Skip to main content
SpringerLink
Log in

A Simulated Annealing Genetic Algorithm for the Electrical Power Districting Problem

Annals of Operations Research volume 121, pages 33–55 (2003)Cite this article

Abstract

Due to a variety of political, economic, and technological factors, many national electricity industries around the globe are transforming from non-competitive monopolies with centralized systems to decentralized operations with competitive business units. A key challenge faced by energy restructuring specialists at the World Bank is trying to simultaneously optimize the various criteria one can use to judge the fairness and commercial viability of a particular power districting plan. This research introduces and tests a new algorithm for solving the electrical power districting problem in the context of the Republic of Ghana and using a random test problem generator. We show that our mimetic algorithm, the Simulated Annealing Genetic Algorithm, outperforms a well-known Parallel Simulated Annealing heuristic on this new and interesting problem manifested by the deregulation of electricity markets.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

References

  1. M. Altman, Is automation the answer? – The computational complexity of automated redistricting, Rutgers J. Comput. Technol. Law 23(1) (1995) 81–142.

    Google Scholar 

  2. C. Backstrom, Problems of implementing redistricting, in: Representation and Redistricting Issues (1982).

  3. P.K. Bergey, C.T. Ragsdale and M. Hoskote, A decision support system for the electrical power districting problem, Decision Support Systems, to appear.

  4. M.H. Browdy, Simulated annealing: An improved computer model for political redistricting, Yale Law & Policy Rev. 8 (1990) 163.

    Google Scholar 

  5. K.A. De Jong, M.A. Potter and W.M. Spears, Using problem generators to explore the problems with epistasis, in: Proc. of the International Conference on Genetic Algorithms, ed. T. Baeck (1997) pp. 333–345.

  6. C. Easingwood, Heuristic approach to selecting sales regions and territories, Oper. Res. Quart. 24(4) (1973) 527–534.

    Google Scholar 

  7. E. El-Farzi and G. Mitra, Solution of set-covering and set-partitioning problems using assignment relaxations, J. Oper. Res. Soc. 43 (1992) 483.

    Google Scholar 

  8. C.M. Fonseca and P.J. Flemming, An overview of evolutionary algorithms in multi-objective optimization, Evolut. Comput. 3(1) (1995) 1–16.

    Google Scholar 

  9. R.S. Garfinkel and G.L. Nemhauser, Optimal political districting by implicit enumeration techniques, Management Sci. 16(8) (1970) B495–B508.

    Google Scholar 

  10. D.E. Goldberg, Genetic algorithms with sharing for multi-modal function optimization, in: Proceedings of the Second International Conference on Genetic Algorithms (Cambridge, MA, 1987) pp. 41–49.

  11. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, MA, 1989).

    Google Scholar 

  12. J. Hershberger, Finding tailored partitions, J. Algorithms 12 (1991) 431.

    Google Scholar 

  13. M.S. Heschel, Effective sales territory development, J. Marketing 41(2) (1977) 39–43.

    Google Scholar 

  14. S.W. Hess and S.A. Samuels, Experiences with a sales districting model: criteria and implementation, Management Sci. 18(4) (1971) 41–54.

    Google Scholar 

  15. S.W. Hess, J.B. Weaver, H.J. Siegreldt, J.N. Whelan and P.A. Zitlau, A nonpartisan political redistricting by computer, Oper. Res. 13 (1965) 998–1008.

    Google Scholar 

  16. J. H. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, MI, 1975), reissued (MIT Press, Cambridge, MA, 1975).

    Google Scholar 

  17. H. Ishibuchi and T. Murata, A multi-objective genetic local search algorithm and its application to flowshop scheduling, IEEE Trans. Systems Man Cybernet. – Part C: Appl. Rev. 28(3) (1998) 392–403.

    Google Scholar 

  18. S. Kirkpatrick, C.D. Gallatt and M.P. Becchi, Optimization by simulated annealing, Science 220 (1983) 671–680.

    Google Scholar 

  19. G. Kliewer and S. Tschoke, A general parallel simulated annealing library and its applications in industry, in: Proceedings of the First Meeting of the EURO-PAREOWorking Group on Parallel Processing in Operations Research (1998).

  20. J.R. Koza, Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems, Technical Report STAN-CS–90–1314, Computer Science Department, Stanford University (1990).

  21. F. Kursawe, A variant of evolution strategies for vector optimization, in: Parallel Problem Solving from Nature (Springer, Berlin, 1991) pp. 193–197, 496.

    Google Scholar 

  22. F.H. Lee and G.S. Stiles, Parallel Simulated Annealing: Several Approaches, Transputer Research and Applications (IOS Press, Amsterdam, 1989).

    Google Scholar 

  23. L.M. Lodish, Vaguely right approach to sales force allocations, Harvard Business Rev. (January– February, 1974) 119–124.

  24. A. Mehrotra, E.L. Johnson and G.L. Nemhauser, An optimization based heuristic for political districting, Management Sci. 44(8) (1998) 1100–1114.

    Google Scholar 

  25. N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth and A.H. Teller, Equations of state calculation by fast computing machines, J. Chem. Phys. 21(6) (1953) 1087–1092.

    Google Scholar 

  26. S.S. Nagel, Simplified bipartisan computer redistricting, Stanford Law Rev. 17 (1965) 863.

    Google Scholar 

  27. I. Rechenberg, Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipen der Biologischen Evolution, 2nd ed. (Fromman-Holzboog, Stuttgart, 1993).

    Google Scholar 

  28. C. Reeves, Hybrid genetic algorithms for bin-packing and related problems, Ann. Oper. Res. 63 (1996) 371–396.

    Google Scholar 

  29. C. Reeves, Genetic algorithms for the operations researcher, INFORMS J. Comput. 9(3) (1997) 231–251.

    Google Scholar 

  30. P. Ross, What are genetic algorithms good at? INFORMS J. Comput. 9(3) (1997) 260–262.

    Google Scholar 

  31. J.D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms, in: Proceedings of an International Conference on Genetic Algorithms and their Applications (1985) pp. 93–100.

  32. R.J. Shanker, R.E. Turner and A.A. Zoltners, Sales territory design: An integrated approach, Management Sci. 22(3) (1975) 309–320.

    Google Scholar 

  33. M. Segal and D.B. Weinbergey, Turfing, Oper. Res. 25(3) (1977) 367–386.

    Google Scholar 

  34. W.M. Spears, The role of mutation and recombination in evolutionary algorithms, Doctoral Dissertation Thesis, George Mason University, VA (1998).

    Google Scholar 

  35. R.G. Torricelli and J.I. Porter, Toward the 1980 census: The reapportionment of New Jersey's congressional districts, Rutgers J. Comput. Technol. Law 7(135) (1979).

  36. W. Vickrey, On the prevention of gerrymandering, Political Sci. Quart. 76(105) (1961).

  37. A.A. Zoltners, A unified approach to sales territory alignment, in: Sales Management: New Developments from Behavioral and Decision Model Research (Marketing Science Institute, Cambridge, MA, 1979) pp. 360–376.

    Google Scholar 

  38. A.A. Zoltners and P. Sinha, Integer programming models for sales resource allocation, Management Sci. 26(3) (1980) 242–260.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Business Management, North Carolina State University, Raleigh, NC, 27695, USA

    Paul K. Bergey

  2. Department of Business Information Technology, Virginia Tech, Blacksburg, VA, 24060, USA

    Cliff T. Ragsdale

  3. The World Bank, 1818 H Street NW, Room No. J9-027, Washington, DC, 20433, USA

    Mangesh Hoskote

Authors
  1. Paul K. Bergey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cliff T. Ragsdale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mangesh Hoskote
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul K. Bergey.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bergey, P.K., Ragsdale, C.T. & Hoskote, M. A Simulated Annealing Genetic Algorithm for the Electrical Power Districting Problem. Annals of Operations Research 121, 33–55 (2003). https://doi.org/10.1023/A:1023347000978

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1023347000978

search

Navigation