Cloud computing's rapid growth, driven by escalating global data traffic, presents challenges in energy efficiency due to the underutilization of servers. We propose a cloud sharing system through a mixed integer bilevel optimization model under demand uncertainty. This model features two distinct types of customers in the public cloud, namely, long-term consumers with monthly subscriptions and short-term consumers who want on-demand access.
In this model, pricing strategies at the upper level dynamically adjust to incentivize short-term consumers to utilize virtual machines (VMs) during periods of low-energy prices. Additionally, these strategies incorporate rewards for long-term consumers, encouraging them to share their VMs in idle periods.
We consider a Distributionally Robust Optimization (DRO) approach to address uncertainties in the consumer bids, enhancing decision-making resilience against variability in willingness to pay.

Electric vehicles are one of the promising technologies that are being used in the transportation sector to help reduce the negative impacts associated with traditional technologies. In order to integrate electric vehicles into public transportation, this study focuses on the examination of how energy uncertainty in electric battery consumption impacts operating costs associated with charging decisions. This research formulates the energy uncertainty challenge as a two-stage stochastic programming model by employing mixed-integer linear programming. The model optimizes bus route assignment and charging decisions such as when to charge, while accounting for the potential deviations in energy requirements that may require a recourse action in certain scenarios. The results establish a correlation between the level of uncertainty in consumption and the difficulty to solve the problem (i.e., CPU time). This research contributes to the realm of electric vehicle fleet management by providing a comprehensive methodology to optimize bus route assignments and charging protocols considering the real-world variability in energy consumption.

We present a bilevel optimization model to support decision-making about locating, sizing and pricing of electric vehicle (EV) charging stations by taking into account the behaviour of EV users. This is done by adopting a preference-list or rank-based approach that characterizes users by a set of distinct ordered sets of predefined preferences or products. In the upper-level, the charging service provider is in charge of making decisions on the location, size and pricing of the charging stations to maximize its profit. In the lower level, EV users select their first available charging station from their preference list. We solve the bilevel optimization problem using a KKT-based single-level reformulation and provide extensive managerial insights on randomly generated set of problem instances.