Ullah Khan Habib, Abbas Muhammad, Nazir Shah, Khan Faheem, Hussain Jamil
Accounting & Information Systems at the College of Business and Economics, Qatar University, Doha, Qatar.
Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Pakistan, Swabi, KPK, Pakistan.
PeerJ Comput Sci. 2024 Oct 15;10:e2308. doi: 10.7717/peerj-cs.2308. eCollection 2024.
Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study's results.
物联网（IoT）的快速发展为众多领域带来了变革性应用，尤其是在汽车行业。随着基于物联网的海上车辆的引入，对于高效评估和决策方法的需求日益增长，以确定汽车行业最具潜力的选择。为解决这一问题，本研究提出的集成多准则决策分析（MCDA）范式结合了加法比率评估（ARAS）和层次分析法（AHP），以根据车辆行业中基于性能和真实性的标准来评估和选择基于物联网的海上车辆。选择问题被分层组织，评估标准使用AHP方法进行优先级排序。选择了七个与性能和认证相关的标准，这些标准可能有助于选择过程。使用AHP，我们为这些标准分配了反映其各自重要性和相互关系的成比例权重。然而，AHP不足以对现有替代方案进行全面分析。为克服这些限制，本研究提出将AHP与ARAS方法相结合，以便根据替代方案相对于既定标准的表现对其进行排名。通过使用ARAS技术，可以克服AHP的限制，并对基于海上物联网的车辆进行更全面的评估。使用经验数据和专业判断证明了该框架的有效性。结果表明，混合方法成功地概括了被评估因素之间的复杂关系，并客观地评估了基于物联网的海上车辆在汽车行业的潜力。本研究通过提供一种有组织、全面的方法来评估和选择基于物联网的海上车辆，扩展了该领域。考虑到多个因素及其相互依赖性，AHP和ARAS混合技术为决策者提供了一个强大的工具，用于评估基于物联网的海上车辆在汽车行业的潜力。汽车行业的受益者可以利用该研究的结果，对基于物联网的海上车辆的部署做出明智决策，并最大限度地发挥其潜力。
