Applying pareto ant colony optimization to solve bi-objective forest transportation planning problems

Problems related to the transportation of timber products have traditionally involved finding routes that minimize timber hauling and road construction costs. However, increasing environmental concerns have introduced negative impacts (i.e., soil erosion and water quality) into forest transportation planning problems (FTPPs). In this paper, we designed and implemented a multi-objective ant colony optimization algorithm (MOACO) to solve a bi-objective FTPP that considers both transportation cost and environmental impacts. The goal is to provide decision makers with different timber transportation planning alternatives to help them make informed decisions. The MOACO incorporates various design choices that have been identified to have better performances in recent research literature. To test for performance, we applied the algorithm to ten FTPPs. Experimental results demonstrate the MOACO was able to solve all test problems under different stop conditions.