Evaluating the effectiveness of drones in emergency situations: a hybrid multi-criteria approach
Document Type
Article
Publication Date
1-1-2021
Abstract
Purpose: The paper aims to build a customized hybrid multi-criteria model to identify the top three utilities of drones at both personal and community levels for two use cases: firefighting in high-rise buildings and logistic support. Design/methodology/approach: A hybrid multi-criterion model that integrates fuzzy analytical hierarchy process (AHP), Best Worst, fuzzy analytical network process (ANP), fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to compute the criteria weights. The weights are validated by a novel ensemble ranking technique further whetted by experts at the community and personal levels to two use cases. Findings: Drones' fire handling and disaster recovery utilities are the most important to fight fire in high-rise buildings at both personal and community levels. Similarly, drones' urban planning, municipal works and infrastructure inspection utilities are the most important for providing logistics support at personal and community levels. Originality/value: The paper presents a novel multi-criteria approach, i.e. ensemble ranking, by combining the criteria ranking of individual methods – fuzzy AHP, Best-Worst, fuzzy ANP and fuzzy DEMATEL – in the ratio of optimal weights to each technique to generate the consolidated ranking. Domain experts also validate this ranking for robustness. This paper demonstrates a viable methodology to quantify the utilities of drones and their capabilities. The proposed model can be recalibrated for different use case scenarios of drones.
Publication Title
Industrial Management and Data Systems
Digital Object Identifier (DOI)
10.1108/IMDS-01-2021-0064
ISSN
02635577
Citation Information
Zhang, J.Z., Srivastava, P.R. and Eachempati, P. (2021), "Evaluating the effectiveness of drones in emergency situations: a hybrid multi-criteria approach", Industrial Management & Data Systems, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IMDS-01-2021-0064