaluating hyperheuristics and local search operators for periodic routing problems

AU - Chen, Yujie

AU - Mourdjis, Philip

AU - Polack, Fiona

AU - Cowling, Peter

AU - Remde, Stephen

PY - 2016/3/15

Y1 - 2016/3/15

N2 - Meta-heuristics and hybrid heuristic approaches have been successfully applied to Periodic Vehicle Routing Problems (PVRPs). However, to be competitive, these methods require careful design of specific search strategies for each problem. By contrast, hyperheuristics use the performance of low level heuristics to automatically select and tailor search strategies. Hyperheuristics have been successfully applied to problem domains such as timetabling and production scheduling. In this study, we present a comprehensive analysis of hyperheuristic approaches to solving PVRPs. The performance of hyperheuristics is compared to published performance of state-of-the-art meta-heuristics.

AB - Meta-heuristics and hybrid heuristic approaches have been successfully applied to Periodic Vehicle Routing Problems (PVRPs). However, to be competitive, these methods require careful design of specific search strategies for each problem. By contrast, hyperheuristics use the performance of low level heuristics to automatically select and tailor search strategies. Hyperheuristics have been successfully applied to problem domains such as timetabling and production scheduling. In this study, we present a comprehensive analysis of hyperheuristic approaches to solving PVRPs. The performance of hyperheuristics is compared to published performance of state-of-the-art meta-heuristics.

KW - Computational analysis

KW - Hyperheuristic

KW - PVRP

UR - http://www.scopus.com/inward/record.url?scp=84961238432&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-30698-8_8

DO - 10.1007/978-3-319-30698-8_8

M3 - Conference contribution

AN - SCOPUS:84961238432

SN - 9783319306971

VL - 9595

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 104

EP - 120

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer-Verlag

T2 - 16th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2016

Y2 - 30 March 2016 through 1 April 2016

ER -

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