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1. Evaluating Heuristics in Engineering Design: A Reinforcement Learning Approach

   ElSayed, K.A.; Bilionis, I.; Panchal, J.H. (August 2021, ASME IDETC)

   null (Ed.)

   Heuristics are essential for addressing the complexities of engineering design processes. The goodness of heuristics is context-dependent. Appropriately tailored heuristics can enable designers to find good solutions efficiently, and inappropriate heuristics can result in cognitive biases and inferior design outcomes. While there have been several efforts at understanding which heuristics are used by designers, there is a lack of normative understanding about when different heuristics are suitable. Towards addressing this gap, this paper presents a reinforcement learning-based approach to evaluate the goodness of heuristics for three sub-problems commonly faced by designers while carrying out design under resource constraints: (i) learning the mapping between the design space and the performance space, (ii) sequential information acquisition in design, and (iii) decision to stop information acquisition. Using a multi-armed bandit formulation and simulation studies, we learn the heuristics that are suitable for these sub-problems under different resource constraints and problem complexities. The results of our simulation study indicate that the proposed reinforcement learning-based approach can be effective for determining the quality of heuristics for different sub-problems, and how the effectiveness of the heuristics changes as a function of the designer's preference (e.g., performance versus cost), the complexity of the problem, and the resources available. 

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2. Extracting Design Process Heuristics: A Systematic Computational Approach

   https://doi.org/10.1115/DETC2025-168784  

   Gadi, Vikranth S; Szajnfarber, Zoe; Panchal, Jitesh H (August 2025, American Society of Mechanical Engineers)

   Abstract Engineering design relies heavily on heuristics, yet there is a lack of systematic methods for identifying and validating design heuristics. This paper introduces a computational approach to representing engineering design problems that involve decomposition and assignment decisions, facilitating systematic extraction of generalizable heuristics. We model design processes using a Markov Decision Process (MDP) framework, characterizing problems through attributes of the problem space, solver capabilities, and trade-offs embedded within preference functions. Reinforcement learning methods are employed to learn optimal policies, from which we extract inclusionary and exclusionary heuristics using Gaussian Mixture Models. The effectiveness of the approach is demonstrated through two case studies: solver-aware system architecting (SASA) for a robotic arm design and sequential information acquisition in parametric design optimization. The results highlight the context-dependent nature of learned heuristics, demonstrating how problem complexity, designer preferences, and solver characteristics influence their selection. 

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3. Developing Heuristics for Resource Allocation and Utilization in Systems Design: A Hierarchical Reinforcement Learning Approach

   https://doi.org/10.1115/1.4068449  

   Gadi, Vikranth S; Szajnfarber, Zoe; Panchal, Jitesh H (June 2025, Journal of Mechanical Design)

   Abstract Systems design involves decomposing a system into interconnected subsystems and allocating resources to teams responsible for designing each subsystem. The outcomes of the process depend on how well limited resources are allocated to different teams, and the strategy each team uses to design the subsystems. This article presents an approach based on hierarchical reinforcement learning (RL) to generate heuristics for solving complex design problems under resource constraints. The approach consists of formulating systems design problems as hierarchical multiarmed bandit (MAB) problems, where decisions are made at both the system level (allocating budget across subsystems) and the subsystem level (selecting heuristics for sequential information acquisition). The approach is demonstrated using an illustrative example of a race car optimization in The Open Racing Car Simulator (TORCS) environment. The results indicate that the RL agent can learn to allocate resources strategically, prioritize the subsystems with the greatest influence on overall performance, and identify effective information acquisition heuristics for each subsystem. For example, the RL agent learned to allocate a larger portion of the budget to the gearbox subsystem, which has a higher-dimensional design space compared to other subsystems. The results also indicate that the extracted heuristics lead to convergence to high-performing car configurations with greater efficiency when compared to using Bayesian optimization for design. 

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4. Leveraging Design Heuristics for Multi-Objective Metamaterial Design Optimization

   Kumar, Roshan S; Srivasta, Srikar; Silberstein, Meredith N; Selva, Daniel (July 2021, IDETC/CIE2021)

   null (Ed.)

   Design optimization of metamaterials and other complex systems often relies on the use of computationally expensive models. This makes it challenging to use global multi-objective optimization approaches that require many function evaluations. Engineers often have heuristics or rules of thumb with potential to drastically reduce the number of function evaluations needed to achieve good convergence. Recent research has demonstrated that these design heuristics can be used explicitly in design optimization, indeed leading to accelerated convergence. However, these approaches have only been demonstrated on specific problems, the performance of different methods was diverse, and despite all heuristics being ``correct'', some heuristics were found to perform much better than others for various problems. In this paper, we describe a case study in design heuristics for a simple class of 2D constrained multiobjective optimization problems involving lattice-based metamaterial design. Design heuristics are strategically incorporated into the design search and the heuristics-enabled optimization framework is compared with the standard optimization framework not using the heuristics. Results indicate that leveraging design heuristics for design optimization can help in reaching the optimal designs faster. We also identify some guidelines to help designers choose design heuristics and methods to incorporate them for a given problem at hand. 

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5. Leveraging Design Heuristics for Multi-Objective Metamaterial Design Optimization

   Kumar, Roshan S; Srivasta, Srikar; Silberstein, Meredith N; Selva, Daniel (July 2021, IDETC/CIE2021)

   null (Ed.)

   Design optimization of metamaterials and other complex systems often relies on the use of computationally expensive models. This makes it challenging to use global multi-objective optimization approaches that require many function evaluations. Engineers often have heuristics or rules of thumb with potential to drastically reduce the number of function evaluations needed to achieve good convergence. Recent research has demonstrated that these design heuristics can be used explicitly in design optimization, indeed leading to accelerated convergence. However, these approaches have only been demonstrated on specific problems, the performance of different methods was diverse, and despite all heuristics being correct'', some heuristics were found to perform much better than others for various problems. In this paper, we describe a case study in design heuristics for a simple class of 2D constrained multiobjective optimization problems involving lattice-based metamaterial design. Design heuristics are strategically incorporated into the design search and the heuristics-enabled optimization framework is compared with the standard optimization framework not using the heuristics. Results indicate that leveraging design heuristics for design optimization can help in reaching the optimal designs faster. We also identify some guidelines to help designers choose design heuristics and methods to incorporate them for a given problem at hand. 

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