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A hyper-heuristic based approach with naive Bayes classifier for the reliability p-median problem.
- Source :
- Applied Intelligence; Nov2023, Vol. 53 Issue 22, p27269-27289, 21p
- Publication Year :
- 2023
-
Abstract
- Facility location models involve identifying locations for facilities that provide services to the customers which are also called demand points. The p-median problem is a facility location problem which deals with locating p facilities in such a way that the sumtotal of demand-weighted distances between each demand point and its respective closest facility is minimized. The p-median problem does not take into account the possibility of failure of the facilities, but rather considers that the facilities once located will always be available to serve the customers or demand points. But, in reality, some of the facilities may face unpredicted disruptions, thereby forcing the customers to seek services from other functioning facilities. The reliability p-median problem (RpMP) concerns with locating facilities which minimize the cost while considering the cost of facility failures. In this paper, we have proposed a hyper-heuristic approach with naive Bayes classifier for the RpMP and compared its performance on standard benchmark instances with two best performing approaches for RpMP, viz. a genetic algorithm and a scatter search approach available in the literature. The results show the effectiveness of our approach in terms of the solution quality. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0924669X
- Volume :
- 53
- Issue :
- 22
- Database :
- Complementary Index
- Journal :
- Applied Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 173178652
- Full Text :
- https://doi.org/10.1007/s10489-023-04983-w