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Diversification-based learning simulated annealing algorithm for hub location problems

Himanshu Rathore (Indian Institute of Management, Rohtak, India)
Shirsendu Nandi (Indian Institute of Management, Rohtak, India)
Peeyush Pandey (Indian Institute of Management, Rohtak, India)
Surya Prakash Singh (Department of Management Studies, Indian Institute of Technology, New Delhi, India)

Benchmarking: An International Journal

ISSN: 1463-5771

Article publication date: 10 June 2019

Issue publication date: 31 July 2019

Abstract

Purpose

The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems.

Design/methodology/approach

This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets.

Findings

This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates.

Practical implications

Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time.

Originality/value

To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

Keywords

Citation

Rathore, H., Nandi, S., Pandey, P. and Singh, S.P. (2019), "Diversification-based learning simulated annealing algorithm for hub location problems", Benchmarking: An International Journal, Vol. 26 No. 6, pp. 1995-2016. https://doi.org/10.1108/BIJ-04-2018-0092

Publisher

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Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited