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This paper addresses a production and distribution problem in a supply chain. The supply chain consists of a plant with no storage capacity that produces only one type of product. The manufactured products are then transported to a depot for storage. Customers demand is met by a homogeneous fleet of vehicles that begins and ends their trips at the depot. The objective of the study is to minimize the overall cost of production, inventory and transport throughout the supply chain. A Branch-and-Cut and a hybrid Two Phases Decomposition Heuristic using a Mixed Integer Programming and a Genetic Algorithm have been developed to solve the problem.

The work on predicting learner performance allows researchers through machine learning methods to participate in the improvement of e-learning. This improvement allows, little by little, e-learning to be promoted and adopted by several educational structures around the world. Neural networks, widely used in various performance prediction works, have made several exploits. However, factors that are highly influential in the field of learning have not been explored in machine learning models. For this reason, our study attempts to show the importance of the forgetting factor in the learning system. Thus, to contribute to the improvement of accuracy in performance predictions. The interest being to draw the attention of researchers in this field to very influential factors that are not exploited. Our model takes into account the study of the forgetting factor in neural networks. The objective is to show the importance of attenuation the forgetting, on the quality of performance predictions in e-learning. Our model is compared to those based on Random Forest and linear regression algorithms. The results of our study show first that neural networks (95.20%) are better than Random Forest (95.15%) and linear regression (93.80%). Then, with the attenuation of forgetting, these algorithms give 96.63%, 95.85% and 93.80% respectively. This work allowed us to show the great relevance of oblivion in neural networks. Thus, the exploration of other unexploited factors will make better performance prediction models.

The cloud IaaS easily offers to have homogeneous multi-core machines (whether they are "bare metal" machines or virtual machines). On each of these machines, there can be high-performance input-output SSD disks. That allows to distribute the files produced during the execution of the workflow to different machines in order to minimize the additional costs associated with transferring these files. In this paper, we propose a scheduling algorithm called WSRDT (Workflow Scheduling Reducing Data Transfers) whose purpose is to minimize the makespan (execution time) of data-intensive workflows by reducing transfers data between dependent tasks on the network. Intermediate files produced by tasks are stored locally on the disk of the machine where the tasks were executed. We experimentally verify that the increase in the number of cores per machine reduces the additional cost due to data transfers on the network. Experiences with a veritable workflow show those advantages of the algorithms presented. Data-driven scheduling significantly reduces the execution time and the volume of data transferred on the network, our approach outperforms one of the best state-of-the-art algorithms that we have adapted with our hypotheses.