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A novel computational approach to solve complex optimization problems involving multiple stakeholders in biomass feedstock production

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1008487.(doi:10.13031/2013.29627)
Authors:   Yogendra Shastri, Alan Hansen, Luis Rodriguez, K.C Ting
Keywords:   Biomass feedstock, optimization, computation, agent-based modeling, decomposition

Biomass feedstock production is an important component of the biomass based energy sector. Seasonal and distributed collection of low energy density material creates unique challenges, and optimization of the complete value chain is critical for cost-competitiveness. BioFeed is a mixed integer linear programming (MILP) model that has been developed and successfully applied to optimize biomass feedstock production of bioenergy crops. It integrates the individual farm design and operating decision with the transportation logistics issues to analyze them as a single system. However, this integration leads to a model that is computationally demanding, leading to large simulation times for simplified case studies. Given these challenges, and in wake of the future model extensions, this work proposes a new computational approach that reduces computational demand, maintains result accuracy, provides modeling flexibility and enables future model enhancements. The new approach, named the Decomposition and Distributed Computing (DDC) approach, first decomposes the model into two separate optimization models: a production model, focusing on on-farm activities such as harvesting, and a provision model, incorporating the post-production activities such as transportation logistics. An iterative scheme based on the concepts from agent based modeling is adapted to solve the production and provision problems iteratively until convergence has been reached. The computational features of the approach are further enhanced by enabling distributed computing of the individual farm optimization models. Simulation studies comparing the performance of the DDC approach with the rigorous MILP solution approach illustrate an order of magnitude reduction in computational time using the proposed DDC approach. Moreover, the solution obtained using the DDC approach is within 5% of the MILP solution. This approach can be a valuable tool to solve complex supply chain optimization problems in other sectors where similar challenges are encountered.

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