### Abstract

Optimal parameter settings to improve the efficiency of solving harvest scheduling models with adjacency constraints were examined using Ilog's Cplex ® 11.2 optimizer tuning tool. A total of 160 randomly generated hypothetical forests were created with either 50 or 100 stands and four age-class distributions. Mixed integer programming problems were formulated in Model I form with four different adjacency constraint types, two Unit Restriction Model (URM) adjacency constraints (Pairwise and Maximal Clique) and two Area Restriction Model (ARM) formulations (Path and Generalized Management Unit). A total of 640 problem sets-where a set is a common forest size, age-class distribution, and adjacency constraint type-were tuned to determine optimal parameter settings and then were solved at both the default and optimal settings. In general, mean solution time was less for a given problem set using the optimal parameters compared to the default parameters. The results discussed provide a simple approach to decrease the solution time of solving mixed integer forest planning problems with adjacency constraints.

Original language | English (US) |
---|---|

Pages (from-to) | 16-26 |

Number of pages | 11 |

Journal | Mathematical and Computational Forestry and Natural-Resource Sciences |

Volume | 4 |

Issue number | 1 |

State | Published - Feb 28 2012 |

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### All Science Journal Classification (ASJC) codes

- Forestry
- Computer Science Applications
- Applied Mathematics
- Environmental Engineering

### Cite this

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*Mathematical and Computational Forestry and Natural-Resource Sciences*, vol. 4, no. 1, pp. 16-26.

**Optimal parameter settings for solving harvest scheduling models with adjacency constraints.** / Manning, Phillip J.; McDill, Marc Eric.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Optimal parameter settings for solving harvest scheduling models with adjacency constraints

AU - Manning, Phillip J.

AU - McDill, Marc Eric

PY - 2012/2/28

Y1 - 2012/2/28

N2 - Optimal parameter settings to improve the efficiency of solving harvest scheduling models with adjacency constraints were examined using Ilog's Cplex ® 11.2 optimizer tuning tool. A total of 160 randomly generated hypothetical forests were created with either 50 or 100 stands and four age-class distributions. Mixed integer programming problems were formulated in Model I form with four different adjacency constraint types, two Unit Restriction Model (URM) adjacency constraints (Pairwise and Maximal Clique) and two Area Restriction Model (ARM) formulations (Path and Generalized Management Unit). A total of 640 problem sets-where a set is a common forest size, age-class distribution, and adjacency constraint type-were tuned to determine optimal parameter settings and then were solved at both the default and optimal settings. In general, mean solution time was less for a given problem set using the optimal parameters compared to the default parameters. The results discussed provide a simple approach to decrease the solution time of solving mixed integer forest planning problems with adjacency constraints.

AB - Optimal parameter settings to improve the efficiency of solving harvest scheduling models with adjacency constraints were examined using Ilog's Cplex ® 11.2 optimizer tuning tool. A total of 160 randomly generated hypothetical forests were created with either 50 or 100 stands and four age-class distributions. Mixed integer programming problems were formulated in Model I form with four different adjacency constraint types, two Unit Restriction Model (URM) adjacency constraints (Pairwise and Maximal Clique) and two Area Restriction Model (ARM) formulations (Path and Generalized Management Unit). A total of 640 problem sets-where a set is a common forest size, age-class distribution, and adjacency constraint type-were tuned to determine optimal parameter settings and then were solved at both the default and optimal settings. In general, mean solution time was less for a given problem set using the optimal parameters compared to the default parameters. The results discussed provide a simple approach to decrease the solution time of solving mixed integer forest planning problems with adjacency constraints.

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M3 - Article

AN - SCOPUS:84860294642

VL - 4

SP - 16

EP - 26

JO - Mathematical and Computational Forestry and Natural-Resource Sciences

JF - Mathematical and Computational Forestry and Natural-Resource Sciences

SN - 1946-7664

IS - 1

ER -