Optimizing forest road planning in a sustainable forest management area in the Brazilian Amazon.

The construction of forest roads in Brazilian Amazon is costly and has a significant environmental impact. Several practices and principles must be observed to comply with legislation, to preserve the remaining forest, and to ensure sustainable exploitation. Road planning is complex in this context, based on the number of aspects and variables that must be considered. This research aimed to evaluate computational methods' effectiveness in planning forest roads, optimizing resources to reduce damage to the remaining forest, compared to traditional planning methods. The study area was a native forest under a sustainable forest management regime located in municipalities of Terra Santa and Oriximiná, in Pará, in Brazilian Amazon. Data obtained from area made it possible formulate six instances of different sizes. A binary integer linear programming model was used, solved using CPLEX software, and Dijkstra, Bellman-Ford, Dial, and D'Esopo-Pape shortest path algorithm, implemented in C programming language. During processing of instances, the time taken to obtain the solution increased according to size of instance, however, time difference was not significant. Among the evaluated algorithms, the D'Esopo-Pape algorithm showed the best performance. The evaluated methods were effective in obtaining an optimal solution for proposed forest road planning. The solutions obtained using computational methods more effectively considered the restrictions associated with sustainable forest management, in contrast to those derived from the traditional planning by forestry company.

Data for a meta-analysis of the adaptive layer in adaptive large neighborhood search.

Meta-analysis, a systematic statistical examination that combines the results of several independent studies, has the potential of obtaining problem- and implementation-independent knowledge and understanding of metaheuristic algorithms, but has not yet been applied in the domain of operations research. To illustrate the procedure, we carried out a meta-analysis of the adaptive layer in adaptive large neighborhood search (ALNS). Although ALNS has been widely used to solve a broad range of problems, it has not yet been established whether or not adaptiveness actually contributes to the performance of an ALNS algorithm. A total of 134 studies were identified through Google Scholar or personal e-mail correspondence with researchers in the domain, 63 of which fit a set of predefined eligibility criteria. The results for 25 different implementations of ALNS solving a variety of problems were collected and analyzed using a random effects model. This dataset contains a detailed comparison of ALNS with the non-adaptive variant per study and per instance, together with the meta-analysis summary results. The data enable to replicate the analysis, to evaluate the algorithms using other metrics, to revisit the importance of ALNS adaptive layer if results from more studies become available, or to simply consult the ready-to-use formulas in the summary file to carry out a meta-analysis of any research question. The individual studies, the meta-analysis and its results are described and interpreted in detail in Renata Turkes, Kenneth Sörensen, Lars Magnus Hvattum, Meta-analysis of Metaheuristics: Quantifying the Effect of Adaptiveness in Adaptive Large Neighborhood Search, in the European Journal of Operational Research.

Metaheuristics applied for storage yards allocation in an Amazonian sustainable forest management area.

In the sustainable management of Amazonian forests, it is essential to carry out the optimal planning of logging infrastructures to reduce costs and environmental impacts. However, there is a high degree of complexity due to the number of variables involved. Among these infrastructures, wood storage yards are of utmost importance as they directly influence the opening of forest roads and trails. The objective of this research was to evaluate the allocation of wood storage yards through exact solution and metaheuristics in a forest management area. The study area was a native forest under sustainable forest management regime located in the Brazilian Amazon. Three instances were formulated involving 5947 trees and 3172 wood storage yards facilities. We used a binary integer programming model solved by CPLEX and the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP), Tabu Search (TS), Variable Neighborhood Search (VNS) and Simulated Annealing (SA). GAP values increased as a function of instances. Although all metaheuristics obtained significant solutions with shorter processing times, only SA obtained feasible solutions in all executions for all three instances. In general, the metaheuristics were efficient in obtaining feasible solutions faster than CPLEX, which represents the feasibility of the planning of allocation storage large areas, and without significant losses of best-known solution. The SA presented the best performance in the three evaluated instances. Contribution of this study can be highlighted: evaluation of alternative computational methods for planning the allocation of wooden storage yards; evidence was obtained of effectiveness and efficiency of assessed metaheuristics and, the applicability of approximate methods in this problem was evaluated.