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A problem of forest harvesting and road building solved through model strengthening and Lagrangean relaxation

机译:通过模型增强和拉格朗日松弛法解决了森林采伐和道路建设问题

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we consider a problem of forest planning on pine plantations over a two to five year horizon. Basic decisions concern the areas to harvest in each period, the amount of timber to produce to satisfy aggregate demands for log exports, sawmills and pulp plants, and the roads to build for access and storage of timber. A linear programming model with 0-1 variables describes the decision process. Solution strategies involve strengthening of the model, lifting some of the constraints, and applying Lagrangean relaxation. Results on real planning problems show that even as these problems become more complex, the proposed solution strategies lead to very good solutions, reducing the residual gap for the most difficult data set from 162% to 1.6%, and for all data sets to 2.6% or less. [References: 22]
机译:我们考虑在两到五年的时间内对松树人工林进行森林规划的问题。基本决策涉及每个时期的采伐面积,为满足原木出口,锯木厂和纸浆厂的总需求而生产的木材量,以及为木材的获取和储存而建造的道路。具有0-1个变量的线性规划模型描述了决策过程。解决方案策略包括加强模型,解除某些约束以及应用拉格朗日松弛法。实际计划问题的结果表明,即使这些问题变得更加复杂,所提出的解决方案策略仍可带来很好的解决方案,将最困难的数据集的剩余差距从162%减少到1.6%,并将所有数据集的剩余差距减少到2.6%或更少。 [参考:22]

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