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

Author

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  • Parag Pendharkar
  • James Rodger

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. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Parag Pendharkar & James Rodger, 2000. "Nonlinear programming and genetic search application for production scheduling in coal mines," Annals of Operations Research, Springer, vol. 95(1), pages 251-267, January.
    • Handle: RePEc:spr:annopr:v:95:y:2000:i:1:p:251-267:10.1023/a:1018958209290
    DOI: 10.1023/A:1018958209290
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    Citations

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    Cited by:

    1. F. Rodriguez & C. Blum & C. García-Martínez & M. Lozano, 2012. "GRASP with path-relinking for the non-identical parallel machine scheduling problem with minimising total weighted completion times," Annals of Operations Research, Springer, vol. 201(1), pages 383-401, December.
    2. Song Jiu, 2021. "A two-phase approach for integrating preventive maintenance with production and delivery in an unreliable coal mine," Journal of Heuristics, Springer, vol. 27(6), pages 991-1020, December.
    3. Alexandra M. Newman & Enrique Rubio & Rodrigo Caro & Andrés Weintraub & Kelly Eurek, 2010. "A Review of Operations Research in Mine Planning," Interfaces, INFORMS, vol. 40(3), pages 222-245, June.
    4. Linder, Eric & Marbuah, George, 2019. "The cost of transparency: Stock market reactions to introduction of the Extractive Sector Transparency Measures Act in Canada," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    5. Pendharkar, Parag C., 2001. "An empirical study of design and testing of hybrid evolutionary-neural approach for classification," Omega, Elsevier, vol. 29(4), pages 361-374, August.
    6. Singh, Gaurav & Sier, David & Ernst, Andreas T. & Gavriliouk, Olena & Oyston, Rob & Giles, Tracey & Welgama, Palitha, 2012. "A mixed integer programming model for long term capacity expansion planning: A case study from The Hunter Valley Coal Chain," European Journal of Operational Research, Elsevier, vol. 220(1), pages 210-224.
    7. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).

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