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

Date
2023-05-09
Authors
Keith, Kolton
Castillo-Villar, Krystel K.
Alaeddini, Adel
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery
Abstract

Bio 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.

Description
Keywords
mixed integer linear programming, supply chain design, biofuels
Citation
Keith, 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
Department
Mechanical Engineering