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.

The postal sector is essential in enhancing services for businesses and individuals by establishing a reliable communication network that ensures the efficient collection, transfer, and global delivery of mail, funds, and parcels. Consequently, optimizing routing and packing systems for the collection and transportation of letters and parcels is a critical component of an effective delivery management system. Traditionally, postal distribution challenges are modeled as Capacitated Vehicle Routing Problems (CVRP). To account for parcel dimensions, this paper introduces the Capacitated Vehicle Routing Problems with Two-Dimensional Loading Constraints (2L-CVRP). The problem involves designing routes that originate and terminate at a central depot, using a fleet of identical vehicles to meet demands at multiple locations. Furthermore, items transported in each vehicle must comply with two-dimensional orthogonal packing constraints, with the primary objective of minimizing total transportation costs. Given the NP-hard complexity of this problem, the paper proposes a hybrid metaheuristic that integrates Variable Neighborhood Search (VNS) with a Genetic Algorithm (GA) to improve GA’s convergence toward high-quality solutions. This hybrid method capitalizes on GA’s exploratory strengths and VNS’s ability to effectively exploit the solution space. VNS is incorporated into GA’s mutation operator, broadening and diversifying the solution space, thereby enabling the hybrid algorithm to explore new regions within the search space more effectively. We design a Decision Support System (DSS) that uses a hybrid HGAVNS to fulfill customer needs and enhance the efficiency of vehicle routing. The proposed method is tested against both generated and existing approaches using benchmark instances. Empirical results on 210 benchmark instances demonstrate the competitiveness of the hybrid approach hybrid approach is highly competitive in terms of solution quality. Overall, the experiments highlight that the Hybrid GA-VNS provides an efficient solution for the 2L-CVRP, delivering results that are on par with state-of-the-art methods.

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.

ABSTRACT Rising global dependence on cloud services has become crucial for enterprises, aiming to guarantee continuous data accessibility while pursuing enhanced energy efficiency and minimized carbon emissions from data centers. However, the persistent challenge of high‐energy consumption in these facilities necessitates a concentrated approach toward energy reduction. This paper introduces an innovative multi‐objective scheduling strategy for scientific workflows, tailored for heterogeneous computing environments. Our method employs a hybrid genetic algorithm, incorporating Hill Climbing to generate an initial population of chromosomes. Subsequently, a genetic algorithm optimizes task assignments to the most suitable virtual machines, utilizing a meticulously designed fitness function to evaluate each chromosome's suitability for solving the scheduling problem. Through extensive experimentation, we demonstrate that our proposed algorithm outperforms other scheduling techniques in terms of solution quality, contributing to reduced energy consumption, processing duration, and cost. We contend that this innovative approach holds substantial potential in mitigating the energy consumption and carbon footprint associated with cloud data centers, offering a sustainable and environmentally conscious solution for scientific workflow scheduling.

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.

Today, providing patients in need of emergency care with high-quality pre-hospital care is a crucial part of society. Pre-hospital emergency care refers to all Emergency Medical Services (EMSs) that patients get outside of a hospital before being transported to the closest hospital. EMS refers to a variety of related tasks, such as dispatch, ambulance response to the scene, triage, and ambulance transfer to a care facility. EMS is essential in saving lives and lowering mortality and morbidity rates. EMS is a crucial political component of healthcare since it frequently serves patients in need of urgent care's first point of contact with the healthcare system. any lag time in access Any delays in receiving treatment can mean the difference between life and death. The increasing demands placed on emergency services have prompted them to implement a number of initiatives, including tighter integration of urgent care in other healthcare settings. This study seeks to offer a bibliography and a thorough analysis of the most well-known EMS optimization models put out for effective global healthcare. We emphasize the methodologies used to solve the cutting-edge models.

This paper presents a novel Best Fit Decreasing (BFD) algorithm for virtual machine (VM) placement, formulated as a bin packing problem in cloud data centers. The algorithm focuses on minimizing resource wastage by strategically allocating VMs to physical servers. It operates in two stages: first, sorting the VMs in descending order of resource requirements, and second, searching for the best-fit server based on available resources. Extensive simulations using real-world workload traces demonstrate that the BFD algorithm outperforms popular placement strategies, reducing the number of servers required and achieving higher packing efficiency. Comparative analysis against First Fit Decreasing (FFD) and Best Fit (BF) algorithms reveals consistent superiority in terms of resource utilization and packing efficiency. The proposed BFD algorithm offers an effective solution for VM placement, improving resource utilization and system efficiency in cloud data centers. Its findings can guide the development of practical VM placement algorithms, leading to enhanced resource management and cost savings in cloud computing environments.

In recent years, the explosion of a large, complex and various amount of data in the transportation field required the implementation of advanced technologies. However, a numerous Intelligent Transportation System (ITS) technologies have been deployed. Data generated in ITS can be stored, managed, analysed and processed using Big Data analytics. The main contributions of this paper are first to design an architecture to deal with big data analytics in ITS and then to implement a parallel Genetic Algorithm(GA)-MapReduce (MR) as an optimization technique in order to minimize the travel distance. The purpose of this parallelism is to improve the population diversity using the parallel processing of the MapReduce and the exploration of the search space using GA in the case of Vehicle routing problem. To test the performance of the proposed model, our analysis is conducted from data collected from sensors located on the highways of Tunisia.

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.

In this paper, we propose a collaborative model for the supplier selection for the purchasing activity in the supply chain. The problem addresses a set of firms that try to look for a cost saving configuration to optimise their ordering plans, given a set of suppliers with quantity discounts options. Possible collaborations between firms, modelled as a coalition formation, can be beneficial in the sense that the gathering of their orders generates a cost minimisation regarding the stand-alone situation. We propose the mathematical formulation of firms’ collaborative ordering modelled as a cost-dependent assignment problem. The collaborative scenario is viewed as a two independent steps: the first step is based on game-theoretic approach to model possible coalitions of firms and to generate stable coalition structures according to the core concept. Once coalitions are formed, the second step consists mainly on the genetic algorithm is trigged to assign coalitions to shared suppliers. The assignment problem is solved using a specifically designed hybrid genetic algorithm. Experiments, driven on a large test-bed, highlight the effectiveness of the collaboration in handling the ordering activity within the supply chain and the usefulness of hybrid genetic algorithm in solving such supplier selection problems. We show that in all cases the collaborative scenario is more profitable regarding the stand-alone position. The obtained results show that the hybrid genetic algorithm is able to generate good quality solutions in a reasonable run time regarding Cplex results.

This paper studies a novel sustainable vessel routing problem modeling considering the multi-compartment, split pickup and split delivery, and time windows concepts. In the presented problem, oil tankers transport crude oil from supply ports to demand ports around the globe. The objective is to find ship routes, as well as port arrival and departure times, in a way that minimizes transportation costs. As a second objective, we considered the sustainability aspect by minimizing the vessel energy efficiency operational indicator. Multiple products are transported by a heterogeneous fleet of tankers. Small realistic test instances are solved with the exact method.

Recently, the explosion of large amounts of traffic data has guided data scientists to create models with big data for a better decision-making. Big Data applications process and analyze this huge amounts of data (collected from a variety of heterogeneous data sources) that cannot be processed with traditional technologies. In this paper, Big Data frameworks are used for solving an optimization problem known as Dynamic Vehicle Routing Problem (DVRP). Hence, due to the NP-Hardness of the problem and to deal with a large size of data, we develop a parallel Spark Genetic Algorithm named (S-GA). This parallelism aims to take the advantage of Spark’s in-memory computing ability (as a master-slave distribution computing) and GA’s iterations operations. Parallel operations were used for fitness evaluation and genetic operations. Based on the parallel S-GA a decision support system is developed for the DVRP in order to generate the best routes. The experiments show that our proposed architecture is improved due to its capacity when coping with Big Data optimization problems by interconnecting components and deploying on different nodes of a cluster.

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.

Analysis of biological systems is being progressively facilitated by computational tools. Most of these tools are based on qualitative and numerical methods. However, they are not always evident, and there is an increasing need to provide an additional semantic layer. Semantic technologies, especially ontologies, are one of the tools frequently used for this purpose. Indeed, they are indispensable for understanding the semantic knowledge about the operation of cells at a molecular level. We describe here the biomolecular network ontology (BNO) created specially to address the needs of analysing the complex biomolecular network’s behaviour. A biomolecular network consists of nodes, denoting cellular entities, and edges, representing interactions among cellular components. The BNO ontology provides a foundation for qualitative simulation of complex biomolecular networks. We test the performance of the proposed BNO ontology by using a real example of a biomolecular network, the bacteriophage T4 gene 32. We illustrate the proposed BNO ontology for reasoning and inferring new knowledge with sets of rules expressed in SWRL. Results demonstrate that the BNO ontology allows to precisely interpret the corresponding semantic context and intelligently model biomolecular networks and their state changes. The Biomolecular Network Ontology (BNO) is freely available at https://github.com/AliAyadi/BNO-ontology-version-1.0.

The fundamental goal of systems biology is to understand the dynamic aspects of cells and their behaviour. This organism is represented by a network so-called complex biomolecular network in which the nodes represent the different cellular components and the edges represent the interactions occurring among them. Through this network, it is easy to study the transition states and the dynamic behaviour of cells. Indeed, perturbing some nodes of the biomolecular network induce the transition of all the network. This process, known as the ”transittability”, expresses the idea of steering the complex biomolecular network from an unexpected state to a desired state. In this context, we are thus interested in how to use the transittability of biomolecular networks to increase the efficiency of translational medicine for improving human health and disease, including genetic and environmental factors of of patient’s well-being. This is a great opportunity to understand diseases, and find new diagnoses and treatments. Due to its complexity, the transittability of complex biomolecular networks can be considered as an optimization problem. Up to a recent date only few studies have been carried out in this problem. Most of them focused only on the minimization of the required nodes to steer the entire network, and others considered the minimization of the number of stimuli to be applied on the network. However, this assumption is not always realistic, because steering complex biomolecular networks is in general a multi-objective optimization problem. It requires finding appropriate trade-offs among various objectives, for example between the appropriate nodes to be stimulated and the number of external stimuli to be used and their cost, and the impact on patient’s well-being. In this paper, the optimization of the transittability of complex biomolecular networks is investigated from the multi-objective perspective. In the mathematical model four criteria are considered simultaneously: the minimization of the number of external stimuli, the minimization of their total cost, the minimization of the number of target nodes, and the minimization of the patient discomfort. All these objectives are described theoretically and mathematically in detail.

With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This phenomenon known as the ”transittability” means that complex biomolecular networks can be steered from an unexpected state to a desired state. This paper investigates the optimization of the transittability of complex biomolecular networks taking into account different objective functions. To solve this problem, we propose a multi-objective optimization approach which consists of two steps, the search and decision making step. The search step is based on a powerful multi-objective genetic algorithm, the non-dominated sorting genetic algrorithm (NSGA-II), to solve our problem and obtain a Pareto-optimal set. As regards the decision making step is based on the use of a multi-criteria decision making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), for providing the best compromise solution according to the user preferences. The proposed approach was tested and applied to solve the steering of the p53 Signaling network. Experimental results illustrate the effectiveness of this 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.

Analysis of biological systems is being progressively facilitated by computational tools. Most of these tools are based on qualitative and numerical methods. However, they are not always evident and there is an increasing need to provide an additional semantic layer. Semantic technologies, especially ontologies, are one of the tools frequently used for this purpose. In fact, they are indispensable for understanding the semantic knowledge about the functioning of cells on a molecular level. We describe here the biomolecular network ontology (BNO) created specially to address the needs of analysing the complex biomolecular network's behaviour. The BNO ontology is freely available at https://github.com/AliAyadi/The-BiomolecularNetwork-Ontology and can be viewed using the standard ontology visualization editor Protégé. This ontology provides qualitative simulation of large and complex biomolecular networks. Therefore, in order to evaluate the efficacy of the BNO ontology we present two kinds of reasoning mechanisms. The first consists on an SWRL rule based reasoning developed using the SWRL rules editor tab, and another consists on an implementation of a rule-based system under the MATLAB/SIMULINK development environment and can be freely downloaded at https://github.com/AliAyadi/QualitativeReasoningInMATLAB. Both of these reasoning mechanisms have been applied to the analysis of a real network of biological interest, the "bacteriophage T4 gene 32" use case.

This paper focuses on building personalized recommender system in the tourism field. The application recommends to a tourist the best attractions in a particular place according to his preferences, his profile and his appreciation to previous visited places. This paper proposes a hybrid recommender system that combines the three most known recommender methods which are: the collaborative filtering (CF), the content-based filtering (CB) and the demographic filtering (DF). In order to implement these recommender methods, we have applied different machine learning algorithms which are the K-nearest neighbors (K-NN) for both CB and CF and the decision tree for the DF. The hybridization is a good choice to make the best of their advantages and to overcome the cold start problem. To enhance the recommendation accuracy, we use two hybridization techniques: switching and weighted. For the weighted approach, a novel linear programming model is applied to obtain the optimal weights' values. An extensive experimental study is conducted based on different evaluation metrics using extracted data from TripAdvisor. Our results show that the hybrid method is more accurate than the other recommender approaches used separately.

In this paper, we present a generalisation of the Capacitated Vehicle Routing problem where customer demand is composed of two-dimensional weighted items, with a time windows, called 2L-CVRPTW. The objective consists in designing a set of trips, starting and terminating at a central depot, minimizing the total transportation cost with a homogenous fleet of vehicles based on a depot node. Items in each vehicle trip must satisfy the two-dimensional orthogonal packing constraint. In literature, several exact and approximate approaches was proposed to find the best and the most feasible solution to the problem. In this paper, we use an adaptive genetic algorithm (GA) to solve the 2L-CVRPTW using an ordered crossover operator. The effectiveness of our approach was demonstrated through experiments on benchmark instances. The results showed that our proposed approach is competitive in terms of the quality of the solutions found.

Because of the lack of satisfactory solutions to explain biological systems, biologists usually focus on modelling and simulation tools to understand the behaviour of these complex organisms. Indeed, computational modelling and simulation of cells plays a pivotal role in systems biology. In this paper, we tackle the problem of studying the behaviour of human cells by reproducing the behaviour of complex biomolecular networks. To this end, we present in this paper an approach for simulating complex biomolecular networks inspired by the discrete-event simulation model (DEVS), a formalism developed for supporting the modelling of complex systems. In this paper, we propose a simulation tool, named ”CBNSimulator”, based on a logical model of the biomolecular network and taking advantage of the performance of a discrete-time simulation model for understanding the evolution and the behaviour of complex biomolecular networks as a discrete sequence of events in time. The proposed tool has been applied to the case study of a ribosomal protein regulation network, named ”the bacteriophage T4 gene 32”, and results given by this simulation tool are in agreement with the expert’s judgement. Moreover, the graphical user interface of CBNSimulator allows biologists to easily reproduce, analyse and understand behaviour of complex biomolecular networks through discrete simulation.

The use of semantic technologies, such as ontologies, to describe and analyse biological systems is at the heart of systems biology. Indeed, understanding the behaviour of cells requires a large amount of context information. In this paper, we propose an ontology entitled ”Biomolecular Network ontology” using the OWL language. The BNO ontology standardises the terminology used by biologists experts to address issues including semantic behaviour representation, reasoning and knowledge sharing. The main benefit of this proposed ontology is the ability to reason about dynamical behaviour of complex biomolecular networks over time. We demonstrate our proposed ontology with a detailed example, the bacteriophage T4 gene 32 use case.

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 investigate in this paper a Green Travelling Salesman Problem with Time Windows (GTSPTW), a bi-criteria variant of the classical TSPTW. The proposed GTSPTW consists of determining the vehicle’s speed in order to minimize the fuel consumption and resulting emission costs. The problem can model numerous industries as service companies and post office. We propose a linear mixed integer mathematical model for the GTSPTW. Due to the NP-hardness of the problem, we develop a hybrid bi-criteria ant colony optimization starts from a local search solution, then performs numerous neighborhoods. We compare the ability of the proposed algorithm with the exact solution using CPLEX on benchmark instances in the literature. In order to demonstrate the performance of our proposed approach, we apply it on a real case within the city of Gafsa in the south of Tunisia.

The efficient management of ambulance routing for emergency requests is vital to save patients when a disaster response scenario occurs. In today’s road traffic, the transportation of patients from emergency points becomes more difficult in such tragic situation. We consider an urgent situation where a lot of hurts/patients require simultaneously an urgent medical care. Patients are either (1) slightly injured which can be assisted on the spot or (2) seriously injured which should be transferred to hospitals. In this paper, we aim to enhance the response-time performance of emergency medical service providers by handling the ambulance routing problem (ARP). The problem can be modeled as either the Open Vehicle Routing Problem (OVRP) or a Vehicle Routing Problem with Pickup and Delivery (VRPPD). We propose a cluster-first route-second algorithm based on the Petal algorithm and the particle swarm optimization (PSO) approach in order to handle efficiently the ARP.

We investigate in this paper a new variant of multi-depot vehicle routing problem with time windows is studied (GMDVRPTW), an extension of the MDVRPTW. In the new variant, the proposed GMDVRPTW consists of determining the vehicle's speed in order to minimize a function comprising fuel consumption and resulting emission costs. An integer programming model is formulated with two objectives to find the minimum travel cost and total fuel consumption and CO2emissions under the constrains of time window, capacity of the vehicle, the fleet size. As the problem is an NP-Hard problem, we develop an improved meta-heuristic, based on an ant colony optimization and local search to solve the problem. The results show that the proposed approach is competitive in terms of solution quality.

Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.

The aircrew rostering problem belongs to the class of NP-Hard combinatorial optimization problems. It consists on constructing individual rosters through the distribution of planned pairings among available crew members in airline industries. In that purpose, several approaches were proposed through adapting imposed constraints to both the requirements and the internal regulations of each airline company. This paper deals with balancing three main objective functions per flight crew member. These objectives are the amount of flight hours, the total layover of all the assigned rotations and the destinations' occurrences. To do so, a new multi-objective linear model is stated and an implementation of an adapted genetic algorithm is illustrated. Testing the algorithm on real-life data provided by the national Tunisian airline company TunisAir proved the efficiency of our approach.

In Supply Chain Management, several costs should be taken into account. They cover all of the holding, purchasing, ordering and transportation. Minimizing costs may be done through considering each cost separately or all of them simultaneously. In this paper, we focus on the ordering problem. In a cooperative context, two major fundamental relations were studied: the first one considers the possible coalitions between the retailers, while the second one studies the assignment between suppliers and coaltions. Our contribution is based on a comparative study of two noteworthy methods of the well-known Game Theory, Nash equilibrium and the Core solutions in an attempt to find the optimal one. To do so, an application is developed to conduct the comparison.

We address in this paper a hydrogen transportation management problem within the supply chain. The hydrogen in the liquid or gaseous state has to be transported to numerous destinations (storage points or final customers) while considering specific product requirements. We focus on the liquid hydrogen transportation problem modeled as a vehicle routing problem with time windows, as the customers generally imposes a time interval for the delivery. We develop a decision support system based on a genetic approach to solve this problem and drive a series of experiments on Solomon's benchmark. We show through empirical study that the genetic algorithm outperforms state-of-the-art approaches.

Systems biology models aim to describe and understand the behaviour of a cell. This living organism is represented by a complex biomolecular network. In the literature, most researches focus only on modeling isolated parts of this network, such as the metabolic network or the gene regulatory network. However, to fully understand the behaviour of a cell we should model and analyze the biomolecular network as a whole. Towards this goal, we firstly present a formalization for describing the logical structure, function and behaviour of complex biomolecular networks. In addition, we propose a semantic approach based on four ontologies to provide a rich description for modeling a biomolecular network and its state changes. This approach contributes to propose to the biologist a platform where to simulate the state changes of biomolecular networks with the hope of steering their behaviours.

We investigate in this paper a biomass transportation problem that considers pickup and delivery points in order to transport the biomass to power generators. The problem is appropriately modeled as the one commodity pickup and delivery traveling salesman problem. Due to its NP-hardness, the problem should be solved by approximate approaches, especially for large-scaled problems. To do so, we develop a hybrid metaheuristic, based on a variable neighborhood search and a variable neighborhood descent, that performs numerous neighborhood structures. The experimental study driven on benchmarks shows that the hybrid heuristic performs well when compared to the existing approaches.

It is often frustrating for drivers to find parking spaces, and parking itself is costly in almost every major city in the world. The search for a parking place is a task which can waste a lot of time and affect the efficiency of economic activities, social interactions, and the health of the environment. The planners of transport and city traffic must pay close attention to this issue in order to achieve an efficient management of mobility in smart cities. This work is intended to serve as an aid in the search for parking seeking the general interest of a group of drivers. We present an intensive description of the parking slots assignment problem for groups and apply it to a real case study. Also, we propose a hybrid genetic algorithm for solving this case and we compare it with three other algorithms in order to evaluate its performance.

In this paper an iterated local search (ILS) is embedded with a variable neighborhood Descent (VND) hyper-heuristic. The proposed hyper-heuristic combines low-level heuristics. Several variants from the literature within the proposed ILS were implemented and tested. This article conducts an empirical study involving hard combinatorial optimization problems, permutation flowshop scheduling problem (PFSP) with the objectives of minimizing makespan and the total flowtime of jobs. The proposed ILS based hyper-heuristic proved its general and applicable across the studied problems.

The multi-objective node placement (MONP) problem involves the extension of an existing heterogeneous network while optimizing three conflicting objectives: maximizing the communication coverage, minimizing active nodes and communication devises costs, and maximizing of the total capacity bandwidth in the network. Multiple devices' types are to be deployed in order to ensure networks' heterogeneity. As the MONP problem is NP-Hard, heuristic approaches are necessary for large problem instances. In this paper, we compare the ability of three different sorting-based multiple objective genetic algorithms to find an optimal placement and connection between the potential placed nodes. The empirical validation is performed using a simulation environment called Inform Lab and based on real instances of maritime surveillance application. Results and discussion on the performance of the algorithms are provided.

The Multi–objective Node Placement (MONP) problem focuses on extending an existing communication in- frastructure with new wireless heterogeneous network components while achieving cost effectiveness and ease of management. This extention aims to broaden the coverage and handle demand fluctuations. In this paper, the MONP problem is modelled as a multi–objective optimization problem with three objectives: maximiz- ing the communication coverage, minimizing active nodes and communication devices costs, and maximizing of the total capacity bandwidth in the network. As the MONP problem is N P–Hard, we present a meta– heuristic based on the Tabu Search approach specifically designed for multi–objective problems in wireless networks. We present an empirical validation of the model and the algorithm based on a selection of a real and large set of instances. We present also a performance comparison between the suggested algorithm and a multi–objective genetic algorithm (MOGA). All tests are performed on a real simulation environment for the maritime surveillance application.

We address in this paper a special case of the online knapsack problem (OKP) that considers a number of items arriving sequentially over time without any prior information about their features. As items features are not known in advance but revealed at their arrival, we allow the decision maker to delay his decision about incoming items (to select the current item or reject it) until observing the next ones. The main objective in this problem is to load the best subset of items that maximizes the expected value of the total reward without exceeding the knapsack capacity. The selection process can be stopped before observing all items if the capacity constraint is exhausted or when the decision maker estimates that no suitable items will be received in the future. We propose to solve this problem an exact solution approach that decomposes the original problem dynamically and incorporates an optimal stopping rule in order to decide whether to load or not each new incoming item. We illustrate the proposed approach by numerical experimentations and compare the obtained results for different utility functions using performance measures. We prove therefore that the solution depends mainly on the decision maker's utility function and his readiness to take risks.

The spatial character intrinsic to the routing field requires the integration of geographic information systems (GIS) and optimization approaches to handle spatial and non-spatial data in transportation applications. Motivated by the need to better support decision making in logistic area, we develop an interactive spatial decision support system (SDSS) for solving the vehicle routing problems by coupling the simulated annealing method with Quantum GIS (QGIS). In this paper, the evoked variants of VRP are detailed and formulated mathematically. The SDSS architecture is designed for the VRPs showing the interaction of GIS and SA approach according to the tight coupling strategy. A VRP variant termed the Open VRP (OVRP) is selected to show the system effectiveness. The computational performance of the SDSS for the OVRP, based on a set of benchmark instances, turned out to be effective on both computation time and solution quality.

Large volume surveillance missions are characterized by the employment of mobile and fixed surveillance assets to a large geographic operation area in order to perform surveillance activities. Finding efficient management solutions should be investigated to optimize assets allocation and tasks achievement. In this paper, we propose to model this optimization problem as a multi-objective, multi-mode assignment and scheduling problem. Resources are to be assigned to accomplish the tasks. Then, surveillance tasks should be scheduled onto successive periods. The problem is designed to consider two conflicting objective functions: minimizing the makespan and minimizing the total cost. As the problem is NP-Hard, a bi-colony ant based approach is proposed. The empirical validation is done using a simulation environment Inform Lab. The experimental results show that the computational time remains polynomial with respect to the problem's size.