Bahram Alidaee

Bahram Alidaee, Haibo Wang, Lutfu Sua et al.

Numerous established machine learning models and various neural network architectures can be restructured as Quadratic Unconstrained Binary Optimization (QUBO) problems. A significant challenge in Adiabatic Quantum Machine Learning (AQML) is the computational demand of the training phase. To mitigate this, approximation techniques inspired by quantum annealing, like Simulated Annealing and Multiple Start Tabu Search (MSTS), have been employed to expedite QUBO-based AQML training. This paper introduces a novel hybrid algorithm that incorporates an "r-flip" strategy. This strategy is aimed at solving large-scale QUBO problems more effectively, offering better solution quality and lower computational costs compared to existing MSTS methods. The r-flip approach has practical applications in diverse fields, including cross-docking, supply chain management, machine scheduling, and fraud detection. The paper details extensive computational experiments comparing this r-flip enhanced hybrid heuristic against a standard MSTS approach. These tests utilize both standard benchmark problems and three particularly large QUBO instances. The results indicate that the r-flip enhanced method consistently produces high-quality solutions efficiently, operating within practical time constraints.

Haibo Wang, Jun Huang, Lutfu Sua et al.

The increasing focus on predicting renewable energy production aligns with advancements in deep learning (DL). The inherent variability of renewable sources and the complexity of prediction methods require robust approaches, such as DL models, in the renewable energy sector. DL models are preferred over traditional machine learning (ML) because they capture complex, nonlinear relationships in renewable energy datasets. This study examines key factors influencing DL technique accuracy, including sampling and hyperparameter optimization, by comparing various methods and training and test ratios within a DL framework. Seven machine learning methods, LSTM, Stacked LSTM, CNN, CNN-LSTM, DNN, Time-Distributed MLP (TD-MLP), and Autoencoder (AE), are evaluated using a dataset combining weather and photovoltaic power output data from 12 locations. Regularization techniques such as early stopping, neuron dropout, L1 and L2 regularization are applied to address overfitting. The results demonstrate that the combination of early stopping, dropout, and L1 regularization provides the best performance to reduce overfitting in the CNN and TD-MLP models with larger training set, while the combination of early stopping, dropout, and L2 regularization is the most effective to reduce the overfitting in CNN-LSTM and AE models with smaller training set.

Haibo Wang, Lutfu S. Sua, Bahram Alidaee

The increasing scale and complexity of global supply chains have led to new challenges spanning various fields, such as supply chain disruptions due to long waiting lines at the ports, material shortages, and inflation. Coupled with the size of supply chains and the availability of vast amounts of data, efforts towards tackling such challenges have led to an increasing interest in applying machine learning methods in many aspects of supply chains. Unlike other solutions, ML techniques, including Random Forest, XGBoost, LightGBM, and Neural Networks, make predictions and approximate optimal solutions faster. This paper presents an automated ML framework to enhance supply chain security by detecting fraudulent activities, predicting maintenance needs, and forecasting material backorders. Using datasets of varying sizes, results show that fraud detection achieves an 88% accuracy rate using sampling methods, machine failure prediction reaches 93.4% accuracy, and material backorder prediction achieves 89.3% accuracy. Hyperparameter tuning significantly improved the performance of these models, with certain supervised techniques like XGBoost and LightGBM reaching up to 100% precision. This research contributes to supply chain security by streamlining data preprocessing, feature selection, model optimization, and inference deployment, addressing critical challenges and boosting operational efficiency.

Bahram Alidaee, Haibo Wang, Hugh Sloan

Graph theory has been a powerful tool in solving difficult and complex problems arising in all disciplines. In particular, graph matching is a classical problem in pattern analysis with enormous applications. Many graph problems have been formulated as a mathematical program and then solved using exact, heuristic, and/or approximated-guaranteed procedures. On the other hand, graph theory has been a powerful tool in visualizing and understanding complex mathematical programming problems, especially integer programs. Formulating a graph problem as a natural integer program (IP) is often a challenging task. However, an IP formulation of the problem has many advantages. Several researchers have noted the need for natural IP formulation of graph theoretic problems. The present study aims to provide a unified framework for IP formulation of graph-matching problems. Although there are many surveys on graph matching problems, none is concerned with IP formulation. This paper is the first to provide a comprehensive IP formulation for such problems. The framework includes a variety of graph optimization problems in the literature. While these problems have been studied by different research communities, however, the framework presented here helps to bring efforts from different disciplines to tackle such diverse and complex problems. We hope the present study can significantly help to simplify some of the difficult problems arising in practice, especially in pattern analysis.