Multi-objective hub-spoke network design of perishable tourism products using combination machine learning and meta-heuristic algorithms

Adel Pourghader Chobar, Mohammad Amin Adibi () and Abolfazl Kazemi
Additional contact information
Adel Pourghader Chobar: Islamic Azad University
Mohammad Amin Adibi: Islamic Azad University
Abolfazl Kazemi: Islamic Azad University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 10, No 3, 23237-23264

Abstract: Abstract In this research, the objective is to design a multi-objective Hub-Spoke network of perishable tourism products. In order to consider the perishable factor of the products, some collection centers are considered for the products which are perished. Accordingly, the combination of Hub-Spoke network and supply chain is assessed here. Moreover, this combination is to use transportation discounts in the supply chain network. The desired combination is done in such a way that the distributors are considered as a set of hubs. As the first objective is to reduce network costs and the second objective is to reduce the emission of environmental pollutants, for the model-based solution method, a combined solution method of meta-heuristic algorithm with machine learning technique has been developed, called Multi-Objective Artificial Immune System algorithm with Machine Learning (ML-MOAIS). Also, MOAIS and NSGA-II algorithms have been used to evaluate this solution method. Employing machine learning (ML) approach leads to limiting the space for problem solving and the faster convergence of the solutions to a feasible solution. This is time-saving and cost-effective. Evaluations and comparisons have been performed in two groups of qualitative and quantitative indicators and the results show that the designed algorithm has been superior to the MOAIS and NSGA-II algorithms on average in both qualitative and quantitative indicators. A detailed analysis of the indicators shows that the Pareto solutions of the proposed algorithm have a higher number, more spread around the Pareto front, and also a higher quality in terms of cost and emission of environmental pollutants.

Keywords: Modeling; Optimization techniques; Artificial intelligence; Supply chain (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-022-02350-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:10:d:10.1007_s10668-022-02350-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-022-02350-2

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().