A multi-criteria decision framework for the holistic design of remotely operated vehicles using the analytic network process

The design of a Remotely Operated Vehicle (ROV) for underwater exploration requires careful consideration of multiple interrelated factors, including structural integrity, hydrodynamic performance, energy efficiency, and cost-effectiveness, among others. However, traditional ROV design methods often fail to adequately account for the complex relationships between these various factors, resulting in designs that lack optimal efficiency. This research presents a comprehensive Multi Criteria Decision Making (MCDM) framework utilizing the Analytical Network Process (ANP) technique to enhance the ROV design process. This ANP model categorizes key ROV design criteria into three main groups: Economic, Operational, and Technical. It employs aggregated pairwise comparisons across the selected sub-criteria and applies column normalization and priority-weight calculations to improve transparency and consistency in the ROV design process. Although ANP has been applied across other engineering domains, it has not yet been applied to ROV design, leaving a gap in structured decision-support tools for this application. This study addresses that gap by developing the first ANP-based framework tailored specifically to ROV design. The case study identified Reliability as the most influential sub-criterion, with a priority weight of 0.134, followed by Propulsion System at 0.122, Sensitivity at 0.091, and Work System at 0.089. These findings demonstrate how the proposed framework can support transparent prioritization of interrelated ROV design factors. This enables the prioritization of design factors based on established criteria and provides a systematic approach for selecting the most suitable design solution. These findings offer practical guidance for ROV designers to allocate engineering resources more effectively and make more defensible design decisions. This also gives managers a transparent basis for budget allocation and risk mitigation.