Skip to main content
Log in

Goal programming using multiple objective hybrid metaheuristic algorithm

  • Theoretical Paper
  • Published:
Journal of the Operational Research Society

Abstract

In this paper, a Goal Programming (GP) model is converted into a multi-objective optimization problem (MOO) of minimizing deviations from fixed goals. To solve the resulting MOO problem, a hybrid metaheuristic with two steps is proposed to find the Pareto set's solutions. First, a Record-to-Record Travel with an adaptive memory is used to find first non-dominated Pareto frontier solutions preemptively. Second, a Variable Neighbour Search technique with three transformation types is used to intensify every non dominated solution found in the first Pareto frontier to produce the final Pareto frontier solutions. The efficiency of the proposed approach is demonstrated by solving two nonlinear GP test problems and three engineering design problems. In all problems, multiple solutions to the GP problem are found in one single simulation run. The results prove that the proposed algorithm is robust, fast and simply structured, and manages to find high-quality solutions in short computational times by efficiently alternating search diversification and intensification using very few user-defined parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13

Similar content being viewed by others

References

  • Baykasoğlu A (2001). Goal programming using multiple objective tabu search . J Opl Res Soc 52: 1359–1369.

    Article  Google Scholar 

  • Baykasoğlu A, Owen S and Gindy N (1999). Solution of goal programming models using a basic taboo search algorithm . J Opl Res Soc 50: 960–973.

    Article  Google Scholar 

  • Charnes A, Cooper W and Ferguson R (1955). Optimal estimation of executive compensation by linear programming . Mngt Sci 1: 138–151.

    Article  Google Scholar 

  • Clayton E, Weber W and Taylor III B (1982). A goal programming approach to the optimization of multiresponse simulation models . IIE Trans 14: 282–287.

    Google Scholar 

  • Coello CAC and Christiansen AD (1999). MOSES: A multiple objective optimization tool for engineering design . Eng Optmiz 31: 337–368.

    Article  Google Scholar 

  • Coello CCA (1996). An empirical study of evolutionary techniques for multiobjective optimization in engineering design. PhD thesis, Department of Computer Science, Tulane University, New Orleans, LA..

  • Deb K (2001). Non-linear goal programming using multi-objective genetic algorithms . J Opl Res Soc 52: 291–302.

    Article  Google Scholar 

  • Dhingra AK and Lee BH (1994). A genetic algorithm approach to single and multiobjective structural optimization with discrete-continuous variables . Int J Num Meth Eng 37: 4059–4080.

    Article  Google Scholar 

  • Dueck G (1993). New optimization heuristics: The great deluge algorithm and the record-to-record travel . J Comput Phy 104: 86–92.

    Article  Google Scholar 

  • Dyer JS (1972). Interactive goal programming . Mngt Sci 19: 62–70.

    Article  Google Scholar 

  • Eschenauer H, Koski J and Osyczka A (1990). Multicriteria Design Optimization . Springer-Verlag: Berlin.

    Book  Google Scholar 

  • Hansen P and Mladenović N (2001). Variable neighbourhood search: Principles and applications . Eur J Opl Res 130: 449–467.

    Article  Google Scholar 

  • Ignizio JP (1978). A review of goal programming: A tool for multiobjective analysis . J Opl Res Soc 29: 1109–1119.

    Article  Google Scholar 

  • Ignizio JP (1982). Linear Programming in Single & Multiple Objective Systems . Prentice Hall: USA.

    Google Scholar 

  • Ignizio JP and Cavalier TM (1994). Linear Programming . Prentice Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Ijiri Y (1972). Management Goals and Accounting for Control . North Holland: Amsterdam.

    Google Scholar 

  • Lee SM (1972). Goal Programming for Decision Analysis . Auerbach: Philadelphia.

    Google Scholar 

  • Osyczka A (1985). Design Optimization . In: Gero JS (ed). Multicriteria Optimization for Engineering Design. Academic Press: New York, pp. 193–227.

    Google Scholar 

  • Romero C (1991). Handbook of Critical Issues in Goal Programming. Pergamon Press: Oxford.

    Google Scholar 

  • Tamiz M and Jones DF (1997). Interactive frameworks for investigation of goal programming models: Theory and practice . J Multi-Crit Decis Anal 6: 52–60.

    Article  Google Scholar 

  • Tamiz M, Jones DF and El-Darzi E (1995). A review of goal programming and its applications . Ann Opns Res 58: 39–53.

    Article  Google Scholar 

  • Zheng DW, Gen M and Ida K (1996). Evolution program for nonlinear goal programming . Comput Ind Eng 31: 907–911.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to S Dhouib or A Kharrat.

Appendix: Part of manual simulation for the first test problem

Appendix: Part of manual simulation for the first test problem

1- Diversification phase: max_det=0.1 and step c x1,x2=(0.2,3)

2- Intensification phase (VNS): m max=3 and step c x1,x2=(0.2,3)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dhouib, S., Kharrat, A. & Chabchoub, H. Goal programming using multiple objective hybrid metaheuristic algorithm. J Oper Res Soc 62, 677–689 (2011). https://doi.org/10.1057/jors.2009.181

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2009.181

Keywords

Navigation