Modeling the Multi-Modal Transport of Logs and the Effects of Changing Fuel Prices

The increase in global competition and energy prices over the past decade has forced industries to search for potential savings in their transportation supply chains and logistics systems. In some industries, such as the forest products industry, minimizing the transportation costs of raw materials is even more crucial. The relatively low value and high weight of their raw materials accounts for nearly half of their delivered cost. The forest products industry in the upper Midwest is based around selective cutting, making long distance transportation of logs to the mills commonplace. This transportation of logs from the forest to the mills uses either trucking or a combination of rail and trucking. The long distances and low value of the logs makes them a prime candidate for movement by rail. The question among many in the industry is whether rail is being used to its full extent and whether utilizing it more would be financially advantageous. This study evaluates the question by building a model that finds the optimal route, including mode choice, based on rates for the transportation modes. A sample of truck trips was run through the model, evaluating the rate optimal route for each trip. The results of the model suggest that using rate optimal transportation for each trip, 22% of the ton-miles should have travelled by rail. The model was also used for fuel price sensitivity analysis. Five sample diesel fuel prices were selected and the model was run to identify the optimal modal split at each of these fuel prices. Based on the rate optimization results, the model suggests that 7% of the total ton-miles should shift from truck to rail for every one dollar increase in fuel prices. ii Acknowledgements