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Nonlinear programming and genetic search application for production scheduling in coal mines

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Abstract

Efforts to eliminate unnecessary scheduling and inventory problems faced by the coal industry today initiated the development of a generalized, nonlinear programming model. Although no accepted methodology for developing such a model for this purpose currently exists, one was created and successfully tested. Production and transportation cost estimates were obtained from independent coal mines in Illinois, Virginia, and Pennsylvania, and based on these estimates, a hypothetical model was developed and tested using genetic search for nonlinear optimization. The results of our tests indicate that the model has potential for decision support in coal mines.

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References

  1. Annual Energy Outlook, Energy Information Agency, Department of Energy (1994).

  2. R.W. Barbaro and R.V. Ramani, Generalized multi-period MIP model for production scheduling and processing facilities selection and location, Mining Engineering 38(2) (1986) 107–114.

    Google Scholar 

  3. J.J. Bernardo and E. Gillenwater, Sequencing rules for productivity improvements in underground coal mining, Decision Sciences 22(3) (1991) 620–634.

    Google Scholar 

  4. J.L. Blanton and R.L. Wainright, Multiple vehicle routing with time and capacity constraints using genetic algorithms, in: Proceedings of 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, CA, 1993) pp. 408–415.

    Google Scholar 

  5. T.W. Camm, Simplified cost models for prefesaibility mineral evaluations, Mining Engineering (1994) 559–562.

  6. K. De Jong, Learning with genetic algorithms: An overview, Machine Learning (1988) 121–138.

  7. F. Glover, Tabu search: A tutorial, Interfaces 20(1) (1989) 74–94.

    Google Scholar 

  8. D.E. Goldberg and R. Lingle, Alleles, Loci, and the Travelling Salesman Problem, in: Proceedings of 1st International Conference on Genetics and their Applications, ed. J.J. Grefenstette (Carnegie- Mellon University, 1991) pp. 154–159.

  9. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (1989).

  10. J.L. Graff, Coal War, Time (July 7, 1997).

  11. F.S. Hiller and G.J. Lieberman, Introduction to Operations Research (McGraw-Hill, 1995).

  12. M.R. Hillard, G.E. Liepins and M. Palmer, Machine learning applications to job shop scheduling, in: Proceedings of the 1st International Conference on Industrial and Engineering Applications ofArtificial Intelligence and Expert Systems, University of Tennessee Space Institute, June 1-3, 1988 (ACM Press, 1988).

  13. A. Kershenbaum, When genetic algorithms work best, INFORMS Journal on Computing 9(3) (1997) 254–255.

    Google Scholar 

  14. Y.C. Kim and W.L. Kai, Long range mine sequencing with 0-1 programming, in: Proceedings of the 22nd International APCOM, Vol. 1, Berlin, Germany (1990) pp. 131–145.

    Google Scholar 

  15. S. Kirkpatrick, C.D. Gelatt and M.P. Vecchi, Optimization by simulated annealing, Science 220(4578) (1983) 671–679.

    Google Scholar 

  16. C.Y. Lee and J.Y. Choi, A genetic algorithm for job sequencing problems with distinct due dates and general early-tardy penalty weights, Computers & Operations Research 22(8) (1995) 857–869.

    Google Scholar 

  17. Y. Lizotte and J. Elbrond, Choice of mine-mill capacities and production schedules using open-ended dynamic programming, CIM Bulletin 75(839) (1982) 154–163.

    Google Scholar 

  18. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs (Springer, 1994).

  19. K. Mukherjee, Application of an interactive method for MOILP in project selection decision - A case from Indian coal mining industry, International Journal of Production Economics 36 (1994) 203–211.

    Google Scholar 

  20. K. Mukherjee and A. Bera, Application of goal programming in project selection decision - A case study from the Indian coal mining industry, European Journal of Operational Research 82 (1995) 18–25.

    Google Scholar 

  21. A.L. Nordstrom and S. Tufeekci, A genetic algorithm for the talent scheduling problem, Computers & Operations Research 21(8) (1994) 927–940.

    Google Scholar 

  22. M.T. Pana, The simulation approach to open pit design, in: Proceedings of 5th International Symposium on Computer Applications in the Mineral Industry, Tuscon, AZ (1965) pp. ZZ1–ZZ24.

  23. P.C. Pendharkar, A fuzzy linear programming application for production scheduling in coal mines, Computers & Operations Research 24(2) (1997) 1141–1149.

    Google Scholar 

  24. C.R. Reeves, Genetic algorithms for the operations researcher, INFORMS Journal on Computing 7(3) (1997) 231–250.

    Google Scholar 

  25. C.R. Reeves, Using genetic algorithms with small populations, in: Proceedings of 5th International Conference on Genetic Algorithms, ed. S. Forrest (Morgan Kaufmann, San Mateo, CA, 1993) pp. 92–99.

    Google Scholar 

  26. Y. Shi and Y.-H. Liu, Fuzzy potential solutions in multi-criteria and multi-constraint level linear programming problems, Fuzzy Sets and Systems 60 (1993) 163–179.

    Google Scholar 

  27. G. Syswerda, Schedule optimization using genetic algorithms, in: Handbook of Genetic Algorithms, ed. L. Davis (Van Nostrand Reinhold, 1991) pp. 333–349.

  28. K. Thomas, Barges vie with rail, The Journal of Commerce (January 6, 1997) p. 55C.

    Google Scholar 

  29. R. Watson, Tracking rail merger mania, The Journal of Commerce (January 6, 1997) p. 17C.

    Google Scholar 

  30. L.D. Whitley, T. Starkweather and D. Fuquay, Scheduling problems and the travelling salesman: The genetic edge recombination operator, in: Proceedings of the 3rd International Conference on Genetic Algorithms (Morgan Kaufmann, 1989) pp. 133–140.

  31. A. Wren and D.O. Wren, A genetic algorithm for public transportation scheduling, Computers & Operations Research 22 (1995) 101–110.

    Google Scholar 

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Authors and Affiliations

  1. Penn State University at Harrisburg, 777 West Harrisburg Pike, Middletown, PA, 17057, USA

    Parag C. Pendharkar

  2. MIS and Decision Sciences, Eberly College of Business Administration, Indiana University of Pennsylvania, Indiana, PA, 15705, USA

    James A. Rodger

Authors
  1. Parag C. Pendharkar
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  2. James A. Rodger
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Pendharkar, P.C., Rodger, J.A. Nonlinear programming and genetic search application for production scheduling in coal mines. Annals of Operations Research 95, 251–267 (2000). https://doi.org/10.1023/A:1018958209290

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