To offer high quality services, when users are increasingly demanding and competition more and more hard, is now a major problem that transportation companies are faced with. So, ensuring a regular traffic needs to identify the randomly occurring disturbances that affect the transportation system and to eliminate or reduce their impacts on the traffic.This paper presents a decision support system TRSS (Traffic Regulation Support System). TRSS is a supervision environment for the regulation of urban transportation system. TRSS (tram and bus) is based on the regulation operator decision-making process. It provides the operator with the information he needs to identify disturbances and evaluate potential corrective actions to be carried out, according to the regulation strategy he has selected.The first part of the paper presents the decision model we work with. The second part deals with the functional model used in the decision support system. Decision support system for transportation and characteristics of a DSS for a transportation system are described in the third part. In the fourth part, we present the components of the decision-making TRSS supervision tool. In the fifth part, we present the criteria of evaluation and the sixth part is devoted to the presentation of the results.

The Maritime Transport is a favoured tool regarding the universal exchanges because it has gone through several evolutions. Indeed, the containerization is considered as one of the most remarkable improvements in the shipping. The containers are rented by shipping companies. However, these companies meet an empty container availability problem at some ports of Maritime Transport Network (MTN) to satisfy the demands of clients. The objective of this work is to solve the problem of the imbalance of the distribution of containers and look for empty containers at less cost to meet the demands of clients. As a result, the authors propose an application to represent the MTN, and provide a balanced distribution of containers. The work presented in this article is based on a heuristic method by neighbourhood. It allows the process of the clients' demands and transfers of full containers as well as the research of empty containers by optimizing the cost of theirs return.

The explosion of the data quantities, which reflects the scaling of volumes, numbers, and types, has resulted in the development of new locations techniques and access to data. The final steps in this evolution have emerged new technologies: cloud computing and big data. The new requirements and the difficulties encountered in the management of data classified “big data” have emerged NoSQL and NewSQL systems. This paper develops a comparative study about the performance of six solutions NoSQL, employed by the important companies in the IT sector: MongoDB, Cassandra, HBase, Redis, Couchbase, and OrientDB. To compare the performance of these NoSQL systems, the authors will use a very powerful tool called YCSB: Yahoo! Cloud Serving Benchmark. The contribution is to provide some answers to choose the appropriate NoSQL system for the type of data used and the type of processing performed on that data.

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.

NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

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The technological revolution integrating multiple information sources and extension of computer science in different sectors led to the explosion of the data quantities, which reflects the scaling of vo-lumes, numbers and types. These massive increases have resulted in the development of new location techniques and access to data. The final steps in this evolution have emerged new technologies: Cloud and Big Data. The reference implementation of the Clouds and Big Data storage is incontestably the Hadoop Distributed File System (HDFS). This latter is based on the separation of metadata to data that consists in the centralization and isolation of the metadata of storage servers. In this paper, the authors propose an approach to improve the service metadata for Hadoop to maintain consistency without much compromising performance and scalability of metadata by suggesting a mixed solution between centralization and distribution of metadata to enhance the performance and scalability of the model.

International audienceThe adaptive hypermedia systems or adaptive Web applications is a research area between hypermedia and user modeling. It can customize hyperspace to different users. The existing reference models are generic and are not dedicated to educational systems. This chapter presents in the first part a reference model that is specific to adaptive educational hypermedia systems. This model is called ALEM (Adaptive Learning Environment Model). It consists of a domain model, a learner model, a course structuring model and an adaptation model. The main contribution of this model is modeling the adaptive learning curriculum. Furthermore we develop the UML Tutor application which is an educational adaptive hypermedia system based on our reference model