Mahdi Khemakhem

The Examination Timetabling Problem (ETP) is an NP-hard combinatorial optimization problem that has been extensively studied over the past decades, with methodologies ranging from classical heuristics to contemporary machine learning approaches. This paper provides a comprehensive literature review and proposes a novel two-stage genetic algorithm based decomposition combined with a clear linear mathematical formulation. A unique contribution of this work is addressing the practical complexity of two-separated examination facilities at PSAU, where male and female students study in completely separate buildings with independent examination rooms, capacities, and supervisory staff, yet must follow a synchronized examination schedule. This creates a coupled dual-ETP problem where two independent resource allocation problems must share identical time slot assignments, significantly increasing model complexity and constraint interactions. The proposed algorithm decomposes the problem into two sequential stages, time slot assignment and room assignment to effectively reduce computational complexity while maintaining solution quality. Additionally, a systematic comparison with Mixed-Integer Programming (MIP) formulation is provided to establish the trade-offs between exact and metaheuristic approaches. The algorithm is further benchmarked against simulated annealing and tabu search baselines to provide broader methodological context, demonstrating its advantage on coupled dual-resource instances. The algorithm shows particular strength in scalability, successfully solving large instances where MIP becomes computationally intractable, while maintaining competitive solution quality.

The multiperiodic crowd tracking (MPCT) problem is an extension of the periodic crowd tracking (PCT) problem, recently addressed in the literature and solved using an iterative solver called PCTs solver. For a given crowded event, the MPCT consists of follow-up crowds, using unmanned aerial vehicles (UAVs) during different periods in a life-cycle of an open crowded area (OCA). Our main motivation is to remedy an important limitation of the PCTs solver called PCTs solver myopia which is, in certain cases, unable to manage the fleet of UAVs to cover all the periods of a given OCA life-cycle during a crowded event. The behavior of crowds can be predicted using machine learning techniques. Based on this assumption, we proposed a new mixed integer linear programming (MILP) model, called MILP-MPCT, to solve the MPCT. The MILP-MPCT was designed using linear programming technique to build two objective functions that minimize the total time and energy consumed by UAVs under a set of constraints related to the MPCT problem. In order to validate the MILP-MPCT, we simulated it using IBM-ILOG-CPLEX optimization framework. Thanks to the clairvoyance of the proposed MILP-MPCT model, experimental investigations show that the MILP-MPCT model provides strategic moves of UAVs between charging stations (CSs) and crowds to provide better solutions than those reported in the literature.

Summary: Recently, the virtual data center embedding (VDCE) problem has drawn significant attention because of a growing need for efficient means of data center resource allocation. By ensuring a set of virtual data center (VDC) integration requests coming from his customers, among the main concern of an infrastructure provider is the maximization of the utilization rate of data center resources and benefits. However, existing VDCE solutions mostly focus on consolidating virtual machines in a single physical data center. Therefore, in this work, we improve the consolidated targets techniques, that consider only the virtual machines integration, by the consideration of network devices and fabrics (e.g., switches and paths/links). We consider new unreleased constraints such as multiple virtual nodes of the same request co‐location, and intermediate node requirements when a virtual link is mapped. To address the above problem, in this paper, we propose a binary linear programming‐based model, called BLP‐VDCE, to solve the VDCE problem with network‐aware consideration. This model ensures a simultaneous consolidated embedding of virtual nodes and virtual links. Extensive simulations show that solving the proposed BLP‐VDCE model can efficiently embed VDC requests with a high physical resource utilization rate.

Crowd management and analysis (CMA) systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles (UAVs) use. Crowd tracking using UAVs is among the most important services provided by a CMA. In this paper, we studied the periodic crowd-tracking (PCT) problem. It consists in using UAVs to follow-up crowds, during the life-cycle of an open crowded area (OCA). Two criteria were considered for this purpose. The first is related to the CMA initial investment, while the second is to guarantee the quality of service (QoS). The existing works focus on very specified assumptions that are highly committed to CMAs applications context. This study outlined a new binary linear programming (BLP) model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of abstraction. To closely approach different real-world contexts, we carefully defined and investigated a set of parameters related to the OCA characteristics, behaviors, and the CMA initial infrastructure investment (e.g., UAVs, charging stations (CSs)). In order to periodically update the UAVs/crowds and UAVs/CSs assignments, the proposed BLP was integrated into a linear algorithm called PCTs solver. Our main objective was to study the PCT problem from both theoretical and numerical viewpoints. To prove the PCTs solver effectiveness, we generated a diversified set of PCTs instances with different scenarios for simulation purposes. The empirical results analysis enabled us to validate the BLP model and the PCTs solver, and to point out a set of new challenges for future research directions.

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.