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Applying simulated annealing using different methods for the neighborhood search in forest planning problems

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  • Borges, Paulo
  • Eid, Tron
  • Bergseng, Even

Abstract

Adjacency constraints along with even flow harvest constraints are important in long term forest planning. Simulated annealing (SA) is previously successfully applied when addressing such constraints. The objective of this paper was to assess the performance of SA under three new methods of introducing biased probabilities in the management unit (MU) selection and compare them to the conventional method that assumes uniform probabilities. The new methods were implemented as a search vector approach based on the number of treatment schedules describing sequences of silvicultural treatments over time and standard deviation of net present value within MUs (Methods 2 and 3, respectively), and by combining the two approaches (Method 4). We constructed three hundred hypothetical forests (datasets) for three different landscapes characterized by different initial age class distributions (young, normal and old). Each dataset encompassed 1600 management units. The evaluation of the methods was done by means of objective function values, first feasible iteration and time consumption. Introducing a bias in the MU selection improves solutions compared to the conventional method (Method 1). However, an increase of computational time is in general needed for the new methods. Method 4 is the best alternative because, for large parts of the datasets, produced the best average and maximum objective function values and had lower time consumption than Methods 2 and 3. Although Method 4 performed very well, Methods 2 and 3 should not be neglected because for a considerable number of datasets the maximum objective function values were obtained by these methods.

Suggested Citation

  • Borges, Paulo & Eid, Tron & Bergseng, Even, 2014. "Applying simulated annealing using different methods for the neighborhood search in forest planning problems," European Journal of Operational Research, Elsevier, vol. 233(3), pages 700-710.
  • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:700-710
    DOI: 10.1016/j.ejor.2013.08.039
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    References listed on IDEAS

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    Cited by:

    1. Liu, Wan Yu, 2016. "A Study on the Forest Thinning Planning Problem Considering Carbon Sequestration and Emission," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235463, Agricultural and Applied Economics Association.
    2. Moriguchi, Kai & Ueki, Tatsuhito & Saito, Masashi, 2017. "Identification of effective implementations of simulated annealing for optimizing thinning schedules for single forest stands," European Journal of Operational Research, Elsevier, vol. 262(3), pages 1094-1108.
    3. Min Kong & Xinbao Liu & Jun Pei & Panos M. Pardalos & Nenad Mladenovic, 2020. "Parallel-batching scheduling with nonlinear processing times on a single and unrelated parallel machines," Journal of Global Optimization, Springer, vol. 78(4), pages 693-715, December.
    4. Wenjuan Fan & Jun Pei & Xinbao Liu & Panos M. Pardalos & Min Kong, 2018. "Serial-batching group scheduling with release times and the combined effects of deterioration and truncated job-dependent learning," Journal of Global Optimization, Springer, vol. 71(1), pages 147-163, May.
    5. Liu, Wan-Yu & Lin, Chun-Cheng & Su, Ke-Hong, 2017. "Modelling the spatial forest-thinning planning problem considering carbon sequestration and emissions," Forest Policy and Economics, Elsevier, vol. 78(C), pages 51-66.
    6. Augustynczik, Andrey Lessa Derci & Arce, Julio Eduardo & Yousefpour, Rasoul & da Silva, Arinei Carlos Lindbeck, 2016. "Promoting harvesting stands connectivity and its economic implications in Brazilian forest plantations applying integer linear programming and simulated annealing," Forest Policy and Economics, Elsevier, vol. 73(C), pages 120-129.
    7. Nader Naderializadeh & Kevin A. Crowe & Melika Rouhafza, 2022. "Solving the integrated forest harvest scheduling model using metaheuristic algorithms," Operational Research, Springer, vol. 22(3), pages 2437-2463, July.

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