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Modeling and solving a logging camp location problem

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Abstract

Harvesting plans for Canadian logging companies tend to cover wider territories than before. Long transportation distances for the workers involved in logging activities have thus become a significant issue. Often, cities or villages to accommodate the workers are far away. A common practice is thus to construct camps close to the logging regions, containing the complete infrastructure to host the workers. The problem studied in this paper consists in finding the optimal number, location and size of logging camps. We investigate the relevance and advantages of constructing additional camps, as well as expanding and relocating existing ones, since the harvest areas change over time. We model this problem as an extension of the Capacitated Facility Location Problem. Economies of scale are included on several levels of the cost structure. We also consider temporary closing of facility parts and particular capacity constraints that involve integer rounding on the left hand side. Results for real-world data and for a large set of randomly generated instances are presented.

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References

  • Albareda-Sambola, M., Fernandez, E., Hinojosa, Y., & Puerto, J. (2009). The multi-period incremental service facility location problem. Computers & Operations Research, 36(5), 1356–1375.

    Article  Google Scholar 

  • Ballou, R. H. (1968). Dynamic warehouse location analysis. Journal of Marketing Research, 5(3), 271–276.

    Article  Google Scholar 

  • Bredström, D., Jönsson, P., & Rönnqvist, M. (2010). Annual planning of harvesting resources in the forest industry. International Transactions in Operational Research, 17(2), 155–177.

    Article  Google Scholar 

  • Canel, C., Khumawala, B. M., Law, J., & Loh, A. (2001). An algorithm for the capacitated, multi-commodity multi-period facility location problem. Computers & Operations Research, 28(5), 411–427.

    Article  Google Scholar 

  • Carlsson, D., D’Amours, S., Martel, A., & Rönnqvist, M. (2009). Supply chain planning models in the pulp and paper industry. INFOR. Information Systems and Operational Research, 47(3), 167–183.

    Article  Google Scholar 

  • Correia, I., & Captivo, M. (2003). A Lagrangean heuristic for a modular capacitated location problem. Annals of Operations Research, 122, 141–161.

    Article  Google Scholar 

  • D’Amours, S., Rönnqvist, M., & Weintraub, A. (2008). Using operational research for supply chain planning in the forest products industry. INFOR. Information Systems and Operational Research, 46(4), 265–281.

    Article  Google Scholar 

  • Dias, J. (2006). Capacitated dynamic location problems with opening, closure and reopening of facilities. IMA Journal of Management Mathematics, 17(4), 317–348.

    Article  Google Scholar 

  • Gendron, B., & Crainic, T. (1994). Relaxations for multicommodity capacitated network design problems (Technical report). Publication CRT-945, Centre de recherche sur les transports, Université de Montréal.

  • Geoffrion, A. M., & Graves, G. W. (1974). Multicommodity distribution system design by benders decomposition. Management Science, 20(5), 822–844.

    Article  Google Scholar 

  • Gouveia, L., & Saldanha da Gama, F. (2006). On the capacitated concentrator location problem: a reformulation by discretization. Computers & Operations Research, 33(5), 1242–1258.

    Article  Google Scholar 

  • Hamacher, H., & Nickel, S. (1998). Classification of location models. Location Science, 6(1–4), 229–242.

    Article  Google Scholar 

  • Holmberg, K. (1994). Solving the staircase cost facility location problem with decomposition and piecewise linearization. European Journal of Operational Research, 75(1), 41–61.

    Article  Google Scholar 

  • Holmberg, K., & Ling, J. (1997). A Lagrangean heuristic for the facility location problem with staircase costs. European Journal of Operational Research, 97(1), 63–74.

    Article  Google Scholar 

  • Klose, A., & Drexl, A. (2005). Facility location models for distribution system design. European Journal of Operational Research, 162(1), 4–29.

    Article  Google Scholar 

  • Lee, C. Y. (1991). An optimal algorithm for the multiproduct capacitated facility location problem with a choice of facility type. Computers & Operations Research, 18(2), 167–182.

    Article  Google Scholar 

  • Melo, M., Nickel, S., & Saldanha da Gama, F. (2005). Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Computers & Operations Research, 33(1), 181–208.

    Article  Google Scholar 

  • Melo, M., Nickel, S., & Saldanha da Gama, F. (2009). Facility location and supply chain management—a review. European Journal of Operational Research, 196(2), 401–412.

    Article  Google Scholar 

  • Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: a review. European Journal of Operational Research, 111(3), 423–447.

    Article  Google Scholar 

  • Paquet, M., Martel, A., & Desaulniers, G. (2004). Including technology selection decisions in manufacturing network design models. International Journal of Computer Integrated Manufacturing, 17(2), 117–125.

    Article  Google Scholar 

  • Peeters, D., & Antunes, A. P. (2001). On solving complex multi-period location models using simulated annealing. European Journal of Operational Research, 130(1), 190–201.

    Article  Google Scholar 

  • Revelle, C., & Eiselt, H. (2005). Location analysis: a synthesis and survey. European Journal of Operational Research, 165(1), 1–19.

    Article  Google Scholar 

  • Revelle, C., Eiselt, H., & Daskin, M. S. (2008). A bibliography for some fundamental problem categories in discrete location science. European Journal of Operational Research, 184(3), 817–848.

    Article  Google Scholar 

  • Rönnqvist, M. (2003). Optimization in forestry. Mathematical Programming, 284, 267–284.

    Google Scholar 

  • Shulman, A. (1991). An algorithm for solving dynamic capacitated plant location problems with discrete expansion sizes. Operations Research, 39(3), 423–436.

    Article  Google Scholar 

  • Smith, H. K., Laporte, G., & Harper, P. R. (2009). Locational analysis: highlights of growth to maturity. Journal of the Operational Research Society, 60, S140–S148.

    Article  Google Scholar 

  • Sridharan, R. (1991). A Lagrangian heuristic for the capacitated plant location problem with side constraints. Journal of the Operational Research Society, 42(7), 579–585.

    Article  Google Scholar 

  • Sridharan, R. (1995). The capacitated plant location problem. European Journal of Operational Research, 87(2), 203–213.

    Article  Google Scholar 

  • Troncoso, J., & Garrido, R. (2005). Forestry production and logistics planning: an analysis using mixed-integer programming. Forest Policy and Economics, 7(4), 625–633.

    Article  Google Scholar 

  • Warszawski, A. (1973). Multi-dimensional location problems. Operational Research Quarterly, 24(2), 165–179.

    Article  Google Scholar 

  • Weintraub, A., & Romero, C. (2006). Operations research models and the management of agricultural and forestry resources: a review and comparison. Interfaces, 36(5), 446–457.

    Article  Google Scholar 

  • Wesolowsky, G. O. (1973). Dynamic facility location. Management Science, 19(11), 1241–1248.

    Article  Google Scholar 

  • Wesolowsky, G. O., & Truscott, W. G. (1975). The multiperiod location-allocation problem with relocation of facilities. Management Science, 22(1), 57–65.

    Article  Google Scholar 

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Acknowledgements

We would like to thank Mathieu Blouin and Jean Favreau from FPInnovations for their valuable support throughout this study and for providing the data used in the experiments. The authors are also grateful to MITACS, the Natural Sciences and Engineering Research Council of Canada (NSERC) and FPInnovations for their financial support.

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Correspondence to Sanjay Dominik Jena.

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Jena, S.D., Cordeau, JF. & Gendron, B. Modeling and solving a logging camp location problem. Ann Oper Res 232, 151–177 (2015). https://doi.org/10.1007/s10479-012-1278-z

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