|Kim, Sojung - Texas A&M University|
|Kim, Sumin - Oak Ridge Institute For Science And Education (ORISE)|
Submitted to: Simulation Modelling Practice and Theory
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/8/2018
Publication Date: 5/9/2018
Citation: Kim, S., Kim, S., Kiniry, J.R. 2018. Two-phase simulation-based location-allocation optimization of biomass storage distribution. Simulation Modelling Practice and Theory. 86:155-168. https://doi.org/10.1016/j.simpat.2018.05.006.
DOI: https://doi.org/10.1016/j.simpat.2018.05.006 Interpretive Summary: This study presents a two-step framework for computer modeling of optimal locations to store biomass for biofuel. Location of storage facilities is critical for the whole bioenergy process. The proposed framework has two computer simulation phases: (1) predicting switchgrass production with the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model and (2) estimating transportation costs of the biofuel using a computer model linked to the geographic information system (GIS). The second model finds the best locations of biomass storage. The proposed approach simulates plant production and transportation and storage aspects. It provides reliable locations for storing biomass to improve the bioenergy production system.
Technical Abstract: This study presents a two-phase simulation-based framework for finding the optimal locations of biomass storage facilities that is a very critical link on the biomass supply chain, which can help to solve biorefinery concerns (e.g. steady supply, uniform feedstock properties, stable feedstock costs, and low transportation cost). The proposed framework consists of two simulation phases: (1) crop yield estimation using a process-based model such as Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) and (2) biomass transportation cost estimation using agent-based simulation (ABS) such as AnyLogic® with geographic information system (GIS). The OptQuest® in AnyLogic is used as an optimization engine to find the best locations of biomass storage facilities based on evaluation results given by the two-phase simulation framework. In addition, network partitioning and integer linear programming techniques are used to mitigate computation demand of the optimization problem. Since the proposed hybrid simulation approach utilizes realistic biofuel feedstock production and considers dynamics of supply chain activities, it is able to provide reliable locations of biomass storage facilities for operational excellence of a biomass supply chain.