A Neural Network Powered Solution Approach for Computationally Expensive Mixed Integer Programs for Bio Jet-fuel Supply Chain Network Design

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dc.contributor.authorKeith, Kolton
dc.contributor.authorCastillo-Villar, Krystel K.
dc.contributor.authorAlaeddini, Adel
dc.date.accessioned2023-11-27T17:25:01Z
dc.date.available2023-11-27T17:25:01Z
dc.date.issued2023-05-09
dc.description.abstractBio jet fuels derived from feedstock offer a sustainable alternative to meeting energy needs. Modeling supply chains that produce said fuels can lead to computationally prohibitive mixed integer linear programs (MILP) that consider optimal facility location and materials routing. The present work proposes an iterative machine learning-based hybrid solution procedure that transfers some of the responsibility of facility location to the learner. First, given a random selection of facility locations, a collection of model solutions is generated. Next, a neural network is fit to the collection of solutions, with facility locations being the input and total supply chain (SC) costs as the output. Then, the next set of locations is selected to optimize the predicted output of the neural network. Finally, the MILP optimization model is called to test the selected locations, and the results are fed back into the neural network and the process is repeated. Numerical experimentation demonstrates the proposed solution procedures yield near-optimal solutions with 0.10-0.18% increase in objective function value alongside a 40-65% reduction in computational time.
dc.description.departmentMechanical Engineering
dc.description.sponsorshipThis work was partially supported by the National Institute of Food and Agriculture (NIFA), U.S. Department of Agriculture (USDA), under the Hispanic Serving Institutions Education Grants Program, award no. 2020-38422-32258. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office Award Number DE-EE0009046. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
dc.identifier.citationKeith, K., Castillo-Villar, K. K., & Alaeddini, A. (2023). A neural network powered solution approach for computationally expensive mixed integer programs for bio jet-fuel supply chain network design. Paper presented at Cyber-Physical Systems and Internet of Things Week 2023, San Antonio, TX, USA. https://doi.org/10.1145/3576914.3588336
dc.identifier.isbn979-8-4007-0049-1
dc.identifier.otherhttps://doi.org/10.1145/3576914.3588336
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2244
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery
dc.rightsAttribution 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectmixed integer linear programming
dc.subjectsupply chain design
dc.subjectbiofuels
dc.titleA Neural Network Powered Solution Approach for Computationally Expensive Mixed Integer Programs for Bio Jet-fuel Supply Chain Network Design
dc.typeArticle

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