Home > Research > Publications & Outputs > Stochastic local search procedures for the prob...

Research

Links

Text available via DOI:

Keywords

View graph of relations

Stochastic local search procedures for the probabilistic two-day vehicle routing problem

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Published

Standard

Stochastic local search procedures for the probabilistic two-day vehicle routing problem. / Doerner, Karl F.; Gutjahr, Walter J.; Hartl, Richard F. et al.
Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Springer, 2008. p. 153-168 (Studies in Computational Intelligence; Vol. 144).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Doerner, KF, Gutjahr, WJ, Hartl, RF & Lulli, G 2008, Stochastic local search procedures for the probabilistic two-day vehicle routing problem. in Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Studies in Computational Intelligence, vol. 144, Springer, pp. 153-168. https://doi.org/10.1007/978-3-540-69390-1_8

APA

Doerner, K. F., Gutjahr, W. J., Hartl, R. F., & Lulli, G. (2008). Stochastic local search procedures for the probabilistic two-day vehicle routing problem. In Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management (pp. 153-168). (Studies in Computational Intelligence; Vol. 144). Springer. https://doi.org/10.1007/978-3-540-69390-1_8

Vancouver

Doerner KF, Gutjahr WJ, Hartl RF, Lulli G. Stochastic local search procedures for the probabilistic two-day vehicle routing problem. In Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Springer. 2008. p. 153-168. (Studies in Computational Intelligence). doi: 10.1007/978-3-540-69390-1_8

Author

Doerner, Karl F. ; Gutjahr, Walter J. ; Hartl, Richard F. et al. / Stochastic local search procedures for the probabilistic two-day vehicle routing problem. Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. Springer, 2008. pp. 153-168 (Studies in Computational Intelligence).

Bibtex

@inbook{84a7de807aa94e76995fcede3f817b04,
title = "Stochastic local search procedures for the probabilistic two-day vehicle routing problem",
abstract = "This chapter is motivated by the study of a real-world application on blood delivery. The Austrian Red Cross (ARC), a non-profit organization, is in charge of delivering blood to hospitals on their request. To reduce their operating costs through higher flexibility, the ARC is interested in changing the policy of delivering blood products. Therefore it wants to provide two different types of service: an urgent service which delivers the blood within one day and the other, regular service, within two days. Obviously the two services come at different prices. We formalize this problem as a stochastic problem, with the objective to minimize the average long-run delivery costs, knowing the probabilities governing the requests of service. To solve real instances of our problem in a reasonable time, we propose three heuristic procedures whose core routine is an Ant Colony Optimization (ACO) algorithm, which differ from each other by the rule implemented to select the regular blood orders to serve immediately. We compare the three heuristics on both a set of real-world data and on a set of randomly generated synthetic data. Computational results show the viability of our approach.",
keywords = "Blood delivery, Stochastic local search, Vehicle routing",
author = "Doerner, {Karl F.} and Gutjahr, {Walter J.} and Hartl, {Richard F.} and Guglielmo Lulli",
year = "2008",
month = sep,
day = "18",
doi = "10.1007/978-3-540-69390-1_8",
language = "English",
isbn = "9783540690245",
series = "Studies in Computational Intelligence",
publisher = "Springer",
pages = "153--168",
booktitle = "Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management",

}

RIS

TY - CHAP

T1 - Stochastic local search procedures for the probabilistic two-day vehicle routing problem

AU - Doerner, Karl F.

AU - Gutjahr, Walter J.

AU - Hartl, Richard F.

AU - Lulli, Guglielmo

PY - 2008/9/18

Y1 - 2008/9/18

N2 - This chapter is motivated by the study of a real-world application on blood delivery. The Austrian Red Cross (ARC), a non-profit organization, is in charge of delivering blood to hospitals on their request. To reduce their operating costs through higher flexibility, the ARC is interested in changing the policy of delivering blood products. Therefore it wants to provide two different types of service: an urgent service which delivers the blood within one day and the other, regular service, within two days. Obviously the two services come at different prices. We formalize this problem as a stochastic problem, with the objective to minimize the average long-run delivery costs, knowing the probabilities governing the requests of service. To solve real instances of our problem in a reasonable time, we propose three heuristic procedures whose core routine is an Ant Colony Optimization (ACO) algorithm, which differ from each other by the rule implemented to select the regular blood orders to serve immediately. We compare the three heuristics on both a set of real-world data and on a set of randomly generated synthetic data. Computational results show the viability of our approach.

AB - This chapter is motivated by the study of a real-world application on blood delivery. The Austrian Red Cross (ARC), a non-profit organization, is in charge of delivering blood to hospitals on their request. To reduce their operating costs through higher flexibility, the ARC is interested in changing the policy of delivering blood products. Therefore it wants to provide two different types of service: an urgent service which delivers the blood within one day and the other, regular service, within two days. Obviously the two services come at different prices. We formalize this problem as a stochastic problem, with the objective to minimize the average long-run delivery costs, knowing the probabilities governing the requests of service. To solve real instances of our problem in a reasonable time, we propose three heuristic procedures whose core routine is an Ant Colony Optimization (ACO) algorithm, which differ from each other by the rule implemented to select the regular blood orders to serve immediately. We compare the three heuristics on both a set of real-world data and on a set of randomly generated synthetic data. Computational results show the viability of our approach.

KW - Blood delivery

KW - Stochastic local search

KW - Vehicle routing

U2 - 10.1007/978-3-540-69390-1_8

DO - 10.1007/978-3-540-69390-1_8

M3 - Chapter

AN - SCOPUS:51649104349

SN - 9783540690245

T3 - Studies in Computational Intelligence

SP - 153

EP - 168

BT - Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management

PB - Springer

ER -