Tabu search optimization of horizontal and vertical alignments of forest roads

Abstract

This article describes a program for optimizing horizontal and vertical alignments of forest roads using Tabu search, a modern heuristic technique. Once a series of intersection points (IPs) is selected manually, the program generates alternative horizontal and vertical alignments. The program precisely generates ground profile and cross sections using a high-resolution digital elevation model (DEM) derived from light detection and ranging (LiDAR) data. It accurately calculates earthwork volumes for curved roadways using the Pappus-based method. The program also estimates construction and maintenance costs. Tabu search optimizes forest road alignments based on the total costs. The application of the program to part of Capitol State Forest in Washington State, USA, indicated that the program successfully found better alignments than manually selected initial alignments. The effect of initial solutions and the number of iterations on the Tabu search process was examined. The result showed that the solutions were improved using the best solutions with the smaller number of grade change points as the initial solutions. It also showed that a small number of iterations could be used to reduce computation time due to the fact that Tabu search is based on a gradient search technique. Finally, the Dijkstra method was examined to find initial solutions without manually initialized solutions. The program, when combined with the Dijkstra method, could find similar-quality solutions from manually initialized solutions. The program will become useful with further tests and verifications.