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A long-term, spatially constrained harvest scheduling model for Eucalyptus plantations in the southeast of Mexico.

机译：墨西哥东南部桉树人工林的长期，受空间限制的采伐计划模型。

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In the states of Tabasco and Chiapas, Mexico there is a lack of long-term harvest scheduling models that consider the effects of the harvest activities on the surrounding areas. Additionally, these problems are combinatorial in nature, which makes them hard to solve. Consequently, only harvest scheduling for small areas can be solved to optimality using traditional approaches such as integer programming (IP). In this study, a genetic algorithms (GA) model was used to generate multiple viable solutions for long-term spatially constrained problems on large areas with a great number of management units. This model enables consideration of regeneration and reharvest in forest planning. The flexibility of the model allows it to handle a different set of time periods, database sizes, different species and diverse tree growth models.; The data set employed corresponds to a eucalyptus plantation with a cutting cycle seven years and a planning horizon of 10 rotation periods. Total plantation area is 300,000 ha, divided in 5,388 harvest units. IP was used as a standard to validate the efficiency and accuracy of the GA method. The GA performance with different combinations of genetic operators was tested. Scheduled volume flow for simulated communities was computed. Additionally, three different volume assignment scenarios (low, medium and high) were compared to estimate the effect of volume assignment on the spatial optimization output.; The significant findings of this research are: (1) a long-term spatially constrained robust solution was found through the use of genetic algorithms for a large area with more harvest units than those reported elsewhere, (2) the solution allowed re-harvest in the same planning horizon, (3) most of the genetic algorithms runs performed better than the integer programming, and (4) on average the volume scheduled for every simulated community was comparable for the two methods used in the work. In both cases, the percentages of the potential volume ranged between 7 and 29%.
机译：在墨西哥的塔巴斯科州和恰帕斯州，缺乏考虑收获活动对周边地区影响的长期收获调度模型。另外，这些问题本质上是组合的，这使得它们很难解决。因此，使用传统方法（例如整数编程（IP））只能将针对小区域的收获调度解决到最佳状态。在这项研究中，使用遗传算法（GA）模型为具有大量管理单元的大区域上的长期空间受限问题生成多个可行的解决方案。该模型可以在森林规划中考虑再生和收割。该模型的灵活性使其可以处理一组不同的时间段，数据库大小，不同的物种和不同的树木生长模型。使用的数据集对应于一个砍伐周期为7年，规划周期为10个旋转周期的桉树人工林。人工林总面积为300,000公顷，分为5388个收获单位。 IP被用作验证GA方法的效率和准确性的标准。测试了遗传操作员不同组合下的GA性能。计算了模拟社区的预定体积流量。另外，比较了三种不同的体积分配方案（低，中和高），以估计体积分配对空间优化输出的影响。这项研究的重要发现是：（1）通过使用遗传算法在大面积区域内获得了长期空间受限的鲁棒解决方案，该区域的收获单位比其他地方报道的要多，（2）该解决方案允许在在相同的计划范围内，（3）大多数遗传算法的运行性能都优于整数编程，并且（4）平均而言，每个模拟社区的计划工作量与工作中使用的两种方法相当。在这两种情况下，潜在交易量的百分比在7％到29％之间。

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