The Linear Multiobjective Project Selection Problem

  • Samuel B. Graves
  • Jeffrey L. Ringuest
  • Andrés L. Medaglia
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 58)

Abstract

This chapter introduces multiobjective mathematical programming concepts in the context of the project selection problem. We compare goal programming and multiobjective mathematical programming and show the advantages of the latter. We illustrate by applying goal programming, multigoal programming and multiobjective programming to the same example problem. We show that the multiobjective formulation yields multiple nondominated solutions to the same problem for which the goal programming formulation reveals only a single solution. The multiobjective model is recommended as a more general approach to the project selection problem, since it is guaranteed to develop the set of all nondominated solutions.

Keywords

Market Share Cash Flow Goal Programming Aspiration Level Nondominated Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Asher, D. I. (1962). A linear programming model of the allocation of R&D effort. IRE Transactions on. Engineering. Management, 9, 154–157.CrossRefGoogle Scholar
  2. Baker, N. R., & Pound, W. H. (1964). R&D project selection: Where we stand. IEEE Transactions on Engineering Management, 11, 124–134.Google Scholar
  3. Baker, N. R. (1974). R&D project selection models: An assessment. IEEE Transactions on Engineering Management, 21 (4), 165–171.Google Scholar
  4. Beged-Dov, A. G. (1965). Optimal assignment of R&D projects in a large company using an integer programming model. IEEE Transactions on Engineering Management, 12, 138–142.Google Scholar
  5. Booker, J. M., & Bryson, M. C. (1985). Decision analysis in project management: An overview. IEEE Transactions on Engineering Management, 32, (1) 3–9.Google Scholar
  6. Changkong, V., & Haimes, Y. Y. (1983). Multiobjective decision making: theory and methodology. New York: Elsevier Science Publishing.Google Scholar
  7. Cohon, J. L. & Marks, D. H. (1975). A review and evaluation of multiobjective programming techniques. Water Resources Research, 11(2). 208–220.CrossRefGoogle Scholar
  8. Czajkowski, A. F., & Jones, S. (1986). Selecting interrelated R&D projects in space technology planning. IEEE Transactions on Engineering Management, 33, (1), 17–24.Google Scholar
  9. Evans, J. P., & Steuer, R. E. (1973). A revised simplex method for linear multiple objective problems. Mathematical Programming, 5(1), 54–72.CrossRefGoogle Scholar
  10. Freeman, R. J. (1960). A stochastic model for determining the size and allocation of the research budget, IRE Transactions on Enginering Management, 7, 2–7.CrossRefGoogle Scholar
  11. Graves, S. B. & Ringuest, J. L. (1992). Choosing the best solutiuon in an R&D project selection problem with multiple objectives. Journal of High Technology Management Research, 3(2), 213–224.CrossRefGoogle Scholar
  12. Graves, S. B., Ringuest, J.L., & Bard, J.F. (1992). Recent development in screening methods for nondominated solutions in multiobjective optimization, Computers and Operations Research, 19(7), 683–694.CrossRefGoogle Scholar
  13. Graves, S. B., Ringuest, J. L. & Case, R. H. (2000). Formulating optimal R&D portfolios. Research-technology Management, 43(3), 47–51.Google Scholar
  14. Hannan, E. L. (1985). An assessment of some criticisms of goal programming. Computers and Operations Research, 12 (6), 525–541.CrossRefGoogle Scholar
  15. Harrald, J. Leotta, J. Wallace, W. A. & Wendell, R. E. (1978). A note on the limitations of goal programming as observed in resource allocation for marine environmental protection. Naval Research. Logistics Quarterly, 25 (4), 733–739.CrossRefGoogle Scholar
  16. Heidenberger, K. & Stummer, C. (1999). Research and development project selection and resource allocation: A review of quantitative modeling approaches. International Journal of Management Reviews, 1, 197–224.CrossRefGoogle Scholar
  17. Keown, A. J., Taylor, B. W., & Duncan, C. P. (1979). Allocation of research and development funds: A zero-one goal programming approach. Omega, 7(4), 345–351.CrossRefGoogle Scholar
  18. Khorramshahgol, R., & Gousty, Y. (1986). Delphic goal programming (DGP): A multiobjective cost/benefit approach to R&D portfolio analysis. IEEE Transactions on Engineering Management, 33(3), 172–175.Google Scholar
  19. Martino, J. P. (1995). Research and development project selection. New York: Wiley.Google Scholar
  20. Mehrez, A., Mossery, S., & Sinuany-Stern, Z. (1982). Project selection in a small university R&D laboratory, R&D Management, 12(4), 169–174.CrossRefGoogle Scholar
  21. Ringuest, J. L. (1992). Multiobjective optimization: Behavioral and computational considerations. Boston: Kluwer.CrossRefGoogle Scholar
  22. Ringuest, J. L. & Graves, S. B. (1989). The linear multi-objective R&D project selection problem, IEEE Transactions on Engineering Management, 36(1), 54–57.CrossRefGoogle Scholar
  23. Ringuest, J. L. & Graves S. B. (1990). The linear multi-objective R&D project selection problem: An alternative to net present value, IEEE Transactions on Engineering Management, 37(2), 143–146.CrossRefGoogle Scholar
  24. Santhanam, R. & Kyparisis, G. J. (1996). Decision models for interdependent information system project selection. European Journal of Operational Research, 89, 380–399.CrossRefGoogle Scholar
  25. Souder, W. E. (1972). Comparative analysis of R&D investment models, AIIE Transactions, 4(1), 57–64.CrossRefGoogle Scholar
  26. Steuer, R. E., & Harris, F. W. (1980). Intra-set point generation and filtering in decision and criterion space. Computers and Operations Research, 7(1–2), 41–53.CrossRefGoogle Scholar
  27. Steuer, R. E. (1983). Operating manual for the ADBASE multiple objective linear programming computer package release: 2/83, College Bus. Admin., Univ. of Georgia, Athens.Google Scholar
  28. Weber, R., Werners, B. & Zimmerman H. J., (1990). Planning models for research and development. European Journal of Operational Research, 48, 175–188.CrossRefGoogle Scholar
  29. Winkofsky, E. P., Baker, N. R., & Sweeney, D. J. (1981). A decision process model of R&D resource allocation in hierarchical organizations. Management Science, 27(3), 268–283.CrossRefGoogle Scholar
  30. Zeleny, M. & Cochrane, J. L. (1973). A priori and a posteriori goals in macroeconomic policy making,” in L. Cochrane and M. Zeleny, (Eds.) Multiple Criteria Decision Making, (pp. 373–391) Columbia, SC: University of S. Carolina Press.Google Scholar
  31. Zeleny, M. (1981). The pros and cons of goal programming, Computers and Operations Research 8(4), 357–359.CrossRefGoogle Scholar
  32. Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Samuel B. Graves
    • 1
  • Jeffrey L. Ringuest
    • 1
  • Andrés L. Medaglia
  1. 1.Boston CollegeUSA

Personalised recommendations