Khaled Elbassioni

Efficiently Computed Periodic Ordering Heuristics in Stochastic Distribution Systems The difficulty of analyzing and optimizing the stochastic one-warehouse multiretailer problem under the (S,T) policy motivates the need to consider approximate but high-fidelity models that are easier to scrutinize. In “Heuristic (S,T) Solutions via an FPTAS for a One-Warehouse Multiretailer Problem,” Najy, Elbassioni, and Diabat show how to efficiently compute competitive solutions for such systems based on an approximate formulation of the inventory problem proposed by Chu and Shen (2010) and build on the assumption of power-of-two (POT) policies. The authors first devise a fully polynomial-time approximation scheme for the continuous relaxation of the model and then show how to round it to a POT solution within an improved approximation factor relative to the present literature. These solutions are shown via simulation to be highly competitive with optimal (S,T) solutions and are derived in a tiny fraction of the time needed by the current best (S,T) algorithms.

We study the social welfare maximization problem in the high-multiplicity setting where agents and/or items are available in multiple types, provided that the numbers of types are small. We focus on the egalitarian and Nash social welfare maximization problems, and show that they are NP-hard even when the number of item types is a constant. Furthermore, we present two polynomial-time approximation schemes (PTAS), one for egalitarian social welfare with two item types, and one for Nash social welfare with any constant number of agent types. The first PTAS can be applied to the unrelated machine scheduling problem, thus partially solving an open question raised by Jansen and Maack in 2019. The second PTAS significantly improves upon the existing PTAS for identical agents.

In modern power systems with increasing renewable energy integration, accurate estimation of time-varying inertia is critical for ensuring grid stability and resilience. This paper proposes an innovative ensemble of dual-stage hybrid deep learning methods that leverage the strengths of different hybrid networks. The goal is to provide computationally efficient, accurate, and robust inertia estimation in real-time under ambient, dynamic, and challenging operating conditions. The ensemble model comprises a fusion of hybrids of long short-term memory and multilayer perceptron (LSTM-MLP), convolutional-LSTM (ConvLSTM), and temporal convolutional network-LSTM (TCN-LSTM). First, the proposed model is trained and evaluated against cutting-edge methods, using a modified IEEE 39-bus system's data comprising electric power (PE), photovoltaic (PV) power, mechanical power (PM), and rate-of-change-of-frequency (RoCoF). The results showcase the root mean squared error (RMSE) of the proposed method achieves 0.027411, outperforming the above individual methods in inertia prediction by 21.86%, 61.81%, 46.03% respectively. Second, we evaluate the model resilience to unavailability of individual input variables and noise in input variables, achieving low average RMSE values of 0.068782 and 0.044712, respectively, for PM and PE inputs. Additionally, the proposed method demonstrates effective generalization in estimating inertia on new data with entirely different distributions from training data. The results also highlight high sensitivity of RoCoF and PV input to noise, thus providing valuable insights for optimizing model performance in real-world applications. Finally, the approach is further validated on a modified IEEE 68-bus system, achieving significant accuracy and robustness, highlighting its capability to handle larger systems and enhancing its practical applicability.

Algorithmic matching is a pervasive mechanism in our social lives and is becoming a major medium through which people find romantic partners and potential spouses. However, romantic matching markets pose a principal-agent problem with the potential for moral hazard. The agent’s (or system’s) interest is to maximize the use of the matching website, while the principal’s (or user’s) interest is to find the best possible match. This creates a conflict of interest: the optimal matching of users may not be aligned with the platform’s goal of maximizing engagement, as it could lead to long-term relationships and fewer users using the site over time. Here, we borrow the notion of price of anarchy from game theory to quantify the decrease in social efficiency of online algorithmic matching sites where engagement is in tension with user utility. We derive theoretical bounds on the price of anarchy and show that it can be bounded by a constant that does not depend on the number of users in the system. This suggests that as online matching sites grow, their potential benefits scale up without sacrificing social efficiency. Further, we conducted experiments with human subjects in a matching market and compared the social welfare achieved by an optimal matching service against a self-interested matching algorithm. We show that introducing competition among matching sites aligns the self-interested behavior of platform designers with their users and increases social efficiency.