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Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand
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Shukla, Nagesh, Tiwari, M. K. and Ceglarek, Darek (2013) Genetic-algorithms-based algorithm portfolio for inventory routing problem with stochastic demand. International Journal of Production Research , Volume 51 (Number 1). pp. 118-137. doi:10.1080/00207543.2011.653010 ISSN 0020-7543.
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Official URL: http://dx.doi.org/10.1080/00207543.2011.653010
Abstract
This paper presents an algorithm portfolio methodology based on evolutionary algorithms to solve complex dynamic optimisation problems. These problems are known to have computationally complex objective functions, which make their solutions computationally hard to find, when problem instances of large dimensions are considered. This is due to the inability of the algorithms to provide an optimal or near-optimal solution within an allocated time interval. Therefore, this paper employs a bundle of evolutionary algorithms (EAs) tied together with several processors, known as an algorithm portfolio, to solve a complex optimisation problem such as the inventory routing problem (IRP) with stochastic demands. EAs considered for algorithm portfolios are the genetic algorithm and its four variants such as the memetic algorithm, genetic algorithm with chromosome differentiation, age-genetic algorithm, and gender-specific genetic algorithm. In order to illustrate the applicability of the proposed methodology, a generic method for algorithm portfolios design, evaluation, and analysis is discussed in detail. Experiments were performed on varying dimensions of IRP instances to validate different properties of algorithm portfolio. A case study was conducted to illustrate that the set of EAs allocated to a certain number of processors performed better than their individual counterparts.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Journal or Publication Title: | International Journal of Production Research | ||||
Publisher: | Taylor & Francis Ltd. | ||||
ISSN: | 0020-7543 | ||||
Official Date: | 2013 | ||||
Dates: |
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Volume: | Volume 51 | ||||
Number: | Number 1 | ||||
Page Range: | pp. 118-137 | ||||
DOI: | 10.1080/00207543.2011.653010 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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