Center for Excellence in Logistics and Distribution (CELDi) Proposal #1127937
This proposal seeks funding for the Center for Excellence in Logistics and Distribution (CELDi) located at the University of Missouri-Columbia.Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 10-601. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research.
Sustainable energy sources are a critical component of our nations future and security. One critical aspect of realizing the potential of biomass energy is the logistics component. To date biomass energy is not being realized due to the high costs of biomass logistics systems. This proposal will address this problem and allow for the development of a multi-stage biomass supply chain and logistics model that addresses uncertainties, such as those in the biomass availability, process performance, and final product demand / price to name a few. The model will also support dynamic decision making and address a broad mix of inherent problems in biomass logistics. Using the results of the research, alternative scenarios will be evaluated and decision support tools will be developed.
If this project is successful then biomass based sources would become viable and biomass based energy sources would reduce carbon emissions, lead to rural economic development, reduce dependence on foreign sources of energy, increase energy security, increase the likelihood for cost effective energy, and influence policy. Participation by students, including continued recruitment of students from underrepresented groups, will be emphasized by the PI. The project provides benefits for CELDi by positioning both the university members and industry partners in a leadership position for biomass based energy. In addition, the industry partners stand to benefit from an early view of the results that will allow them to develop and implement biomass based energy sources and develop possible market strategies and products.
Sustainable energy sources are a critical component of our nationâ€™s future and security. One key aspect of realizing the potential of biomass energy is the logistics component. To date biomass energy is not being realized due to conversion costs and the high costs of biomass logistics systems. This project addresses biomass logistics by developing a multi-stage biomass supply chain and logistics model that addresses uncertainties, such as those in the biomass availability, process performance, and final product demand / price to name a few. The model also supports dynamic decision making and addresses a broad mix of inherent problems in biomass logistics. Using the results of the research, alternative scenarios are evaluated and decision support tools are developed. For this project the literature was reviewed and assessed and biomass logistics data was compiled. Based on this analysis and an analysis of the problems inherent in biomass logistics we developed the first stage strategic logistics model and the second stage operational logistics model for biomass supply chains. These models together provide a complete solution for biomass supply chains. The main objective of the strategic model is to find the best location allocation plan for the facilities in the system. This goal was achieved through two major steps; first suitability analysis was applied to determine the candidate locations. Once these were determined a mixed integer linear programming model was developed and solved to find the optimal solution for the locations. Case studies in three different areas in Missouri were conducted with the consideration of different settings of parameters. The results show that factors such as the capacity of the pretreatment facility, transportation cost and the capital cost of harvesting would affect the decisions significantly. The operational logistics model has specific objectives such as determining optimal assignment plans of harvesting teams at the beginning of each period and allocating biomass flows optimally between levels, so that the total operational cost of harvesting, transportation, preprocessing, storage and conversion over a finite planning horizon is minimized. The model considers many complicated but practical factors such as seasonality in supply and demand, cutting cycle and biomass deterioration. A dynamic programming model was developed, where the location of harvesting team, forest recovery time and storage level are used to denote the state of the system. However, the state space of this dynamic program is quite large rendering it impossible to solve directly. As a result, a mixed integer linear programming model was proposed by substituting the decision variables and state variables. After working with our partners, we developed meaningful case scenarios with different harvesting systems to evaluate the performance of the model and solution approach. The results show that a high-productivity harvesting system can be more cost efficient than a low productivity system in small areas where reallocation cost does not contribute much to the total cost. In addition, the results also indicate that factors such as geographical distribution of forests, measures of distance and the structure of the transportation network affect the reallocation of harvesting teams. GIS was implemented to improve the accuracy of the case study. Real transportation networks were used so that road-network distance could be calculated. GIS also provided explicit routes on the real road network with detailed directions. In addition to the research findings, this project significantly contributed to the development and training of the researchers and students. Those involved in this research developed a broad understanding of energy and its different forms and its impact on organizations as well as the power and benefits of logistics systems. For the industrial engineering profession, this effort gave us a significant foothold in the energy sector. As this research broadens to include social, policy, economic, and development issues, we are learning diverse knowledge that will be a foundation of future research work. The project also generated industry case studies that are used in related classes. This incorporation of research and real world results in undergraduate as well as graduate curriculum allows for a broad educational impact. Lastly, we have communicated and worked with a number of biomass related entities to learn practical operations, to test different concepts, get feedback, and to disseminate our work. In particular, the MU power plant, which produces 32% of its energy from biomass, helped us develop a case scenario. We have also talked to experts and farmers to better understand their involvement with the energy crop production and to share what we learned through the project. In addition to the organizations that have directly collaborated, we have talked to several member companies of CELDi (Center for Excellence in Logistics and Distribution), including energy providers and logistics providers, to share our results. The results of our project were also disseminated through multiple journal and conference papers and presentations.