hybrid cloud-IoT architectures are transforming healthcare by enhancing data management, improving patient outcomes, and enabling real-time decision-making. This paper presents a comprehensive review of the current state and future prospects of hybrid cloud-IoT architectures in healthcare. Our study identifies key trends, such as the increasing adoption of edge computing to reduce latency, the integration of AI for predictive analytics, and the emphasis on robust security measures to protect sensitive patient data. We address significant challenges, including data interoperability, scalability issues, and privacy concerns, and offer potential solutions and best practices. Furthermore, we provide a taxonomy-driven comparative framework evaluating five architectural paradigms edge, fog, mesh, hub-and-spoke, and serverless using healthcare-specific performance indicators such as cost, scalability, latency, security, and data confidentiality. Unlike prior studies, we synthesize existing research to produce a design recommendation. We propose a hybrid fog–mesh model that targets low latency, security, and scalability for healthcare delivery. This study links architectural theory to deployment practice. It focuses on dynamic VM selection and allocation, latency-aware computing, and data sovereignty. It also outlines key directions for future research to guide the implementation and optimization of hybrid cloud-IoT architectures in healthcare.

Integrating Working Capital Requirement (WCR) into supply chain decision-making is essential for balancing operational efficiency with financial sustainability. This study presents an Inventory Routing Problem (IRP) model tailored to healthcare supply chains, incorporating WCR considerations to optimize overall costs. By aligning inventory levels with financial considerations, our approach provides a more integrated perspective on supply chain management. The results highlight the significant impact of WCR optimization on decision-making, offering a framework for developing cost-effective and financially sustainable supply chains.

Crises such as pandemics, natural disasters, and mass casualty incidents place immense pressure on emergency medical services to ensure rapid and effective patient care. Optimized ambulance routing is essential for these operations, as it directly influences response times, patient survival rates, and the overall resilience of healthcare systems. This study proposes a hybrid approach combining the Non-Dominated Sorting Genetic Algorithm III and automated machine learning, specifically the Tree-Based Pipeline Optimization Tool, to solve the priority-oriented ambulance routing problem. The Non-Dominated Sorting Genetic Algorithm III is used to minimize treatment completion times and prioritize critically ill patients, while the Tree-Based Pipeline Optimization Tool automates the generation of predictive models for probabilistic routing factors such as patient priority and traffic conditions. By integrating the optimization capabilities of evolutionary algorithms with machine learning, the proposed method improves both operational efficiency and equitable healthcare delivery. Experimental results show that the hybrid approach outperforms traditional methods in optimizing ambulance routes and prioritizing patients effectively.

In this paper, we propose a hybrid approach for the optimal allocation of hospital beds to emergency patients, tackling the common challenge of unlabeled clinical data. Instead of relying on predefined labels, we use a rule-based labeling step to assign initial risk levels based on key medical features. These labels are then used to train several supervised learning models, helping to improve risk prediction and explainability. The predicted levels: Minimum, Moderate, or Maximum, serve as input to an optimization model that assigns patients to beds while reducing transfer cost and supporting infection control. Results show promising improvements in fairness and allocation efficiency, with a comparative study highlighting the benefits of our method.

This study addresses the critical challenge of hospital bed assignment during emergencies by introducing a two-stage combining unsupervised learning and optimization. In the first stage, clustering techniques (K-means, GMM, DBSCAN, and HDBSCAN) are employed to automatically categorize patients into different risk levels based on clinical features, eliminating the need for labeled training data. In the second stage, a linear programming model optimizes the assignment of patients to hospital beds by minimizing costs while respecting capacity limits and medical priority constraints. The proposed approach is tested and validated on instances derived from real-world datasets from Tunisian hospitals. Our proposal approach significantly reduces the data preprocessing workload while ensuring effective prioritization of critical cases across both small and large-scale scenarios. Additionally, the optimal number of clusters is determined through silhouette analysis, enhancing the clinical relevance of the patient clutering.

Efficient patient admission scheduling is a key challenge in hospital management, as it directly impacts resource utilization and the quality of care. The Patient Admission Scheduling Problem (PASP) involves assigning patients to hospital beds over a planning horizon while considering medical constraints and hospital capacity. Due to its complexity, heuristic and metaheuristic approaches are often used to find high-quality solutions within a reasonable time. In this work, we propose a Variable Neighborhood Search (VNS) metaheuristic to solve the PASP. VNS systematically explores different neighborhoods to escape local optima and improve solution quality. To assess the impact of local search strategies, we implement four versions of VNS, each using a different method for modifying patient assignments. The proposed approach is evaluated on benchmark instances, where we conduct parameter tuning and analyze computational performance. Experimental results demonstrate the effectiveness of the method, showing that the appropriate choice of local search strategies significantly impact the quality of the results.

Emergency departments (EDs) face increasing patient demand, leading to overcrowding and resource strain. Accurate forecasting of ED visits is critical for optimizing hospital operations and ensuring efficient resource allocation. This paper proposes a hybrid model combining Variational Autoencoder (VAE) and Gated Recurrent Unit (GRU) to enhance patient flow predictions. The VAE extracts meaningful latent features while handling missing data, whereas the GRU captures complex temporal dependencies, improving forecasting accuracy. Compared to traditional models such as LSTM, GRU, and 1D CNN, our hybrid VAE-GRU model demonstrates superior predictive performance. Experimental results, based on real-world hospital data, highlight the model’s effectiveness in reducing prediction errors and improving decision-making in dynamic ED environments. Additionally, we compare the proposed model with ARIMA-ML, emphasizing the tradeoffs between computational efficiency and prediction accuracy. The findings suggest that hybrid deep learning approaches can significantly enhance healthcare resource management, reducing patient waiting times and improving overall hospital efficiency.

Although supply chain optimization has traditionally focused on operational efficiency, financial dimensions often receive less attention. In particular, standard formulations of the Inventory Routing Problem (IRP) aim to minimize transportation and inventory costs, without accounting for financial flows or their impact on overall financial viability. To address this gap, we introduce the Operational Cash Requirement (OCR) as a dynamic indicator that captures the influence of financing strategies on inventory and routing decisions over time. This paper presents a new mixed-integer linear programming (MILP) formulation that integrates OCR into the IRP by explicitly modeling payment and collection delays. The model captures the interaction between physical and financial flows and extends the objective function to account for the financial resources required to support operational activities. Computational experiments on small-scale instances using an exact method demonstrate that incorporating financial constraints can significantly affect operational decisions, highlighting the importance of considering cash flow dynamics in logistics planning to better align operational and financial performance.

In this article, we present a comprehensive study regarding the problem of allocating a fleet of electric vehicles to charging stations according to charging time and battery constraints. Each charging station’s capacity as well as the necessary charging time is known in advance while each vehicle’s arrival time is provided by a GPS device. We provide an integer programming model solved with an exact method to effectively handle this combinatorial problem along with a set of metaheuristic algorithms. To evaluate the performance of this solution framework, computational experiments are conducted on large-scale randomly generated instances simulating a real-world scenario.

In recent years, there has been an increased demand for fish and seafood, highlighting the essential role of fish protein as a primary source of animal protein consumption in various regions worldwide. Seafood plays a crucial role in supporting future food security needs. However, the seafood industry generates a significant amount of waste, posing challenges in its proper management. The seafood supply chain is complex due to the involvement of multiple actors and the numerous flows between them. To address this issue, this paper proposes an efficient multi-echelon closed-loop seafood supply chain network (CLSC). This network is designed to globally oversee the seafood supply chain and its associated waste generated by its actors, showing that the waste generated by end customers is essential for creating added value to the seafood supply chain, which can be further utilized by other industries. Implementing this network helps reduce overall operational costs by introducing a novel mixed-integer linear programming (MILP) mathematical model aimed at minimizing total costs, including facility operating expenses and transportation costs. The application of this model is demonstrated through tested scenarios with various dimensions. The results of this analysis illustrate the promising outcomes of employing the proposed model.

The Ambulance Routing Problem (ARP) is a critical challenge in emergency medical services, aiming to efficiently allocate ambulances to patients. This research addresses the problem as a multi-objective optimization problem with real-world significance. The research methodology involves formulating ARP as a mathematical model, considering constraints such as patient-to-ambulance assignment, ambulance routing, and the requirement for ambulances to depart from point-of-care (PoC) locations. The NP-hard nature of ARP necessitates innovative solution approaches to handle its combinatorial complexity. This study proposes the application of the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) as a metaheuristic to tackle ARP by simultaneously minimizing total travel cost and completion time. NSGA-III is applied to optimize the formulated model, seeking a set of Pareto-optimal solutions that trade-off between total travel cost and completion time, and it is then evaluated using benchmark instances.

The patient bed assignment problem (PBAP) consists of assigning a set of patients to a set of beds over a horizon time, by considering not only patient pathologies and state conditions, but also bed availability, and medical needs. The PBAP becomes more complex when dealing with elective patients who require specific clinical needs. A solution to this problem is not trivial to find due to the multiple constraints that are considered. The problem is hence modeled as an optimization problem that aims to minimize a set of penalties related to the non-satisfied constraints. To deal with this complex problem, this paper proposes a constraint satisfaction-based approach to solve the problem, namely the min-conflict algorithm (MCA). The proposed approach is compared with different existing solution approaches to evaluate its performance. The experiments show that the proposed MCA performs better in most benchmark instances in terms of costs and computational time.

Efficient management of healthcare waste is crucial for public health and environmental sustainability. However, the dynamic nature of waste generation and transportation presents challenges in optimizing collection routes. This study proposes a novel architecture using big data analytics and a parallel Spark-Genetic Algorithm (Spark-GA) to address the dynamic healthcare waste collection and transportation vehicle routing problems (DHWVRP). The architecture integrates real-time data streams from various sources to dynamically update waste generation rates and transportation conditions. However, the real-time processing using Spark-GA can facilitate improved decision-making. The Spark-GA optimizes collection routes based on evolving data, considering factors such as waste volume, vehicle capacity, and time constraints. Results demonstrate significant improvements in route efficiency and resource utilization compared to traditional methods. This research contributes to enhancing the sustainability and effectiveness of healthcare waste management through advanced data analytics and optimization techniques. In order to evaluate the effectiveness of the suggested model, we execute our research using a real-time environment that is stimulated by injecting 10,000 messages every 0.1s and second. Results improve the performance of spark-GA processing and involve the correct function of spark through the data loading from kafka in the two cases. As a result, the performance of the suggested architecture is apparent.

The dynamic pollution routing problem poses a significant challenge in optimizing transportation routes to minimize environmental impact. This study presented a novel approach using big data analytics to address this issue effectively. Specifically, we employ a parallel Spark-Genetic Algorithm (Spark-GA) to efficiently manage a large amounts of data generated from diverse sources including traffic cameras, sensors, social media, and GPS and optimize routing decisions in real-time. Our method offers a scalable and efficient solution for dynamically adapting to changing pollution levels and traffic conditions to deal with the computational power of Spark and the optimization capabilities of genetic algorithms. We demonstrate the effectiveness and scalability of our approach in achieving improved route optimization and reduced environmental impact with experiments. Our findings highlight the potential of big data analytics in tackling complex transportation and environmental challenges, paving the way for more sustainable and efficient urban logistics systems. To evaluate the effectiveness of the suggested model, we execute our research using a dynamic environment that is stimulated by injecting 10,000 messages every 0.1s and second. Results improve the performance of spark-GA processing and involve the correct function of spark through the data loading from kafka in the two cases. As a result, the performance of the suggested architecture is apparent.

We provide a patient-centered hospital emergency allocation model in this work. Assigning each patient to the nearest hospital in accordance with resource availability restrictions is the aim of this study. We assume that necessary resource information to manage the emergency (beds and medical staff) are known in real time, and that every patient’s arrival time is supplied by a GPS device. Our contribution is to assess multiple meta-heuristic methods in order to effectively address this combinatorial problem. The goal of the Hospital Patient Allocation Problem (HPA) is to reduce the overall amount of time needed for patient emergency care. Computational experiments are carried out on massively simulated cases to evaluate the effectiveness of the investigated methodologies.

This study focuses on the issue of assigning hospital beds to patients based on their medical condition within a specific length of stay. To handle this problem a two-stage approach combines machine learning (ML) techniques and optimization is proposed. The first level seeks to classify patients into three categories based on their contagious states by considering various features. The second one aims to minimize the cost of assigning patients to specific beds according to their states under a set of constraints (capacity constraints, transfer constraints, etc). The problem is formulated as a linear programming to increase the patient admission rate during emergencies. The proposed methodology is tested using a set of instances based on real data taken from Tunisian Hospitals. Experimental results illustrate the effectiveness of this approach, the best model is chosen based on the highest accuracy, specificity, and sensitivity.

Efficient inventory management (IM) presents an important key driver for supply chain (SC) sustainability. This latter becomes a crucial concern for decision-makers and managers in all domains, particularly in the matter of sensitive areas that affect human well-being, namely the pharmaceutical industry. Medicines IM for a sustainable Pharmaceutical Supply Chain (PSC) brought further particularities compared to the traditional SCs. Besides the economic preoccupation, social and environmental issues might be considered. In this work, we assess the impact of the Lateral Transshipment (LT) strategy on the sustainability of the IM process. We compare the total costs of two cases, IM with and without LT strategy. We propose an IM model that seeks the optimal replenishment order quantity of multiple types of products and the shipment time in a two-echelon PSC under a centralized setting. The considered PSC consists of a pharmaceutical company (PC), a Pharma-distributor (PD), and multiple hospitals. The mathematical model takes into account the transportation costs including LT costs -in the case when LT is included- as well as shortage, and products with high deterioration rate costs. We attempt to minimize unused medicines leftover by minimizing the deterioration rate of products at both distributor and hospital sites.

In this paper, we present a charging station allocation model for electric vehicles. The goal is to assign each electric vehicle to the closest charging station with respect to capacity and charging time constraints. We assume that each vehicle's arrival time is provided by a GPS device and each charging station capacity as well as the required charging time are known in advance. We propose an integer programming model solved with CPLEX to efficiently deal with this combinatorial problem. The objective of Electric Vehicles Charging Stations Allocation (EVCSA) is to minimize the total required time from a start point to a destination going through a Charging Station (CS). To evaluate the performance of the proposed approach, computational experiments are conducted on large scale randomly generated instances simulating a real world scenario.

The rise of terrorism over the past decade did not only hinder the development of some countries, but also it continues to destroy humanity. To face this concept of an emerging crisis, every country and every citizen is responsible for the fight against terrorism. As conventional plans became useless against terrorism, governments are required to establish innovative concepts and technologies to support units in this asymmetric war. In this paper, we propose a new multi-agent model for counter-terrorism characterized by a methodical process and a flexibility to handle different contingency scenarios. The division of labour in our multi-agent model improves decision making and the structuring of organisational plans.

We address, in this paper, a very recurring problem within hospitals that consists in assigning elective patients to a limited number of beds. Especially when dealing with patients requiring urgent intervention, this problem becomes more complex and the time factor becomes the most critical. In such situations, a set of patients are to be examined and their clinical states are to be well specified in order to decide whether they need admission and hospitalization or not. In case of hospitalization, the hospital staff should assign patients to beds while taking into account beds availability in terms of specialization and patient needs. All these actions should be well planned in order to maximize the quality of service in the hospitals. This challenging problem can be modeled as an assignment problem that handles a set of patients to be assigned to a set of beds over a given time horizon, while taking into account availability constraints expressed in terms of beds, medical necessity and patient demands. Due to its NP-hardness, the problem is mainly solved using approximate approaches, especially for large-scaled instances. We propose a hybrid simulated annealing approach, combining both advantages of simulated annealing (SA), that provides a local search, and genetic algorithm, that provides a global search, to enhance the performance of SA. The experimental results show that the proposed metaheuristic generates high-quality solutions for several benchmark instances from the literature with regards to the basic simulated annealing approach.

Accurate and quick treatment of patients is the most important aim of the health care systems, especially at emergency departments (ED), as such departments are dealing with life and death situations on a daily basis. Nevertheless, the patients scheduling problem (PSP) is often very difficult to solve in practice particularly by applying manual approaches. To this end, the target of this paper is to study the PSP in ED. We propose a hybrid ILS/VND algorithm that aims to minimize the total waiting time of patient’s. After generating the initial solution using the "Triage - First in First out" (TFF) heuristic, the solution is further improved by employing a VND algorithm. The proposed VND involves four neighborhood structures in order to disrupt the actual solution and provide a better exploration of the search space. The obtained results show that the hybrid ILS/VND algorithm is computationally effective and provides high-quality solutions.

Health organizations are complex to manage due to their dynamic processes and distributed hospital organization. It is therefore necessary for healthcare institutions to focus on this issue to deal with patients’ requirements. Preparing a schedule for patients in the emergency department is a complex task, which requires taking into account numerous rules, related to various aspects: respect the triage process (emergency degrees of patients), respect the availability of resources, etc. In this paper, we present a mixed integer linear programming (MILP) approach to facilitate this task. The objective is to minimize the total waiting time of patient’s in the emergency department. We consider simultaneously four patients’ process: registration and triage, consultation, treatment and hospitalization. The model is characterized by the availability of both human (triage staff, physician, nurse) and material resources (bed) in each process through the stay of patient in the ED except for triage and registration which does not require a bed. To solve this model, we used the commercial solver IBM ILOG CPLEX Optimization Studio. The program has been tested on a set of instances. Numerical results show that the proposed approach can significantly improve the efficiency of emergency department by reducing the total waiting time of patients.

We introduce and study the problem of obtaining a spatial or temporal configuration that maximizes the number of constraints satisfied in a qualitative constraint network (QCN). We call this problem the MAX-QCN problem and prove that it is NP-hard for most of the qualitative calculi. We also propose a complete generic branch and bound algorithm for solving the MAX-QCN problem. This algorithm builds on techniques used in the literature for solving the consistency checking problem and the minimal labeling problem of a given QCN. In particular, we make use of a tractable subclass of relations, a chordal graph provided by a triangulation of the input QCN, and the partial weak composition as a filtering method. The experimentation that we have conducted with QCNs from the Interval Algebra and the Region Connection Calculus shows the interest of our proposed algorithm.