Advances in big data and artificial intelligence (AI) applications are encouraging improvements in the use of existing personal exposure data to predict personal exposure of new workplaces, creating safer work environment by considering the health risks of workers as early as the design stage. As workplace designs and their resulting exposure scenarios are correlated with a large variety of features, the amount of details and potential permutations of combinations can easily surpass the human capacity for conducting a comprehensive analysis that carefully considers each feature and their implication of the risk outcome. This provides a strong incentive for an automated, if not computer-assisted decision support system for making occupational health by design assessments. An automated workplace design process can be viewed as a problem of AI planning, in which the planning system must synthesize the best design strategy given a description of the workplace scenario and the desired objectives. However, several challenges arise for the implementation of such system, namely 1) the concern of using black-box models for high-stake decisions that may lead to unfair biases; 2) challenges in obtaining high-quality data for model training; and 3) challenges in optimizing decisions under competing objectives and deep uncertainties of future states. In this study, we present a simulation analysis of a many-objective workplace design problem to highlight the advantages of applying learning algorithm tools for exploring potential design strategies. A computational experiment was conducted based on Stoffenmanager®, an occupational exposure modeling tool, to generate potential exposure conditions for scenario discovery. A many-objective robust decision-making (MORDM) framework was used to compare the selected strategies based on overall preference and different regret metrics. We found that by combining mechanistic exposure models such as Stoffenmanager® along with MORDM tools such as many-objective evolution algorithm and patient rule induction methods can serve as a powerful foundation for exploratory modeling for workplace design. Based on the results, we propose an adaptive workplace design framework that will enable early indicators to be identified, thereby achieving a more proactive approach to make risk-informed design decisions.