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Research in Engineering Design

, Volume 27, Issue 4, pp 347–366 | Cite as

Optimizing time–cost trade-offs in product development projects with a multi-objective evolutionary algorithm

  • Christoph Meier
  • Ali A. YassineEmail author
  • Tyson R. Browning
  • Ulrich Walter
Original Paper

Abstract

Time–cost trade-offs arise when organizations seek the fastest product development (PD) process subject to a predefined budget, or the lowest-cost PD process within a given project deadline. Most of the engineering and project management literature has addressed this trade-off problem solely in terms of crashing—options to trade cost for time at the individual activity level—and using acyclical networks. Previously (Meier et al. in IEEE Trans Eng Manag 62(2):237–255, 2015), we presented a rich model of the iterative (cyclical) PD process that accounts for crashing, overlapping, and stochastic activity durations and iterations. In this paper, we (1) propose an optimization strategy for the model based on a multi-objective evolutionary algorithm, called ε-MOEA, which identifies the Pareto set of best time–cost trade-off solutions, and (2) demonstrate the approach using an automotive case study. We find that, in addition to crashing, activity overlapping, process architecture, and work policy provide further managerial levers for addressing the time–cost trade-off problem. In particular, managerial work policies guide process cost and duration into particular subsets of the Pareto-optimal solutions. No work policy appeared to be superior to the others in both the cost and duration dimensions; instead, a time–cost trade-off arises due to the choice of work policy. We conclude that it is essential for managers to consider all of the key factors in combination when planning and executing PD projects.

Keywords

Time–cost trade-off Product development Project management Iteration Crashing Overlapping Work policy Optimization Genetic algorithm 

Notes

Acknowledgment

The first author would like to thank the Bavarian Science Foundation. The second author is grateful for support from the University Research Board (URB) program at the American University of Beirut. The third author is grateful for support from the Neeley Summer Research Award Program from the Neeley School of Business at TCU.

Supplementary material

163_2016_222_MOESM1_ESM.docx (140 kb)
Supplementary material 1 (DOCX 140 kb)

References

  1. Abdelsalam HME, Bao HP (2007) Re-sequencing of design processes with activity stochastic time and cost: an optimization-simulation approach. J Mech Des 129(2):150–157CrossRefGoogle Scholar
  2. Adler P, Mandelbaum A, Nguyen V, Schwerer E (1995) From project to process management: an empirically-based framework for analyzing product development time. Manag Sci 41(3):458–484CrossRefzbMATHGoogle Scholar
  3. Baldwin AN, Austin S, Hassan TM, Thorpe A (1999) Modelling information flow during the conceptual and schematic stages of building design. Constr Manag Econ 17:155–167CrossRefGoogle Scholar
  4. Bean JC (1994) Genetic algorithms and random keys for sequencing and optimization. J Comput 6(2):154–160zbMATHGoogle Scholar
  5. Berthaut F, Pellerin R, Perrier N, Hajji A (2014) Time-cost trade-offs in resource-constraint project scheduling problems with overlapping modes. Int J Proj Organ Manag 6(3):215–236Google Scholar
  6. Browning TR (2001) Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Trans Eng Manag 48(3):292–306CrossRefGoogle Scholar
  7. Browning TR, Eppinger SD (2002) Modeling impacts of process architecture on cost and schedule risk in product development. IEEE Trans Eng Manage 49(4):428–442CrossRefGoogle Scholar
  8. Browning TR, Ramasesh RV (2007) A survey of activity network-based process models for managing product development projects. Prod Oper Manag 16(2):217–240CrossRefGoogle Scholar
  9. Browning TR, Yassine AA (2016) Managing a portfolio of product development projects under resource constraints. Decis Sci (forthcoming)Google Scholar
  10. Brucker P, Drexl A, Mohring R, Neumann K, Pesch E (1999) Resource-constrained project scheduling: notation, classification, models, and methods. Eur J Oper Res 112:3–41CrossRefzbMATHGoogle Scholar
  11. Bruni ME, Beraldi P, Guerriero F (2015) The stochastic resource-constrained project scheduling problem. In: Schwindt C, Zimmermann J (eds) Handbook on project management and scheduling, vol 2. Springer, Berlin, pp 811–835Google Scholar
  12. Cho S-H, Eppinger SD (2005) A simulation-based process model for managing complex design projects. IEEE Trans Eng Manage 52(3):316–328CrossRefGoogle Scholar
  13. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. Evol Comput IEEE Trans 8(3):256-279CrossRefGoogle Scholar
  14. Coello CAC, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, vol 242. Kluwer Academic, New YorkGoogle Scholar
  15. Cohen I, Golany B, Shtub A (2007) The stochastic time–cost tradeoff problem: a robust optimization approach. Networks 49(2):175–188MathSciNetCrossRefzbMATHGoogle Scholar
  16. Cooper KG (1993) The rework cycle: benchmarks for the project manager. Proj Manag J 24(1):17–21Google Scholar
  17. Coverstone-Carroll V, Hartmann JW, Mason WJ (2000) Optimal multi-objective low-thrust spacecraft trajectories. Comput Methods Appl Mech Eng 186(2–4):387–402CrossRefzbMATHGoogle Scholar
  18. De P, Dunne EJ, Ghosh JB, Wells CE (1995) The discrete time-cost trade off problem revisited. Eur J Oper Res 81:225–238CrossRefzbMATHGoogle Scholar
  19. Deb K (2009) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterzbMATHGoogle Scholar
  20. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  21. Deb K, Mohan M, Mishra S (2003) Towards a quick computation of well-spread pareto-optimal solutions. In: Evolutionary multi-criterion optimization. Second international conference, EMO 2003, pp 222–236Google Scholar
  22. Deb K, Mohan M, Mishra S (2005) Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions. Evol Comput 13(4):501–525CrossRefGoogle Scholar
  23. Deckro RF, Hebert JE, Verdini WA, Grimsrud PH, Venkateshwar S (1995) Nonlinear time/cost tradeoff models in project management. Comput Ind Eng 28(2):219–229CrossRefGoogle Scholar
  24. Doerner KF, Gutjahr WJ, Hartl RF, Strauss C, Stummer C (2008) Nature-inspired metaheuristics for multiobjective activity crashing. Omega 36(6):1019–1037CrossRefGoogle Scholar
  25. Eppinger SD, Browning TR (2012) Design structure matrix methods and applications. MIT Press, CambridgeGoogle Scholar
  26. Fonseca CM, Fleming PJ (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms-part1: a unified formulation. IEEE Trans Syst Man Cybernet Part A Syst Hum 28(1):26–37CrossRefGoogle Scholar
  27. Fujita K, Hirokawa N, Akagi S, Kitamura S, Yokohata H (1998) Multi-objective optimal design of automotive engine using genetic algorithms. In: Proceedings of 1998 ASME design engineering technical conferencesGoogle Scholar
  28. Gerk JEV, Qassim RY (2008) Project acceleration via activity crashing, overlapping, and substitution. IEEE Trans Eng Manag 55(4):590–601CrossRefGoogle Scholar
  29. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New YorkzbMATHGoogle Scholar
  30. Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of genetic algorithms, vol 1, pp 69–93Google Scholar
  31. Goldberg DE, Deb K, Thierens D (1991) Toward a better understanding of mixing in genetic algorithms. In: Proceedings of the 4th international conference on genetic algorithmsGoogle Scholar
  32. Hanne T (1999) On the convergence of multiobjective evolutionary algorithms. Eur J Oper Res 117(3):553–564CrossRefzbMATHGoogle Scholar
  33. Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207(1):1–14MathSciNetCrossRefzbMATHGoogle Scholar
  34. Hazır Ö, Erel E, Günalay Y (2011) Robust optimization models for the discrete time/cost trade-off problem. Int J Prod Econ 130(1):87–95CrossRefGoogle Scholar
  35. Hazır Ö, Haouari M, Erel E (2015) Robust optimization for the discrete time-cost tradeoff problem with cost uncertainty. In: Schwindt C, Zimmermann J (eds) Handbook on project management and scheduling, vol 2. Springer, Berlin, pp 865–874Google Scholar
  36. Helbig S, Pateva D (1994) On several concepts for ε-efficiency. OR Spektrum 16(3):179–186MathSciNetCrossRefzbMATHGoogle Scholar
  37. Herroelen W, Leus R (2005) Project scheduling under uncertainty: survey and research potentials. Eur J Oper Res 165:289–306CrossRefzbMATHGoogle Scholar
  38. Huang E, Chen S-JG (2006) Estimation of project completion time and factors analysis for concurrent engineering project management: a simulation approach. Concurr Eng 14(4):329–341CrossRefGoogle Scholar
  39. Karniel A, Reich Y (2009) From DSM based planning to design process simulation: a review of process scheme verification issues. IEEE Trans Eng Manag 56(4):636–649CrossRefGoogle Scholar
  40. Kline SJ (1985) Innovation is not a linear process. Res Manag 28(2):36–45Google Scholar
  41. Knjazew D (2002) OmeGA: a competent genetic algorithm for solving permutation and scheduling problems. Kluwer Academic Publishers Group, NorwellCrossRefzbMATHGoogle Scholar
  42. Krishnan V, Ulrich KT (2001) Product development decisions: a review of the literature. Manag Sci 47(1):1–21CrossRefGoogle Scholar
  43. Krishnan V, Eppinger SD, Whitney DE (1997) A model-based framework to overlap product development activities. Manag Sci 43(4):437–451CrossRefzbMATHGoogle Scholar
  44. Kumar R, Rockett P (2002) Improved sampling of the pareto-front in multiobjective genetic optimizations by steady-state evolution: a pareto converging genetic algorithm. Evol Comput 10(3):283–314CrossRefGoogle Scholar
  45. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evol Comput 10(3):263–282CrossRefGoogle Scholar
  46. Lévárdy V, Browning TR (2009) An adaptive process model to support product development project management. IEEE Trans Eng Manag 56(4):600–620CrossRefGoogle Scholar
  47. Liberatore MJ, Pollack-Johnson B (2009). Quality, time, and cost tradeoffs in project management decision making. In: Portland international conference on management of engineering & technology, 2009. PICMET 2009, pp 1323–1329Google Scholar
  48. Meier C (2011) Time-cost tradeoffs in product development processes, Doktor-Ingenieurs (Dr.-Ing.) thesis, Technische Universität München, Munich, GermanyGoogle Scholar
  49. Meier C, Yassine AA, Browning TR (2007) Design process sequencing with competent genetic algorithms. J Mech Des 129(6):566–585CrossRefGoogle Scholar
  50. Meier C, Browning TR, Yassine AA, Walter U (2015) The cost of speed: work policies for crashing and overlapping in product development projects. IEEE Trans Eng Manag 62(2):237–255CrossRefGoogle Scholar
  51. Nasr W, Yassine A, Abou Kasm O (2015) An analytical approach to estimate the expected duration and variance for iterative product development projects. Res Eng Des 27(1):55–71CrossRefGoogle Scholar
  52. Poloni C, Giurgevich A, Onesti L, Pediroda V (2000) Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for complex design problems in fluid dynamics. Comput Methods Appl Mech Eng 186(2–4):403–420CrossRefzbMATHGoogle Scholar
  53. Roemer TA, Ahmadi R (2004) Concurrent crashing and overlapping in product development. Oper Res 52(4):606–622CrossRefzbMATHGoogle Scholar
  54. Roemer TA, Ahmadi R, Wang RH (2000) Time-cost trade-offs in overlapped product development. Oper Res 48(6):858–865CrossRefGoogle Scholar
  55. Rudolph G, Agapie A (2000) Convergence properties of some multi-objective evolutionary algorithms. In: Congress on evolutionary computation (CEC 2000), pp 1010–1016Google Scholar
  56. Sargent RG (1999) Validation and verification of simulation models. In: Winter simulation conference, Phoenix, AZ, 5–8 DecGoogle Scholar
  57. Shaja AS, Sudhakar K (2010) Optimized sequencing of analysis components in multidisciplinary systems. Res Eng Des 21(3):173–187CrossRefGoogle Scholar
  58. Smith RP, Eppinger SD (1997) Identifying controlling features of engineering design iteration. Manag Sci 43(3):276–293CrossRefzbMATHGoogle Scholar
  59. Smith RP, Morrow JA (1999) Product development process modeling. Des Stud 20(3):237–261CrossRefGoogle Scholar
  60. Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248CrossRefGoogle Scholar
  61. Tavares VL, Ferreira JA, Coelho JS (2002) A comparative morphologic analysis of benchmark sets of project networks. Int J Project Manag 20(6):475–485CrossRefGoogle Scholar
  62. Vanhoucke M (2015) Generalized discrete time-cost tradeoff problems. In: Schwindt C, Zimmermann J (eds) Handbook on project management and scheduling, vol 1. Springer, Berlin, pp 639–658Google Scholar
  63. Wolpert DH, Macready WG (1997) No free lunch theorems for search. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar
  64. Yassine A, Braha D (2003) Complex concurrent engineering and the design structure matrix method. Concurr Eng Res Appl 11(3):165–176CrossRefGoogle Scholar
  65. Yassine A, Whitney D, Lavine J, Zambito T (2000) Do-it-right-first-time (DRFT) approach to DSM restructuring. In: ASME international design engineering technical conferences (Design theory & methodology conference), Baltimore, MD, 10–13 SeptGoogle Scholar
  66. Zambito T (2000) Using the design structure matrix to structure automotive hood system development. Master’s thesis, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  67. Zhuang M, Yassine AA (2004) Task scheduling of parallel development projects using genetic algorithms. In: ASME international design engineering technical conferences. (Design automation conference), Salt Lake City, Sept 28–Oct 2Google Scholar
  68. Zitzler E, Laumanns M, Thiele L (2002) SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary methods for design, optimisation, and control, Barcelona, Spain, pp 19–26Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Christoph Meier
    • 1
  • Ali A. Yassine
    • 2
    Email author
  • Tyson R. Browning
    • 3
  • Ulrich Walter
    • 1
  1. 1.Institute of AstronauticsTechnische Universität MünchenGarchingGermany
  2. 2.Department of Industrial Engineering and ManagementAmerican University of BeirutBeirutLebanon
  3. 3.Neeley School of BusinessTexas Christian UniversityFort WorthUSA

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