Christopher McComb

Drawing Inspiration From Human Design Teams for Better Search and Optimization: The Heterogeneous Simulated Annealing Teams Algorithm

Insights uncovered by research in design cognition are often utilized to develop methods used by human designers; in this work, such insights are used to inform and improve computational methodologies. This paper introduces the heterogeneous simulated annealing team (HSAT) algorithm, a multiagent simulated annealing (MSA) algorithm. HSAT is based on a validated computational model of human-based engineering design and retains characteristics of the model that structure interaction between team members and allow for heterogeneous search strategies to be employed within a team. The performance of this new algorithm is compared to several other simulated annealing (SA) based algorithms on three carefully selected benchmarking functions. The HSAT algorithm provides terminal solutions that are better on average than other algorithms explored in this work.

Mining Process Heuristics from Designer Action Data via Hidden Markov Models

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and parameter design. High-performing designers are distinguished by their opportunistic tuning of parameters early in the process, enabling a more effective and nuanced search for solutions. [DOI: 10.1115/1.4037308]

Capturing Human Sequence-Learning Abilities in Configuration Design Tasks Through Markov Chains

Design often involves searching for a solution by iteratively modifying and adjusting a current design. Through this process, designers improve the quality of the current design as well as learning what patterns of operations are most likely to lead to the quickest future improvements. Prior work in psychology has shown that humans can be adept at learning how to apply short sequences of operations for maximum effect while solving a problem. This work explores the sequencing of operations specifically within the domain of engineering design by examining the results of two human studies in which participants created solutions to configuration design problems. First, a statistical analysis of the data from those studies uses Markov chains to show that meaningful operation sequences exist, and can be accurately described using first-order models. Second, this work uses an agent-based modeling framework in conjunction with Markov chain concepts to simulate the performance of teams with and without the ability to learn sequences. These computational studies confirm the assumption that sequence-learning abilities are helpful during design. Journal of Mechanical Design

Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test

The performance of a team with the right characteristics can exceed the mere sum of the constituent members’ individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources allotted for solving the problem. Regression analysis is then used to create equations for predicting optimized values for team characteristics based on problem properties. These equations achieve moderate to high accuracy, making it possible to design teams based on those problem properties. Further analysis reveals hypotheses about how the problem properties can influence a team’s search for solutions. This work also conducts a cognitive study on a different problem to test the predictive equations. For a configuration problem of moderate size, the model predicts that zero interaction between team members should lead to the best outcome. A cognitive study of human teams verifies this surprising prediction, offering partial validation of the predictive theory. [DOI: 10.1115/1.4035793]

Studying Human Design Teams via Computational Teams of Simulated Annealing Agents

Novel design methodologies are often evaluated through empirical studies involving human designers. However, such empirical studies can incur a high personnel cost. Further, it can be difficult to isolate the effects of specific team or individual characteristics. These limitations could be bypassed by employing a computational model of design teams. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, an agent-based platform that provides a means for efficiently simulating human design teams. A number of empirically demonstrated cognitive phenomena are modeled within the platform, striking a balance between model simplicity and direct applicability to engineering design problems. This paper discusses the composition of the CISAT modeling framework and demonstrates how it can be used to simulate the performance of human design teams in a cognitive study. Results simulated with CISAT are compared directly to the results derived from human designers. Finally, the CISAT model is also used to investigate the characteristics that were most and least helpful to teams during the cognitive study.Copyright

Improving Irrigation in Remote Areas: Multi-Objective Optimization of a Treadle Pump

Water-lifting technologies in rural areas of the developing world have enormous potential to stimulate agricultural and economic growth. The treadle pump, a human-powered low-cost pump designed for irrigation in developing countries, can help farmers maximize financial return on small plots of land by ending their dependency on rain-fed irrigation systems. The treadle pump uses a suction piston to draw groundwater to the surface by way of a foot-powered treadle attached to each suction piston. Current treadle pump designs lift water from depths up to 7 meters at a flow-rate of 1–5 liters per second. This work seeks to optimize the design of the Dekhi style treadle pump, which has gained significant popularity due to its simplicity. A mathematical model of the working fluid and treadle pump structure has been developed in this study. Deterministic optimization methods are then employed to maximize the flow rate of the groundwater pumped, maximize the lift height, and minimize the volume of material used for manufacturing. Design variables for the optimization included the dimensions of the pump, well depth, and speed of various parts of the system. The solutions are subject to constraints on the geometry of the system, the bending stress in the treadles, and ergonomic factors. Findings indicate that significant technical improvements can be made on the standard Dekhi design, such as increasing the size of the pump cylinders and hose, while maintaining a standard total treadle length. These improvements could allow the Dekhi pump to be implemented in new regions and benefit additional rural farmers in the developing world.Copyright

Quantitative Comparison of High- and Low-Performing Teams in a Design Task Subject to Drastic Changes

Many design tasks are subject to changes in goals or constraints. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. A design team often cannot anticipate such changes, yet they pose a considerable challenge. This paper presents a study where engineering teams sought to solve a design task that was subject to two large, unexpected changes in problem formulation that occurred during problem solving. Continuous design data was collected to observe how the designers responded to the changes. We show that high- and low-performing teams demonstrated very different approaches to solving the problem and overcoming the changes. In particular, high-performing teams achieved simple designs and extensively explored small portions of the design space; low-performing teams explored complex designs with little exploration around a target area of the design space. These strategic differences are interpreted with respect to cognitive load theory and goal theory. The results raise questions as to the relationship between characteristics of design problems and solution strategies. In addition, an attempt at increasing the teams’ resilience in the face of unexpected changes is introduced by encouraging early divergent search.Copyright

Utilizing Markov Chains to Understand Operation Sequencing in Design Tasks

Design often involves searching for a final design solution by iteratively modifying and adjusting a current design. Through this process designers are able to improve the quality of the current design and also learn what patterns of operations are most likely to lead to the quickest future improvements. Prior work in psychology has shown that humans can be adept at learning how to apply short sequences of operations for maximum effect while solving a problem. This work explores the sequencing of operations specifically within the domain of engineering design by examining the results of a human study in which participants designed trusses. A statistical analysis of the data from that study uses Markov Chains to show with high confidence that meaningful operation sequences exist. This work also uses an agent-based modeling framework in conjunction with Markov Chain concepts to simulate the performance of teams with and without the ability to learn sequences. These computational studies offer confirmation for the conclusion that sequence-learning abilities are helpful during design.

Identifying technical and economic improvements to the MoneyMaker Hip pump through multi-objective optimization

Water-lifting technologies in rural areas of the developing world have enormous potential to stimulate agricultural production and economic growth. The MoneyMaker Hip pump designed by Kickstart is a human-powered low cost pump, which can help subsistence farmers increase financial returns. This work optimizes the design of the MoneyMaker Hip pump, which is the cheapest and lightest portable water pump from Kickstart. A mathematical model of the working fluid and MoneyMaker Hip pump structure is developed. Deterministic optimization methods are then employed to maximize the flow rate of the groundwater pumped, maximize the lift height, and minimize the volume of material used for manufacturing. Design variables for the optimization included the dimensions of the pump, well depth, and speed of various parts of the system. The solutions are subject to constraints on the geometry of the system, the bending stress in the handle and cylinder, and ergonomic factors. Findings indicate that several technical improvements can be made on the current MoneyMaker Hip pump design to reduce the cost and human workload.

Data on the design of truss structures by teams of engineering students

This experiment was conducted in order to compare different approaches that human teams use to solve design problems that change dynamically during solving. Specifically, study participants were given the task of designing a truss structure (similar to a bridge spanning a chasm) in teams of three. At two points during design, the problem statement was changed unexpectedly, requiring participants to adapt. Two conditions were given different initial problem representations. During the study, every participant had access to a computer interface that allowed them to construct, test, and share solutions. The interface also made it possible to collect a step-by-step log of the actions made by participants during the study. This article contains data collected from 48 participants (16 teams). This data has been used previously in behavioral analyses, sequence-based analysis, and development of computational models.