Collaborative intuitionistic fuzzy-AHP to evaluate simulation-based analytics for freight transport

Globally, the transportation and logistics sector is facing economic disruptions owing to geopolitical tensions and post-COVID-19 global economic downturns. This disruption places more pressure on transportation companies to review their work methods and processes. Coupling data and model-driven app...

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Veröffentlicht in:Expert systems with applications 2023-09, Vol.225, p.120116, Article 120116
Hauptverfasser: Ben Rabia, Mohamed Amine, Bellabdaoui, Adil
Format: Artikel
Sprache:eng
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Zusammenfassung:Globally, the transportation and logistics sector is facing economic disruptions owing to geopolitical tensions and post-COVID-19 global economic downturns. This disruption places more pressure on transportation companies to review their work methods and processes. Coupling data and model-driven approaches is essential for developing effective and efficient resilience strategies. To address this issue, this study provides an overview of the appearance of simulation in business analytics. However, a thorough review of the literature based on the PRISMA search process allowed us to identify that none of the previous studies could highlight the role or evaluate the hybridization between business analytics and simulation and their joint use in freight transportation. Moreover, this study proposes a collaborative framework based on the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique to select a business analytics-enabled simulation architecture. This study contributes to the freight transport sector by setting up an updated list of criteria and sub-criteria necessary for business analytics evaluation and enriches the literature by applying the IF-AHP technique to a concrete case of implementing data analytics and simulation. This study also suggests future directions to enrich the academic literature and offers insights to improve the framework for other use cases.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120116