Knisely, Benjamin MartinHumans have heterogeneous physical and cognitive capabilities. Engineers must cater to this heterogeneity to minimize opportunities for user error and system failure. Human factors considerations are typically evaluated late in the design process, risking expensive redesign when new human concerns become apparent. Evaluating user capability earlier could mitigate this risk. One critical early-stage design decision is function allocation – assigning system functions to humans and machines. Automating functions can eliminate the need for users to perform risky tasks but increases resource requirements. Engineers require guidance to evaluate and optimize function allocation that acknowledges the trade-offs between user accommodation and system complexity. In this dissertation, a multi-stage design methodology is proposed to facilitate the efficient allocation of system functions to humans and machines in heterogeneous user populations. The first stage of the methodology introduces a process to model population user groups to guide product customization. User characteristics that drive performance of generalized product interaction tasks are identified and corresponding variables from a national population database are clustered. In stage two, expert elicitation is proposed as a cost-effective means to quantify risk of user error for the user group models. Probabilistic estimates of user group performance are elicited from internal medicine physicians for generalized product interaction tasks. In the final stage, the data (user groups, performance estimations) are integrated into a multi-objective optimization model to allocate functions in a product family when considering user accommodation and system complexity. The methodology was demonstrated on a design case study involving self-management technology use by diabetes patients, a heterogeneous population in a safety-critical domain. The population modeling approach produced quantitatively and qualitatively validated clusters. For the expert elicitation, experts provided internally validated, distinct estimates for each user group-task pair. To validate the utility of the proposed method (acquired data, optimization model), engineering students (n=16) performed the function allocation task manually. Results indicated that participants were unable to allocate functions as efficiently as the model despite indicating user capability and cost were priorities. This research demonstrated that the proposed methodology can provide engineers valuable information regarding user capability and system functionality to drive accessible early-stage design decisions.enDissertationMechanical engineeringEngineeringAccessibilityData-driven DesignDesign MethodologyEngineering DesignHuman FactorsHuman Performance