Quang-Vinh Dang

Classification is widely used to categorize instances and predict their membership of new instances in various learning applications. However, class imbalance is a common issue in many real‐world situations, where class distributions are not uniform. As traditional classification algorithms have been designed to handle balanced classification problems, class imbalance is one of the most significant challenges in modern learning tasks, resulting in degrading classification performance and increasing learning difficulty. Additionally, class overlap occurs when instances from multiple heterogeneous classes share a common feature space. Both class imbalance and class overlap problems are significant challenges in many practical learning tasks, since they may adversely affect the learning performances as well as increase the learning difficulty. Thus, in this study, to enhance the classification performance of imbalanced data with class overlap, a new hybrid resampling method based on relative class impurity, called CIHR, is proposed. In particular, the relative class impurity measure is considered an indicator of class overlap degree for adaptively applying oversampling and undersampling. The comprehensive experimental analysis considering several class imbalanced datasets and classification models demonstrates that the proposed method improves the classification performance as well as effectively mitigates the class imbalance and overlap problems.

The electricity network has reached its transport capacity limits in various areas in the Netherlands and the challenge of granting connections became critical. The concept of energy hubs, where neighboring prosumers collaborate to optimize the available capacity, poses itself as a short-term alternative for grid reinforcement. This study presents a coordinated, MPC-based combined with the partheno-genetic algorithm, optimization approach enabling a smooth transition into an electric fleet for logistics companies, taking part of an energy hub, while respecting the grid's limited capacity and taking into account uncertainties arising from load and generation profiles. The last-mile deliveries of the logistic company are depicted by the Electric Vehicle Routing Problem formulation, and the partheno-genetic algorithm is implemented to solve it, where the parameters of interest are fed to the energy management system that minimizes the overall energy costs at the energy hub while incorporating day-ahead market prices. The intermittency of renewable generation and load demand is tackled by adopting the model predictive control (MPC) framework, providing a corrective mechanism that ensures that the overarching objective of the system is met while respecting the grid's limitation. Three charging strategies at the hub are investigated: dynamic charging, where charging power varies by magnitude and time, direct charging, having a fixed charging power and time, and delayed overnight charging, where the charging power is spread over a scheduled horizon. The results demonstrate the mitigation of the grid's connection capacity while attaining cost savings with the proposed dynamic charging strategy, ranging between 11.5% and 52% compared to the delayed overnight charging and direct charging strategies.

Brainport Industries Campus (BIC) is a unique initiative taken by the high-tech suppliers of the Eindhoven region to come together and work towards collaborative consumption of shared resources such as production machines, automated guided vehicles, clean rooms and storage spaces in order to jointly meet their customers’ demands, reduce cost of ownership and better manage risks during uncertain demand and supply cycles. However, the matching-demand-with-supply process in a sharing economy requires novel perspectives and tools. In this chapter, we propose a digital platform for serving multiple tenants of BIC sharing a fleet of heterogeneous automated guided vehicles (AGVs). The proposed platform includes a user interface as an order input system, an order database and a cloud server that houses the necessary intelligence for communicating with all the aforementioned modules. This platform that serves multiple tenants sharing a heterogeneous AGV fleet is the first of its kind.