Theory of fuzzy integrals and its applications

Integer programming formulations of discrete hub location problems

The agent computing paradigm is rapidly emerging as one of the powerful technologies for the development of large-scale distributed systems to deal with the uncertainty in a dynamic environment. The domain of traffic and transportation systems is well suited for an agent-based approach because transportation systems are usually geographically distributed in dynamic changing environments. Our literature survey shows that the techniques and methods resulting from the field of agent and multiagent systems have been applied to many aspects of traffic and transportation systems, including modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. This paper examines an agent-based approach and its applications in different modes of transportation, including roadway, railway, and air transportation. This paper also addresses some critical issues in developing agent-based traffic control and management systems, such as interoperability, flexibility, and extendibility. Finally, several future research directions toward the successful deployment of agent technology in traffic and transportation systems are discussed.

A Review of the Applications of Agent Technology in Traffic and Transportation Systems

The fields of medicine science and health informatics have made great progress recently and have led to in-depth analytics that is demanded by generation, collection and accumulation of massive data. Meanwhile, we are entering a new period where novel technologies are starting to analyze and explore knowledge from tremendous amount of data, bringing limitless potential for information growth. One fact that cannot be ignored is that the techniques of machine learning and deep learning applications play a more significant role in the success of bioinformatics exploration from biological data point of view, and a linkage is emphasized and established to bridge these two data analytics techniques and bioinformatics in both industry and academia. This survey concentrates on the review of recent researches using data mining and deep learning approaches for analyzing the specific domain knowledge of bioinformatics. The authors give a brief but pithy summarization of numerous data mining algorithms used for preprocessing, classification and clustering as well as various optimized neural network architectures in deep learning methods, and their advantages and disadvantages in the practical applications are also discussed and compared in terms of their industrial usage. It is believed that in this review paper, valuable insights are provided for those who are dedicated to start using data analytics methods in bioinformatics.

A Survey of Data Mining and Deep Learning in Bioinformatics

Blockchain has been envisioned to be a disruptive technology with potential for applications in various industries. As more and more different blockchain platforms have emerged, it is essential to assess their performance in different use cases and scenarios. In this paper, we conduct a systematic survey on the blockchain performance evaluation by categorizing all reviewed solutions into two general categories, namely, empirical analysis and analytical modelling. In the empirical analysis, we comparatively review the current empirical blockchain evaluation methodologies, including benchmarking, monitoring, experimental analysis and simulation. In analytical modelling, we investigate the stochastic models applied to performance evaluation of mainstream blockchain consensus algorithms. Through contrasting, comparison and grouping different methods together, we extract important criteria that can be used for selecting the most suitable evaluation technique for optimizing the performance of blockchain systems based on their identified bottlenecks. Finally, we conclude the survey by presenting a list of possible directions for future research.

/pdf/performance-evaluation-of-blockchain-systems-a-systematic-2djqil7z7f.pdf

Performance Evaluation of Blockchain Systems: A Systematic Survey

The﻿technological﻿revolution﻿integrating﻿multiple﻿information﻿sources﻿and﻿extension﻿of﻿computer﻿ science﻿in﻿different﻿sectors﻿led﻿to﻿the﻿explosion﻿of﻿the﻿data﻿quantities,﻿which﻿reflects﻿the﻿scaling﻿of﻿volumes,﻿numbers﻿and﻿types.﻿These﻿massive﻿increases﻿have﻿resulted﻿in﻿the﻿development﻿of﻿new﻿location﻿ techniques﻿and﻿access﻿to﻿data.﻿The﻿final﻿steps﻿in﻿this﻿evolution﻿have﻿emerged﻿new﻿technologies:﻿Cloud﻿ and﻿Big﻿Data.﻿The﻿reference﻿implementation﻿of﻿the﻿Clouds﻿and﻿Big﻿Data﻿storage﻿is﻿incontestably﻿the﻿ Hadoop﻿Distributed﻿File﻿System﻿(HDFS).﻿This﻿latter﻿is﻿based﻿on﻿the﻿separation﻿of﻿metadata﻿to﻿data﻿that﻿ consists﻿in﻿the﻿centralization﻿and﻿isolation﻿of﻿the﻿metadata﻿of﻿storage﻿servers.﻿In﻿this﻿paper,﻿the﻿authors﻿ propose﻿an﻿approach﻿to﻿improve﻿the﻿service﻿metadata﻿for﻿Hadoop﻿to﻿maintain﻿consistency﻿without﻿ much﻿compromising﻿performance﻿and﻿scalability﻿of﻿metadata﻿by﻿suggesting﻿a﻿mixed﻿solution﻿between﻿ centralization﻿and﻿distribution﻿of﻿metadata﻿to﻿enhance﻿the﻿performance﻿and﻿scalability﻿of﻿the﻿model. KeywoRDS Big Data, Clouds of Storage, Hadoop, HDFS, MapReduce, Metadata

Towards a New Model of Storage and Access to Data in Big Data and Cloud Computing

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.

https://www.mdpi.com/1996-1073/13/18/4948/pdf

Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

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.

/pdf/decision-making-system-for-regulation-of-a-bimodal-urban-24umilsrb6.pdf

Decision Making System for Regulation of a Bimodal Urban Transportation Network, Associating a Classical and a Multi-Agent Approaches

A spatial group decision support system: Coupling negotiation and multicriteria approaches

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.

Karim Bouamrane

Papers

Towards a New Model of Storage and Access to Data in Big Data and Cloud Computing

Manufacturing 4.0 Operations Scheduling with AGV Battery Management Constraints

Decision Making System for Regulation of a Bimodal Urban Transportation Network, Associating a Classical and a Multi-Agent Approaches

A spatial group decision support system: Coupling negotiation and multicriteria approaches

Evaluation of NoSQL Databases: MongoDB, Cassandra, HBase, Redis, Couchbase, OrientDB