- Changes in the global climate and forest management practices have given rise to increasing numbers and severity of wildfires. More than five million acres burned in the United States in 2017, while in Canada 7.4 million acres burned. In particular, an increasing amount of dead woody biomass is a key factor in forest fire hazards. The call for mitigating the effects of climate change, specifically focusing on reducing the risk of wildfires, has attracted considerable global attention toward renewable energy sources. The objective of this research is to provide decision makers in private industry and governmental agencies the ability to reliably assess economic, environmental, and social criteria simultaneously while optimizing bio-oil supply chains in managing the land and forests to decrease wildfire risks. An optimized biomass to bio oil supply chain is presented by using a mathematical problem considering economic, environmental, and social criteria. The focus of the application of this work is on northwest Oregon forests. The production of bio-oil is not only able to help mitigate climate change impacts such as forest fire hazards, but it can also improve energy independence, employment opportunities, and economic development.
To extend prior related research, a single objective mathematical model is first presented, which relaxes a limitation of prior mathematical models for bio-oil supply chain problems by considering carbon cost as a part of the total supply chain cost. Since the model is a mixed integer linear programming problem, a metaheuristic optimization approach (genetic algorithm) is designed to obtain an optimized solution. The proposed mathematical model can be applied in the design of a biomass to bio-oil supply chain including mobile refineries, in which total cost consists of logistics cost and carbon cost. Decision makers will be able to apply the proposed genetic algorithm for large scale problems to overcome restrictions of exact methods.
As the demand for sustainable supply chains continues, logistics problems must be designed to balance solutions across the three pillars of sustainability: the economy, environment, and society. Thus, a multi-objective mathematical model is next developed for a bio oil supply chain, which includes six levels: harvesting sites, collection sites, mobile refineries, fixed refineries, distribution centers, and residential areas. The branch-and-cut search in CPLEX software solves the proposed model using data from northwest Oregon forests. The model obtains optimal values for three decision variables, i.e., mass of biomass to be transported, mass of bio-oil to be transported, and the facility locations, to simultaneously optimize total cost, carbon footprint, and number of jobs created. From evaluation of the model, it is found that supplementing a traditional bio-oil supply chain with mobile refineries has the potential to significantly reduce the cost of bio-oil. Sensitivity analysis is performed to evaluate the effect of key parameters on supply chain objectives under different scenarios. It was also found that the percentage yield parameter and mobile refinery capacity have a more significant effect on the selected objectives than the other parameters tested. Based on the supply chain modeling, the behavior of the predicted cost of bio-oil, carbon footprint, and number of jobs created is intuitive with respect to the changes in the model parameters. Further, the sensitivity analysis results show that the cost of bio oil predicted by the mathematical model falls in the cost interval found in the market and research literature.
In addition to reducing wildfire risks and energy dependence by collecting combustible forest biomass, the research result shows that consideration of societal aspects in bio-oil supply chains can provide a competitive cost of bio-oil. Exploration of mobile refineries is a focus here to elucidate bio-oil supply chain sustainability performance through mathematical modeling, and has not been previously reported in literature. The lack of access to the conversion processes prevented a more accurate estimation of the cost of bio-oil. To improve this limitation, modeling the parameters of bio-oil supply chains using stochastic approaches in future research would allow for a more in-depth investigation of tradeoffs between objectives.