Chris Hoyle - Associate Professor Dr. Chris Hoyle

Associate Professor
School of Mechanical, Industrial, and Manufacturing Engineering
Oregon State University
 

mail:

Oregon State University
School of Mechanical, Industrial,
and Manufacturing Engineering
204 Rogers Hall
Corvallis, OR 97331-6001

email: chris.hoyle@oregonstate.edu
phone: (541) 737-7035


Office Location:
418 Rogers

Biography

Dr. Hoyle’s interest is in developing mathematically sound methods to help formalize the design and decision making process and optimize systems to make them more robust. His current research interests are focused upon decision making in engineering design, with emphasis on the early design phase when uncertainty is high and the potential design space is large. His areas of expertise are uncertainty quantification methodologies, Bayesian statistics and modeling, stochastic consumer choice modeling, optimization and design automation.

Dr. Hoyle received his PhD from Northwestern University in Mechanical Engineering in 2009 and his Master’s degree in Mechanical Engineering from Purdue University in 1994. He served as an Adjunct Professor of Mechanical Engineering at Illinois Institute of Technology in 2009 and was an Intern at NASA Ames in 2006. He was previously a Design Engineer, an Engineering Manager, and a Program Manager at Motorola for 10 years before enrolling in the PhD program at Northwestern University.

Curriculum Vitae

 Hoyle CV

Publications

 Journal Publications
  1. Hulse, D., & Hoyle, C. (2022). "Understanding Resilience Optimization Architectures: Alignment and Coupling in Multilevel Decomposition Strategies." Journal of Mechanical Design, 144(11), 111704
  2. Biswas, A., Fuentes, C., and Hoyle, C. (2022). "A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems." ASME. J. Comput. Inf. Sci. Eng. doi: https://doi.org/10.1115/1.4054480
  3. Biswas, A., Fuentes, C., & Hoyle, C. (2021). A Multi-Objective Bayesian Optimization Approach Using the Weighted Tchebycheff Method. Journal of Mechanical Design, 144(1), 011703.
  4. Chen, Y., Gibson, N., Biswas, A., Li, A., Bashiri, H., Sharifi, E., Fuentes, C., Hoyle, C., Leon, A.S. and Skypeck, C.J., (2021). Valuation of operational flexibility: A case study of Bonneville power administration. Energy Economics, 98, p.105251.
  5. Hulse, D., Biswas, A., Hoyle, C., Tumer, I. Y., Kulkarni, C., & Goebel, K. (2021). Exploring Architectures for Integrated Resilience Optimization. Journal of Aerospace Information Systems, 1-14.
  6. Hulse, D., Walsh, H., Dong, A., Hoyle, C., Tumer, I., Kulkarni, C., & Goebel, K. (2021). fmdtools: A Fault Propagation Toolkit for Resilience Assessment in Early Design. International Journal of Prognostics and Health Management, 12(3).
  7. Biswas, A. and Hoyle, C. (2021). An Approach to Bayesian Optimization for Design Feasibility Check on Discontinuous Black-Box Functions. Journal of Mechanical Design, Jan 12, 1-26.
  8. Hulse, D., Hoyle, C., Tumer, I. Y., and Goebel, K. (2021). How Uncertain Is Too Uncertain? Validity Tests for Early Resilient and Risk-Based Design Processes. Journal of Mechanical Design, 143(1), 011702.
  9. Biswas, A., Chen, Y., Gibson, N., and Hoyle, C. (February 7, 2020). Bi-Level Flexible-Robust Optimization for Energy Allocation Problems.. ASCE-ASME J. Risk Uncertainty Part B, 6(3), 031002.
  10. O’Halloran, B. M., Hoyle, C., Tumer, I. Y., & Stone, R. B. (2019). The early design reliability prediction method. Research in Engineering Design, 30(4), 489-508.
  11. Piacenza, J. R., Faller, K. J., Bozorgirad, M. A., Cotilla-Sanchez, E., Hoyle, C., & Tumer, I. (2019). Understanding the Impact of Decision Making on Robustness During Complex System Design: More Resilient Power Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering.
  12. Hulse, D., Hoyle, C., Goebel, K., & Tumer, I. Y. (2019). Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment. Journal of Mechanical Design, 141(2), 021403.
  13. Hulse, D., Tumer, K., Hoyle, C., & Tumer, I. (2019). Modeling multidisciplinary design with multiagent learning. AI EDAM, 33(1), 85-99.
  14. Zurita, N. F. S., Colby, M. K., Tumer, I. Y., Hoyle, C., & Tumer, K. (2018). Design of Complex Engineered Systems Using Multi-Agent Coordination. Journal of Computing and Information Science in Engineering, 18(1), 011003.
  15. Piacenza, J. R., Proper, S., Bozorgirad, M. A., Hoyle, C., & Tumer, I. Y. (2017). Robust Topology Design of Complex Infrastructure Systems. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 3(2), 021006.
  16. Keshavarzi, E., McIntire, M., & Hoyle, C. (2017). A dynamic design approach using the Kalman filter for uncertainty management. AI EDAM, 31(2), 161-172.
  17. Mehrpouyan, H., Giannakopoulou, D., Brat, G., Tumer, I. Y., & Hoyle, C. (2016). Complex Engineered Systems Design Verification Based on Assume‐Guarantee Reasoning. Systems Engineering, 19(6), 461-476.
  18. Mehrpouyan, H., Giannakopoulou, D., Brat, G., Tumer, I.Y., and Hoyle, C., “Towards A Framework for Resilient Design of Complex Engineered Systems,” Research in Engineering Design. Accepted with revision, 2016.
  19. McIntire, M. G., Hoyle, C., Tumer, I. Y., & Jensen, D. C. (2016). Safety-informed design: Using subgraph analysis to elicit hazardous emergent failure behavior in complex systems. AI EDAM, 30(4), 466-473.
  20. DuPont, B., Azam, R., Proper, S., Cotilla-Sanchez, E., Hoyle, C., Piacenza, J., Oryshchyn, D., Zitney, S., and Bossart, S., “An Optimization Framework for Decision Making in Large, Collaborative Energy Supply Systems,” Journal of Energy Resources Technology, 138(5), 051601, 2016.
  21. Mehrpouyan, H., Haley, B., Dong, A., Hoyle, C., Tumer, I.,“Resiliency Analysis for Complex Engineered System Design”, AI EDAM Special Issue on the Design of Complex Engineering Systems, 29(1), pp 93-108, 2015.
  22. Jensen, D., Bello, O., Hoyle, C., Tumer, I., “Reasoning about Emergent System Failure Behavior Using Large Sets of Qualitative Function-Based Simulation Data”, AI EDAM Special Issue on the Design of Complex Engineering Systems, 28(4), pp 385-398, 2014.
  23. Yannou, B., Yvars, P.A., Hoyle, C., Chen, W., “Set-Based Design by Simulation of Usage Scenario Coverage”, Journal of Engineering Design, 24(8), 575-603, 2013.
  24. Van Bossuyt, D., Hoyle, C., Tumer, I., Dong, A., “Risk attitudes in risk-based design: Considering Risk Attitude Using Utility Theory in Risk-Based Design,” AI EDAM Special Issue on Intelligent Decision Support and Modelling, Vol. 26, No. 4, 2012.
  25. He, L., Chen, W., Hoyle, C., Yannou, B., “Choice Modeling for Usage Context-Based Design,” Journal of Mechanical Design, Vol. 134, No. 3, 2012.
  26. He, L., Hoyle, C., Chen, W., “Examination of Customer Satisfaction Surveys in Choice Modelling to Support Engineering Design”, Journal of Engineering Design, Vol. 22, No. 10, 2011.
  27. Hoyle, C., Chen, W., and Wang, N., “Understanding and Modeling Heterogeneity of Human Preferences for Engineering Design”, Journal of Engineering Design, Vol. 22, No.8, 2011.
  28. Hoyle, C., Chen, W., Wang, N., Koppelman, F., “Integrated Bayesian Hierarchical Choice Modeling Approach to Capture Heterogeneous Consumer Preferences in Engineering Design”, Journal of Mechanical Design, Vol. 132, No. 12, 2010.
  29. Hoyle, C., Chen, W., Ankenman, B., Wang, N., “Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design”, Journal of Mechanical Design, Vol. 131, No. 7, 2009.
  30. Hoyle, C., Tumer, I., Mehr, A., Chen, W., “Health Management Allocation during Conceptual System Design”, Journal of Computing & Information Science in Engineering, Vol. 9, No. 2, 2009.
  31. Hoyle, C. and Chen, W., “Product Attribute Function Deployment (PAFD) for Decision–Based Conceptual Design”, IEEE Transactions on Engineering Management, Vol. 56, No. 2, 2009.
  32. Kumar, D., Hoyle, C., Chen, W., Wang, N., Gomez-Levi, G., Koppelman, F., “A Hierarchical Choice Modeling Approach for Incorporating Customer Preferences and Market Trends in Engineering Design”, International Journal of Product Development, Vol. 8, No. 3, 2009.
  33. Ramani, K. and Hoyle, C., “Processing of Thermoplastic Composites Using a Powder Slurry Technique. I. Impregnation and Preheating,” Materials and Manufacturing Processes, Vol. 10, No. 6, pp. 1169-1182, 1995.
  34. Ramani, K. and Hoyle, C., “Processing of Thermoplastic Composites Using a Powder Slurry Technique. II. Coating and Consolidation,” Materials and Manufacturing Processes, Vol. 10, No. 6, pp. 1183-1200, 1995.
  35. Ramani, K., Borgoankar, H., Hoyle, C., “Experiments on Compression Molding and Pultrusion of Thermoplastic Powder Impregnated Towpregs,” Composites Manufacturing, Vol. 6, No. 1, pp. 35-43, 1995.

View all publications at:
      Google Scholar         ResearchGate

Research Interests
  • Robust system design
  • Uncertainty modeling
  • Optimization of large scale systems
  • Consumer choice modeling

Application Areas
  • Electrical power grid
  • Military vehicle design
  • Sustainable building design
  • Automotive engineering

Teaching
  • Undergraduate Courses:
  • Graduate Courses:
    • ME 517: Optimization in Design (co-teach with Matt Campbell)
    • ME 615: Design Under Uncertainty
    • ME 617: Design Automation (co-teach with Matt Campbell)

Book

Decision-Based Design: Integrating Consumer Preferences into Engineering Design, Wei Chen, Chris Hoyle, Henk Jan Wassenaar, Springer 2013.

  • Available at:

    Amazon.com      Springer

PhD Dissertation

 Configuring Engineering Systems Considering Consumer Heterogeneity