Research in Engineering Design

, Volume 29, Issue 4, pp 589–603 | Cite as

Incorporating quantitative reliability engineering measures into tradespace exploration

  • Saikath Bhattacharya
  • Vidhyashree Nagaraju
  • Eric Spero
  • Anindya Ghoshal
  • Lance FiondellaEmail author
Original Paper


Recently, tradespace analysis and exploration has emerged as an important focus area within the Department of Defense Engineered Resilient System initiative, which draws upon engineering concepts, science, and design tools to produce trusted and effective solutions for a wide range of operational contexts. Most of the previous research on tradespace analysis, including those developed for rotorcraft, emphasize performance. However, non-functional requirements such as reliability, availability, and maintainability have received minimal consideration, despite their direct influence on program level concerns such as operation and support as well as affordability. This paper proposes a strategy to incorporate reliability engineering into tradespace analysis. We also develop a subsystem-level reliability investment model that is illustrated through a simplified example. Our results suggest that reliability investment could achieve significant savings over a systems lifecycle, thereby enabling improved fleet availability and a larger fleet size.


Tradespace exploration Reliability Availability Fleet size System lifecycle Operation Support cost 



Army material systems analysis activity


Army Research Laboratory


Capability assessment and trade-off environment


Cost Assessment and Program Evaluation


Cost analysis and strategy assessment


Department of Defense


Essential Function Failure


Engineered Resilient System


Fully mission capable


Future vertical lift


Joint multi-role rotorcraft technology demonstrator


Multi-attribute tradespace exploration


Mean time between essential function failure


Mean time to repair


NASA design and analysis of rotorcraft


Not mission capable


S] Operation and Support


Reliability, availability, and maintainability


Systems Engineering Research Center


Test, Analyze, and Fix


Tradespace exploration


Value-driven design





Steady-state availability of subsystem i




Budget allocated to availability improvement


Cost of subsystem i replacements over system lifecycle


Cost of operating TAAF phase per unit time


Cost of n subsystems over system lifecycle

\(\texttt {CV}\)

Coefficient of variation of initial B-mode failures


Lambert Function


Total number of B-mode failures


Lifecycle duration


Mean time between essential function failures


Number of subsystem i replacements over system lifecycle


Reliability of subsystem i


Cost to replace subsystem i


Average fix effectiveness factor for all B-mode failure types, (\(d=\mu _{d}\)) for notational convenience

\(f_{\lambda }\)

Gamma distribution of initial B-mode failure rates


Unobserved B-mode failures


Number of subsystems


System failure rate at time T


Cost to achieve desired MTBEFF


Number of systems that can be supported

\(\lambda _{A}\)

Initial A-mode failure rate

\(\lambda _{B}\)

Initial B-mode failure rate

\(\mu _{b}\)

Average value of cost increment


Expected failure intensity



This research was supported by Cooperative Research and Development Agreement (ARL CRADA \(\#~14-30\)) for Multi Task Technology Transfer between the U.S. Army Research Laboratory and the University of Massachusetts Dartmouth, U.S. Army Research Laboratory through the National Institute of Aerospace (NIA) under Grant award number C15-2A00-UMASS, subaward activity number 2A69-UMASS, and a Summer Research Fellowship Program grant from the Office of the Provost at the University of Massachusetts Dartmouth, Dartmouth, Massachusetts, USA.


  1. Arruda J, Gavrilovski A, Ahn B, Chae HG, Spero E, Mavris DN (2014) The capability assessment and tradeoff environment (CATE) for advanced aerospace vehicle and technology assessment. Proc Comput Sci 28:583–590CrossRefGoogle Scholar
  2. Bagheri H, Tang C, Sullivan K (2014) Trademaker: automated dynamic analysis of synthesized tradespaces. In: ACM International Conference on Software Engineering, pp 106–116Google Scholar
  3. Benton AW, Crow LH (1989) Integrated reliability growth testing. In: Annual Reliability and Maintainability Symposium, pp 160–166Google Scholar
  4. Boehm B, Lane J, Koolmanojwong S (2013) An orthogonal framework for improving life cycle affordability. Proc Comput Sci 16:1170–1179CrossRefGoogle Scholar
  5. Buede D (2004) On trade studies. Int Counc Syst Eng Symp 14(1):2027–2034Google Scholar
  6. Collopy PD, Bloebaum CL, Mesmer BL (2012) The distinct and interrelated roles of value-driven design, multidisciplinary design optimization, and decision analysis. In: Proceedings of AIAA Aviation Technology, Integration, and Operations Conference,
  7. Crow LH (1984) Methods for assessing reliability growth potential. In: Annual Reliability and Maintainability Symposium, pp 484–489,
  8. Dagli CH, Singh A, Dauby JP, Wang R (2009) Smart systems architecting: computational intelligence applied to trade space exploration and system design. Syst Res Forum World Sci 3:101–119CrossRefGoogle Scholar
  9. DoD (2011) Department of defense reliability, availability, maintainability, and cost rationale Report Manual. Tech. rep., DTIC DocumentGoogle Scholar
  10. Dou K, Wang X, Tang C, Ross A, Sullivan K (2015) An evolutionary theory-systems approach to a science of the ilities. Proc Comput Sci 44:433–442CrossRefGoogle Scholar
  11. Ebeling CE (2004) An introduction to reliability and maintainability engineering. Tata McGraw-Hill Education, New YorkGoogle Scholar
  12. Edwards J, Gosling T, Burney T, Enser S, Keith K, Lackey J, Perdue C, Powelson R, Prybyla J, Spence J et al (2010) Defense management: DoD needs better information and guidance to more effectively manage and reduce operating and support costs of major weapon systems. Tech. rep., DTIC DocumentGoogle Scholar
  13. Ellner P, Hall B (2006) Planning model based on projection methodology (PM2). Tech. rep., DTIC Document, Accession Number : ADA448130Google Scholar
  14. Ellner PM, Hall JB (2004) AMSAA maturity projection model based on stein estimation. Tech. rep., DTIC DocumentGoogle Scholar
  15. Emmons DL, Lobbia M, Radcliffe T, Bitten RE (2010) Affordability assessments to support strategic planning and decisions at NASA. In: IEEE Aerospace Conference,, pp 1–13Google Scholar
  16. Fitch P, Cooper JS (2005) Life-cycle modeling for adaptive and variant design. part 1: methodology. Res Eng Des 15(4):216–228CrossRefGoogle Scholar
  17. Forbes JA, Long EA, Lee DA, Esmann WJ, Cross LC (2008) Developing a reliability investment model. LMI Report HPT80-T1Google Scholar
  18. Gertsbakh I (2000) Reliability theory: with applications to preventive maintenance. Springer Science & Business Media, BerlinzbMATHGoogle Scholar
  19. Department of Defense Guidebook US (2007) U. S. Department of Defense, Defense Acquisition Guidebook, 5000.01. URL
  20. Hofstetter WK, Crawley EF (2013) A methodology for portfolio-level analysis of system commonality. Res Eng Des 24(4):349–373CrossRefGoogle Scholar
  21. Department of Defense Instruction US (2015) Operation of the Defense, Acquisition System,DoD 5000.02. URL
  22. Johnson W (2010) NDARC-NASA design and analysis of rotorcraft theoretical basis and architecture. In: Proc. American Helicopter Society Aeromechanics Specialist’s Conference, San Francisco, CA, pp 1–26Google Scholar
  23. Long EA, Forbes J, Hees J, Stouffer V (2007) Empirical relationships between reliability investments and life-cycle support costs. LMI Report SA701T1Google Scholar
  24. Malone P, Nguyen T (2015) PBR: A method to meet life cycle affordability goals. In: Annual Reliability and Maintainability Symposium, pp 1–6Google Scholar
  25. McManus H, Richards M, Ross AM, Hastings DE (2007) A framework for incorporating ilities in tradespace studies. AIAA Space 1:941–954Google Scholar
  26. Miller SW, Simpson TW, Yukish MA, Stump G, Mesmer BL, Tibor EB, Bloebaum CL, Winer EH (2014) Toward a value-driven design approach for complex engineered systems using trade space exploration tools. In: ASME, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference,
  27. National Research Council (2015) Reliability growth: enhancing defense system reliability. The National Academies Press, WashingtonGoogle Scholar
  28. OT&E (2005) Memorandum of agreement on operational suitability terminology and definitions to be used in operational test and evaluation. Tech. rep., OT&EGoogle Scholar
  29. Parnell GS, Cilli MV, Buede D (2014) Tradeoff study cascading mistakes of omission and commission. International Council on Systems Engineering Symposium, Wiley Online Library 24:332–346Google Scholar
  30. Raheja DG, Gullo LJ (eds) (2012) Design for reliability. Wiley, HobokenGoogle Scholar
  31. Richards MG, Ross AM, Hastings DE, Rhodes DH (2009) Multi-attribute tradespace exploration for survivability. PhD thesis, Massachusetts Institute of Technology, Engineering Systems DivisionGoogle Scholar
  32. Richards MG, Ross AM, Shah NB, Hastings DE (2009b) Metrics for evaluating survivability in dynamic multi-attribute tradespace exploration. J Spacecr Rockets 46(5):1049–1064CrossRefGoogle Scholar
  33. Ross AM, Hastings DE (2005a) The tradespace exploration paradigm. Int Counc Syst Eng Symp 15:1706–1718Google Scholar
  34. Ross AM, Hastings DE (2005b) The tradespace exploration paradigm. Int Counc Syst Eng Symp 15:1706–1718Google Scholar
  35. Ross AM, Hastings DE, Warmkessel JM, Diller NP (2004) Multi-attribute tradespace exploration as front end for effective space system design. J Spacecr Rockets 41(1):20–28CrossRefGoogle Scholar
  36. Shabi J, Reich Y (2012) Developing an analytical model for planning systems verification, validation and testing processes. Adv Eng Inform 26(2):429–438CrossRefGoogle Scholar
  37. Shabi J, Reich Y, Diamant R (2017) Planning the verification, validation, and testing process: a case study demonstrating a decision support model. J Eng Des 28(3):171–204CrossRefGoogle Scholar
  38. Simpson T, Spencer D, Yukish M, Stump G (2008a) Visual steering commands and test problems to support research in trade space exploration. In: 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference,
  39. Simpson TW, Carlsen DE, Congdon CD, Stump G, Yukish MA (2008b) Trade space exploration of a wing design problem using visual steering and multi-dimensional data visualization. In: 4th AIAA Multidisciplinary Design Optimization Specialist Conference, pp 7–10Google Scholar
  40. Simpson TW, Miller S, Tibor EB, Yukish MA, Stump G, Kannan H, Mesmer B, Winer EH, Bloebaum CL (2017) Adding value to trade space exploration when designing complex engineered systems. Syst Eng 20(2):131–146CrossRefGoogle Scholar
  41. Singh A, Dagli CH (2009) Multi-objective stochastic heuristic methodology for tradespace exploration of a network centric system of systems. In: Annual IEEE Systems Conference, pp 218–223Google Scholar
  42. Sitterle VB, Curry MD, Freeman DF, Ender TR (2014) Integrated toolset and workflow for tradespace analytics in systems engineering. Int Counc Syst Eng Symp 24:347–361Google Scholar
  43. Sitterle VB, Freeman DF, Goerger SR, Ender TR (2015) Systems engineering resiliency: guiding tradespace exploration within an engineered resilient systems context. Proc Comput Sci 44:649–658CrossRefGoogle Scholar
  44. Spero E, Avera MP, Valdez PE, Goerger SR (2014a) Tradespace exploration for the engineering of resilient systems. Proc Comput Sci 28:591–600CrossRefGoogle Scholar
  45. Spero E, Bloebaum CL, German BJ, Pyster A, Ross AM (2014b) A research agenda for tradespace exploration and analysis of engineered resilient systems. Proc Comput Sci 28:763–772CrossRefGoogle Scholar
  46. Stevens Institute of Technology (2012) Tradespace & affordability program (TAP) workshop report v1.0, Washington, DC. Tech. repGoogle Scholar
  47. Stump G, Lego S, Yukish M, Simpson TW, Donndelinger JA (2009) Visual steering commands for trade space exploration: User-guided sampling with example. J Comput Inf Sci Eng 9(4):044,501CrossRefGoogle Scholar
  48. Stump GM, Yukish M, Simpson TW, Harris EN (2003) Design space visualization and its application to a design by shopping paradigm. Proc ASME Des Eng Tech Conf 2:795–804Google Scholar
  49. Vascik P, Ross AM, Rhodes DH (2015) A method for exploring program and portfolio affordability tradeoffs under uncertainty using epoch-era analysis: A case application to carrier strike group design. Tech. rep., DTIC Document, Accession Number : ADA623192Google Scholar
  50. Winer E, Bloebaum C (2001) Visual design steering for optimization solution improvement. Struct Multidiscip Optim 22(3):219–229CrossRefGoogle Scholar
  51. Winer E, Bloebaum C (2002a) Development of visual design steering as an aid in large-scale multidisciplinary design optimization. part i: method development. Struct Multidiscip Optim 23(6):412–424CrossRefGoogle Scholar
  52. Winer E, Bloebaum C (2002b) Development of visual design steering as an aid in large-scale multidisciplinary design optimization. part ii: method development. Struct Multidiscip Optim 23(6):425–435CrossRefGoogle Scholar
  53. Witus G, Bryzik W (2015) Progress toward a dod ground vehicle tradespace and affordability analysis framework. Proc Comput Sci 44:537–546CrossRefGoogle Scholar
  54. Wu M (2014) Design for affordability in defense and aerospace systems using tradespace-based methods. Master’s thesis, Massachusetts Institute of TechnologyGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Saikath Bhattacharya
    • 1
  • Vidhyashree Nagaraju
    • 1
  • Eric Spero
    • 2
  • Anindya Ghoshal
    • 2
  • Lance Fiondella
    • 1
    Email author
  1. 1.Electrical and Computer EngineeringUniversity of MassachusettsDartmouthUSA
  2. 2.U.S. Army Research LaboratoryAberdeen Proving GroundUSA

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