Approximation algorithms for solving multi-objective optimization problems

This paper tries to cover the main aspects/properties related to scheduling problems, approximation algorithms, and multi-objective combinatorial optimization. Then, we try to describe the main techniques that can be used to solve such problems. In this paper, the reviews results relate to multi-objective optimization problems, exact and approximation search, with the aim of getting all Pareto optimal solutions for some NP-hard problems

Classification of COVID-19 in Chest X-Ray Images using Deep Transfer Learning

In December 2019, the novel coronavirus appeared in Wuhan, China, and became a critical public health problem worldwide. The transmission of this virus via small droplets produced by coughing, sneezing, and talking led to the rapid spread of the virus. Noteworthy, the coronavirus caused a devastating effect on daily lives, public health, the global economy and still threatening the lives of billions of people. Therefore, a fast and accurate method of diagnosing COVID-19 infection is vital to prevent the spread of the disease and to quickly treat affected patients. In this paper, we proposed a deep learning model for classifying covid-19 chest X-ray images into six classes. However, the main challenges are there is no large enough covid-19 dataset in the public domain compared to other classes. Hence, it is not easy to distinguish the similarities between categories and detailed features. Therefore, to counteract the problem of insufficient annotated images of covid-19 compatible with other classes, transfer learning is used which is also an effective deep feature extractor to extract similarity features between these classes.  In fact, we trained three pre-training models [RestNet50, MobileNet, ResNet101] to classify covid-19 X-ray images into six classes. The experimental results showed the validity and efficiency of our proposed model which exceeds all proposed models in the literature

An approach for Schools Management System on The Cloud Computing

                                        The schools management information system plays an essential role for the success of the school management. The main purpose of the management information system at initial steps of its development is to improve the efficiency of the office activities. In school environment it was used to store student and personnel data. The most concern was being focused on data entry and collation, rather than on data transfer or analysis. This paper proposed a framework based on cloud computing that provides detailed and summarized information on the critical areas of the management activities to guide schools administrators in planning and in decision-making. Such information system will be accessible anywhere anytime as data are stored in remote servers that are accessible to users over the internet. Unified Modeling Language (UML) is used in the development of the proposed framework. To evaluate the developed framework, a web based application was developed using the proposed framework, and then some International Organization for Standardization (ISO) qualification metrics were used to evaluate the developed web based application using some selected characteristics. The evaluation results show that the proposed framework is very effective. Through the evaluation, the proposed framework is found to represents a solution to most of the problems mentioned in the previous researches and this implies that the proposed framework can be adopted by schools for more efficient information management and more effective management decisions. &nbsp

Algorithmes d'approximation polynomiale pour des problèmes d’ordonnancement sur machines parallèles dans un contexte multi-objectif ou contraint

This thesis addresses the scheduling problems on parallel machines, with and without nonavailability constraints, and the design of approximation methods dedicated to solving this problem in a multi-objective context (load balancing and minimization of the delivery times). These are critical logistical issues for the quality of service and the performance of such systems. These are NP-hard optimization problems. In this context, the carried work of this thesis leads to a contribution to solving and approximating the performance of these systems with parallel resources. Many optimization methods have been developed and tested in this context. These methods include different approaches such as heuristics of guaranteed performance, dynamic programming algorithms, polynomial time approximation scheme (PTAS), and fully polynomial time approximation scheme (FPTAS). In particular, we thoroughly analyzed the basic substructure with two parallel machines. We have studied the scenario, with a constraint of unavailability on a machine, associated with this substructure. We have shown that the problem has a constant polynomial approximation algorithm. Thus, we presented a dynamic programming algorithm and an FPTAS, which has a strongly polynomial running time. Experimental tests have been performed and used to compare the performances of the proposed algorithms on several sets of instances. The second important contribution of this thesis is related to the determination of the Pareto solutions for the same substructure (two parallel machines) but without non-availability constraint. Many methods have been proposed in this section: dynamic programming, PTAS, FPTAS in two versions, with detailed experimental comparisons. The third contribution of the thesis concerns the extension of the multi-objective study to the problem of scheduling jobs on m parallel machines. Different extensions and algorithms have been proposed: a dynamic programming, a PTAS and an FPTAS for fixed value of m. Experimental tests were conducted and allowed to evaluate and compare the performance of these methods.Cette thèse traite du problème d’ordonnancement sur machines parallèles, avec et sans indisponibilités, et de la conception de méthodes d’approximation dédiées à la résolution de ce problème dans un contexte multi-objectif (équilibrage des charges et minimisation de la date de livraison). Ce sont des problèmes cruciaux dans le domaine logistique pour améliorer la qualité de service et la performance de tels systèmes. Il s’agit des problèmes d’optimisation NP-difficiles. Dans cette optique, les travaux de cette thèse constituent une contribution à la résolution exacte et approchée de ces systèmes à ressources parallèles. Plusieurs méthodes d’optimisation ont été développées et testées dans ce cadre. De telles méthodes incorporent différentes approches de résolution ou d’optimisation, comme les heuristiques à garantie de performance, les algorithmes de programmation dynamique, les schémas d’approximation polynomiaux (PTAS) et entièrement polynomiaux (FPTAS)

Algorithmes d'approximation polynomiale pour des problèmes d’ordonnancement sur machines parallèles dans un contexte multi-objectif ou contraint

Cette thèse traite du problème d’ordonnancement sur machines parallèles, avec et sans indisponibilités, et de la conception de méthodes d’approximation dédiées à la résolution de ce problème dans un contexte multi-objectif (équilibrage des charges et minimisation de la date de livraison). Ce sont des problèmes cruciaux dans le domaine logistique pour améliorer la qualité de service et la performance de tels systèmes. Il s’agit des problèmes d’optimisation NP-difficiles. Dans cette optique, les travaux de cette thèse constituent une contribution à la résolution exacte et approchée de ces systèmes à ressources parallèles. Plusieurs méthodes d’optimisation ont été développées et testées dans ce cadre. De telles méthodes incorporent différentes approches de résolution ou d’optimisation, comme les heuristiques à garantie de performance, les algorithmes de programmation dynamique, les schémas d’approximation polynomiaux (PTAS) et entièrement polynomiaux (FPTAS).This thesis addresses the scheduling problems on parallel machines, with and without nonavailability constraints, and the design of approximation methods dedicated to solving this problem in a multi-objective context (load balancing and minimization of the delivery times). These are critical logistical issues for the quality of service and the performance of such systems. These are NP-hard optimization problems. In this context, the carried work of this thesis leads to a contribution to solving and approximating the performance of these systems with parallel resources. Many optimization methods have been developed and tested in this context. These methods include different approaches such as heuristics of guaranteed performance, dynamic programming algorithms, polynomial time approximation scheme (PTAS), and fully polynomial time approximation scheme (FPTAS). In particular, we thoroughly analyzed the basic substructure with two parallel machines. We have studied the scenario, with a constraint of unavailability on a machine, associated with this substructure. We have shown that the problem has a constant polynomial approximation algorithm. Thus, we presented a dynamic programming algorithm and an FPTAS, which has a strongly polynomial running time. Experimental tests have been performed and used to compare the performances of the proposed algorithms on several sets of instances. The second important contribution of this thesis is related to the determination of the Pareto solutions for the same substructure (two parallel machines) but without non-availability constraint. Many methods have been proposed in this section: dynamic programming, PTAS, FPTAS in two versions, with detailed experimental comparisons. The third contribution of the thesis concerns the extension of the multi-objective study to the problem of scheduling jobs on m parallel machines. Different extensions and algorithms have been proposed: a dynamic programming, a PTAS and an FPTAS for fixed value of m. Experimental tests were conducted and allowed to evaluate and compare the performance of these methods