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Design decision-making under competition is a critical challenge in real-world engineering design. These challenges are compounded by bounded rationality, where cognitive limitations and imperfect information influence decision-making strategies. To address these issues, we develop a game-theoretic research platform to investigate team-based design under competition. This platform abstracts and simulates real-world competitive design scenarios through controlled experiments. It features a user-friendly interface to collect behavioral data, which supports the analysis of team and individual strategies. Additionally, we validated the platform through a pilot study, demonstrating its ability to capture realistic design features and generate meaningful insights into competitive design behaviors. 

Rational decision-making is crucial in the later stages of engineering system design to allocate resources efficiently and minimize costs. However, human rationality is bounded by cognitive biases and limitations. Understanding how humans deviate from rationality is critical for guiding designers toward better design outcomes. In this paper, we quantify designer rationality in competitive scenarios based on utility theory. Using an experiment inspired by crowd-sourced contests, we show that designers employ varied search strategies. Some participants approximate a Bayesian agent that aimed to maximize its expected utility. Those with higher rationality reduce uncertainty more effectively. Furthermore, rationality correlates with both the proximity to optimal design and design iteration costs, with winning participants exhibiting greater rationality than losing participants. 

Abstract Swarm manufacturing is an emerging manufacturing paradigm that employs a heterogeneous swarm of robots to accomplish complex hybrid manufacturing tasks. Cooperative 3D printing (C3DP), a specialized form of swarm manufacturing, enables multiple printers to collaboratively produce large-scale parts, addressing key tradeoffs in additive manufacturing, such as size, speed, quality, and cost. A fundamental challenge in C3DP is ensuring collision-free, time-optimal printing in a shared workspace. This is a complex problem that can be influenced by factors such as the number of printers, part geometry, printer positioning, mobility, and kinematics. In this article, we present SafeZone*, a collision-free and scalable C3DP framework that optimizes printing time by co-considering the geometry (area and shape) and topology (space-connectivity) of a shared workspace during layer partitioning. We first establish a conceptual framework to mathematically represent the topology of a layer through partition graphs. Then, we use a Voronoi tessellation within a constrained optimization framework to control the partition graph and minimize makespan. The Voronoi sites are associated with printer locations, allowing the framework to integrate physical constraints and facilitating solutions for systems with robotic manipulators. Physical testing in a four-printer scenario with robotic arms confirms that SafeZone* enables collision-free printing, resulting in a printing time reduction of 44.63% when compared to the single-printer scenario. Finally, numerical studies reveal trends in the optimal solutions concerning the chromatic number of their resulting partition graphs and the distribution of the printing areas among printers. 

Network-based analyses have effectively understood customer preferences through interactions between customers and products, particularly for tailored product design. However, research applying this analysis to diverse customers with varied preferences is limited. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers’ two-stage decision-making process: consideration-then-choice. In heterogeneous markets, customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, this method segments customers based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using joint correspondence analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer–product networks and apply the exponential random graph model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how understanding customer preferences at different decision stages can inform and refine design strategies, ensuring products align with diverse market needs. 

Abstract We present NoodlePrint, a generalized computational framework for maximally concurrent layer-wise cooperative 3D printing (C3DP) of arbitrary part geometries with multiple robots. NoodlePrint is inspired by a recently discovered set of helically interlocked space-filling shapes called VoroNoodles. Leveraging this unique geometric relationship, we introduce an algorithmic pipeline for generating helically interlocked cellular segmentation of arbitrary parts followed by layer-wise cell sequencing and path planning for cooperative 3D printing. Furthermore, we introduce a novel concurrence measure that quantifies the amount of printing parallelization across multiple robots. Consequently, we integrate this measure to optimize the location and orientation of a part for maximally parallel printing. We systematically study the relationship between the helix parameters (i.e., cellular interlocking), the cell size, the amount of concurrent printing, and the total printing time. Our study revealed that both concurrence and time to print primarily depend on the cell size, thereby allowing the determination of interlocking independent of time to print. To demonstrate the generality of our approach with respect to part geometry and the number of robots, we implemented two cooperative 3D printing systems with two and three printing robots and printed a variety of part geometries. Through comparative bending and tensile tests, we show that helically interlocked part segmentation is robust to gaps between segments. 

Abstract System design has been facing the challenges of incorporating complex dependencies between individual entities into design formulations. For example, while the decision-based design framework successfully integrated customer preference modeling into optimal design, the problem was formulated from a single entity’s perspective, and the competition between multiple enterprises was not considered in the formulation. Network science has offered several solutions for studying interdependencies in various system contexts. However, efforts have primarily focused on analysis (i.e., the forward problem). The inverse problem still remains: How can we achieve the desired system-level performance by promoting the formation of targeted relations among local entities? In this study, we answer this question by developing a network-based design framework. This framework uses network representations to characterize and capture dependencies and relations between individual entities in complex systems and integrate these representations into design formulations to find optimal decisions for the desired performance of a system. To demonstrate its utility, we applied this framework to the design for market systems with a case study on vacuum cleaners. The objective is to increase the sales of a vacuum cleaner or its market share by optimizing its design attributes, such as suction power and weight, with the consideration of market competition relations, such as inter-brand triadic competition involving three products from different brands. We solve this problem by integrating an exponential random graph model (ERGM) with a genetic algorithm. The results indicate that the new designs, which consider market competition, can effectively increase the purchase frequency of specific vacuum cleaner models and the proposed network-based design method outperforms traditional design optimization. 

In multi-agent Bayesian optimization for Design Space Exploration (DSE), identifying a communication network among agents to share useful design information for enhanced cooperation and performance, considering the trade-off between connectivity and cost, poses significant challenges. To address this challenge, we develop a distributed multi-agent Bayesian optimization (DMABO) framework and study how communication network structures/connectivity and the resulting cost would impact the performance of a team of agents when finding the global optimum. Specifically, we utilize Lloyd’s algorithm to partition the design space to assign distinct regions to individual agents for exploration in the distributed multi-agent system (MAS). Based on this partitioning, we generate communication networks among agents using two models: 1) a range-limited model of communication constrained by neighborhood information; and 2) a range-free model without neighborhood constraints. We introduce network density as a metric to quantify communication costs. Then, we generate communication networks by gradually increasing the network density to assess the impact of communication costs on the performance of MAS in DSE. The experimental results show that the communication network based on the range-limited model can significantly improve performance without incurring high communication costs. This indicates that increasing the density of a communication network does not necessarily improve MAS performance in DSE. Furthermore, the results indicate that communication is only beneficial for team performance if it occurs between specific agents whose search regions are critically relevant to the location of the global optimum. The proposed DMABO framework and the insights obtained can help identify the best trade-off between communication structure and cost for MAS in unknown design space exploration. 

Effective coordination of design teams must account for the influence of costs incurred while searching for the best design solutions. This article introduces a cost-aware multi-agent system (MAS), a theoretical model to (1) explain how individuals in a team should search, assuming that they are all rational utility-maximizing decision-makers and (2) study the impact of cost on the search performance of both individual agents and the system. First, we develop a new multi-agent Bayesian optimization framework accounting for information exchange among agents to support their decisions on where to sample in search. Second, we employ a reinforcement learning approach based on the multi-agent deep deterministic policy gradient for training MAS to identify where agents cannot sample due to design constraints. Third, we propose a new cost-aware stopping criterion for each agent to determine when costs outweigh potential gains in search as a criterion to stop. Our results indicate that cost has a more significant impact on MAS communication in complex design problems than in simple ones. For example, when searching in complex design spaces, some agents could initially have low-performance gains, thus stopping prematurely due to negative payoffs, even if those agents could perform better in the later stage of the search. Therefore, global-local communication becomes more critical in such situations for the entire system to converge. The proposed model can serve as a benchmark for empirical studies to quantitatively gauge how humans would rationally make design decisions in a team. 

Abstract Swarm manufacturing (SM) is an emerging manufacturing paradigm that employs a heterogeneous swarm of robots to accomplish complex hybrid manufacturing tasks. Cooperative 3D Printing (C3DP), a special form of swarm manufacturing, uses multiple printers to print large-scale parts cooperatively and aims to tackle key challenges in the additive manufacturing industry, such as trade-offs among size, speed, quality, and cost. A fundamental challenge in C3DP is how to achieve collision-free, time-efficient printing when multiple printers operate in a shared workspace. This is a complex problem since the solution may depend on a myriad of factors, such as the number of printers, part geometry, printer positioning, mobility, and kinematics, or whether the printing path pre-determined. In this paper, we present SafeZone, a collision-free and scalable C3DP framework that aims to minimize printing time by considering both the geometry and topology (space-connectivity) of the resulting workspace when segmenting the part layer. To achieve this, we use a guided Voronoi tessellation that can only produce degree-3 partitions, which we show to have optimal scheduling properties based on the chromatic number of the resulting partition graph. The sites of the Voronoi tessellation are constrained to only lie on the boundary of their convex hull, thus facilitating collision-free operation in C3DP systems with robotic arms. We demonstrate through physical testing in a 4-printer scenario with SCARA arms that SafeZone can produce collision-free prints, resulting in a printing time reduction of 44.63% when compared to the single-printer scenario. Finally, we show how the partition created by our methodology has a printing time reduction of 22.83% when compared to a naive choice which does not consider workspace topology.