Risk is becoming an important factor in facilitating the resource allocation in engineering design because of its essential role in evaluating functional reliability and mitigating system failures. In this work, we aim at expanding existing quantitative risk modeling methods to collaborative system designs regarding resource allocation in a distributed environment, where an overlapped risk item can affect multiple stakeholders, and correspondingly be examined by multiple evaluators simultaneously. Because of different perspectives and limited local information, various evaluators (responsible for same or different components of a system), though adopting the same risk definition and mathematical calculation, can still yield unsatisfying global results, such as inconsistent probability and/or confusing consequence evaluations, which can then cause potential barriers in achieving agreement or acceptable discrepancies among different evaluators involved in the collaborative system design. Built upon our existing work, a Risk-based Distributed Resource Allocation Methodology (R-DRAM) is developed to help system manager allocate limited resource to stakeholders, and further to components of the targeted system for the maximum global risk reduction. Besides probability and consequence, two additional risk properties, tolerance and hierarchy, are considered for comprehensive systematic risk design. Tolerance is introduced to indicate the effective risk reduction, and hierarchy is utilized to model the comprehensive risk hierarchy. Finally a theoretical framework based on cost-benefit measure is developed for resource allocation. A case study is demonstrated to show the implementation process. The preliminary investigation shows promise of the R-DRAM in facilitating risk-based resource allocation for collaborative system design using a systematic and quantifiable approach in distributed environment.
An ontology-based approach to knowledge representation for Computer-Aided Control System Design