Advances and New Orientations in Goal Programming

  • Dylan JonesEmail author
  • Carlos Romero
Part of the Multiple Criteria Decision Making book series (MCDM)


This chapter starts by providing a categorization of current goal programming literature by type of variant used. Subsequently, goal programming is presented as a secondary model of a general p-metric distance function primary model. This orientation allows us to link goal programming with several fields like the determination of social choice functions or the interpretation and implementation of the Simonian concepts of bounded rationality and “satisficing”. To undertake the latter task, this epistemic framework is understood as a Laudian “Research Tradition” instead of the usual understanding as a scientific theory. Finally, potential future developments to expand the use and flexibility of goal programming as well as to explore possible logical connections of goal programming with other decision-making areas are highlighted.


Goal programming p-metrics Bounded rationality Research traditions 



The authors would like to thanks Drs. Diaz-Balteiro, González-Pachón and Tamiz and Aouni, for their intelligent reflections and continuous discussions on goal programming over many years.


  1. André, F. J., Cardenete, M. A., & Romero, C. (2010). Designing public policies-an approach based on multi-criteria analysis and computable general equilibrium modeling. Heidelberg: Springer.Google Scholar
  2. Audet, C., Carrizosa, E., & Hansen, P. (2004). An exact method for fractional goal programming. Journal of Global Optimization, 29, 113–120.CrossRefGoogle Scholar
  3. Bankian-Tabrizi, B., Shahanaghi, K., & Jabalameli, M. S. (2012). Fuzzy multi-choice goal programming. Applied Mathematical Modelling, 36, 1415–1420.CrossRefGoogle Scholar
  4. Bentham, J. (1948). An introduction to the principles of morals and legislation. Oxford: Blackwell (original work published in 1789).Google Scholar
  5. Chang, C. T. (2007). Multi-choice goal programming. Omega-International Journal of Management Science, 35, 389–396.CrossRefGoogle Scholar
  6. Chang, C. T. (2008). Revised multi-choice goal programming. Applied Mathematical Modelling, 32, 2587–2595.CrossRefGoogle Scholar
  7. Charnes, A., & Collomb, B. (1972). Optimal economic stabilization policy: Linear goal-interval programming models. Socio-Economic Planning Sciences, 6, 431–435.CrossRefGoogle Scholar
  8. Charnes, A., & Cooper, W. W. (1961). Management models and industrial applications of linear programming. New York: Wiley.Google Scholar
  9. Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimization-Part I. European Journal of Operational Research, 1, 39–54.CrossRefGoogle Scholar
  10. Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1, 138–151.Google Scholar
  11. Charnes, A., Cooper, W. W., Harrald, J., Karwan, K., & Wallace, W. (1976). A goal interval programming model for resource allocation in a marine environmental protection problem. Journal of Environmental Economics and Management, 3, 347–362.CrossRefGoogle Scholar
  12. Choobineh, M., & Mohagheghi, S. (2016). A multi-objective optimization framework for energy and asset management in an industrial microgrid. Journal of Cleaner Production, 139, 1326–1338.CrossRefGoogle Scholar
  13. Deaton, A., & Muellbauer, J. (1986). Economics and consumer behavior. Cambridge: Cambridge University Press.Google Scholar
  14. Debreu, G. (1959). Theory of value-an axiomatic analysis of economic equilibrium. New York: Wiley.Google Scholar
  15. Flavell, R. B. (1976). A new goal programming formulation. Omega-International Journal of Management Science, 4, 731–732.CrossRefGoogle Scholar
  16. Gigerenzer, G. (2001). The adaptive toolbox. In G. Gigerenzer & R. Selten (Eds.), Bounded rationality-the adaptive toolbox (pp. 37–50). Cambridge, Massachusetts: The MIT Press.Google Scholar
  17. Gigerenzer, G., & Selten, R. (Eds.). (2001). Bounded rationality-the adaptive toolbox. Cambridge, Massachusetts: The MIT Press.Google Scholar
  18. González-Pachón, J., & Romero, C. (1999). Distance-based consensus methods: A goal programming approach. Omega-International Journal of Management Science, 27, 341–347.CrossRefGoogle Scholar
  19. González-Pachón, J., & Romero, C. (2004). Satisficing logic and goal programming: Towards an axiomatic link. INFOR-Canadian Journal of Operational Research, 42, 157–161.CrossRefGoogle Scholar
  20. González-Pachón, J., & Romero, C. (2006). An analytical framework for aggregating mutiattribute utiliy functions. Journal of the Operational Research Society, 57, 1241–1247.Google Scholar
  21. González-Pachón, J., & Romero, C. (2007). Inferring consensus weights from pairwise comparison matrices. Annals of Operations Research, 154, 123–132.CrossRefGoogle Scholar
  22. González-Pachón, J., & Romero, C. (2009). Aggregation of ordinal and cardinal preferences: A framework based on distance functions. Journal of Multi-Criteria Decision Analysis, 15, 79–85.CrossRefGoogle Scholar
  23. González-Pachón, J., & Romero, C. (2011). The design of socially optimal decisions in a consensus scenario. Omega-International Journal of Management Science, 39, 179–185.CrossRefGoogle Scholar
  24. González-Pachón, J., & Romero, C. (2016). Bentham, marx and rawls ethical principles: In search for a compromise. Omega-International Journal of Management Science, 62, 47–51.CrossRefGoogle Scholar
  25. Hannan, E. L. (1981). On fuzzy goal programming. Decision Sciences, 12, 522–531.CrossRefGoogle Scholar
  26. Ignizio, J. P. (1976). Goal programming and extensions. Massachusetts: Lexington Books.Google Scholar
  27. Ignizio, J. P., & Perlis, J. H. (1979). Sequential linear goal programming. Computers & Operations Research, 6, 141–145.CrossRefGoogle Scholar
  28. Jones, D. F., Ouelhadj, D., & Glampedakis, A. (2017). Incorporation of poverty principles into goal programming. In: Paper Presented at the XII International Conference on Multiobjective and Goal Programming (MOPGP17), Metz, France (2017).Google Scholar
  29. Jones, D. F., & Tamiz, M. (1995). Expanding the flexibility of goal programming via preference modeling techniques. Omega-International Journal of Management Science, 23, 41–48.CrossRefGoogle Scholar
  30. Jones, D. F., & Tamiz, M. (2010). Practical goal programming. New York: Springer.CrossRefGoogle Scholar
  31. Laudan, L. (1977). Progress and its problems-towards a theory of scientific growth. Berkeley: University of California Press.Google Scholar
  32. Lee, S. M. (1972). Goal programming for decision analysis. Philadelphia: Auerbach.Google Scholar
  33. Masri, H. (2017). A multiple stochastic goal programming approach for the agent portfolio selection problem. Annals of Operations Research, 251, 179–192.CrossRefGoogle Scholar
  34. Nagel, E. (1961). The structure of science-problems in the logic of scientific explanation. London: Routledge & Kegan Paul.Google Scholar
  35. Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11, 325–336.CrossRefGoogle Scholar
  36. Rawls, J. (1971). A theory of justice. Oxford: Oxford University Press.Google Scholar
  37. Rodríguez-Uría, M. V., Caballero, R., Ruiz, F., & Romero, C. (2002). Meta-goal programming. European Journal of Operational Research, 136, 422–429.CrossRefGoogle Scholar
  38. Romero, C. (1991). Handbook of critical issues in goal programming. Oxford: Pergamon Press.Google Scholar
  39. Romero, C. (2001). Extended lexicographic goal programming: A unifying approach. Omega-International Journal of Management Science, 29, 63–71.CrossRefGoogle Scholar
  40. Romero, C. (2004). A general structure of achievement function for a goal programming model. European Journal of Operational Research, 153, 675–686.CrossRefGoogle Scholar
  41. Romero, C., Tamiz, M., & Jones, D. F. (1998). Goal programming, compromise programming and reference point method formulations: Linkages and utility interpretations. Journal of the Operational Research Society, 49, 986–991.CrossRefGoogle Scholar
  42. Rubinstein, A. (1998). Modeling bounded rationality. Cambridge, Massachusetts: The MIT Press.Google Scholar
  43. Sargent, T. (1993). Bounded rationality in macroeconomics. Oxford: Clarendon.Google Scholar
  44. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99–118.CrossRefGoogle Scholar
  45. Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63, 129–138.CrossRefGoogle Scholar
  46. Simon, H. A. (1979). Rational decision making in business organizations. American Economic Review, 69, 99–118.Google Scholar
  47. Tamiz, M., Jones, D. F., & Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of Operational Research, 111, 569–581.CrossRefGoogle Scholar
  48. Tamiz, M., Mirrazavi, S. K., & Jones, D. F. (1999). Extensions of Pareto efficiency analysis to integer goal programming. Omega-International Journal of Management Science, 27, 179–188.CrossRefGoogle Scholar
  49. Yu, P. L. (1973). A class of solutions for group decision problems. Management Science, 19, 936–946.CrossRefGoogle Scholar
  50. Yu, P. L. (1985). Multiple criteria decision making: Concepts, techniques and extensions. New York: Plenum Press.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of MathematicsCentre of Operational Research and Logistics (CORL), University of PortsmouthPortsmouthUK
  2. 2.Group of Economics for a Sustainable Environment (ECSEN)Technical University of MadridMadridSpain

Personalised recommendations