A1 Journal article (refereed), original research (Journal article, original research)

A Hybrid Methodology Based on Machine Learning for a Supply Chain Optimization Problem

Open Access publication

Publication Details

Authors: Duc Duy Nguyen, Nananukul Narameth

Publisher: IOP Publishing: Conference Series

Publication year: 2020

Language: English

Related journal or series: Journal of Physics: Conference Series

Volume number: 1624

ISSN: 1742-6588

eISSN: 1742-6596

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1088/1742-6596/1624/5/052022

Open Access: Open Access publication


This paper presents an advanced methodology that integrates a machine learning methodology into an optimization process. The framework of an interactive machine learning algorithm was developed to meet the challenges in solving large-scale optimization problems. An artificial neural network (ANN) is used with the knowledge gained from solving previous problems with different scenarios to define a good starting point for a solution searching process. By using an initial solution, known as “warm start”, the search space can be reduced to get more opportunity to find an optimal solution. The applicability of the proposed method was evaluated by using it to determine the optimal facility locations for a biomass supply chain problem using a real case study from Central Vietnam. The supply chain planning model is based on an optimization model, where the goal is to maximize the benefits from meeting the electricity demand minus the total cost from facility cost, penalty cost from lost demand, and operational costs form the supply chain. The structure of the ANN, the number of intermediate layers and the number of processing nodes, was determined by comparing the accuracy from different configurations. The ANN with two intermediate layers possesses the best performance from the training and testing datasets. The proposed model succeeded in predicting the facility location with more than 98% prediction accuracy. The results from our framework provide optimal solutions while saving runtime.

Last updated on 2021-07-12 at 08:48