Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?†Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution.supply chain;MAS;multi agent systems

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?” Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

Organization based multiagent architecture for distributed environments

[EN]Distributed environments represent a complex field in which applied solutions should be flexible and include significant adaptation capabilities. These environments are related to problems where multiple users and devices may interact, and where simple and local solutions could possibly generate good results, but may not be effective with regards to use and interaction. There are many techniques that can be employed to face this kind of problems, from CORBA to multi-agent systems, passing by web-services and SOA, among others. All those methodologies have their advantages and disadvantages that are properly analyzed in this documents, to finally explain the new architecture presented as a solution for distributed environment problems. The new architecture for solving complex solutions in distributed environments presented here is called OBaMADE: Organization Based Multiagent Architecture for Distributed Environments. It is a multiagent architecture based on the organizations of agents paradigm, where the agents in the architecture are structured into organizations to improve their organizational capabilities. The reasoning power of the architecture is based on the Case-Based Reasoning methology, being implemented in a internal organization that uses agents to create services to solve the external request made by the users. The OBaMADE architecture has been successfully applied to two different case studies where its prediction capabilities have been properly checked. Those case studies have showed optimistic results and, being complex systems, have demonstrated the abstraction and generalizations capabilities of the architecture. Nevertheless OBaMADE is intended to be able to solve much other kind of problems in distributed environments scenarios. It should be applied to other varieties of situations and to other knowledge fields to fully develop its potencial.[ES]Los entornos distribuidos representan un campo de conocimiento complejo en el que las soluciones a aplicar deben ser flexibles y deben contar con gran capacidad de adaptación. Este tipo de entornos está normalmente relacionado con problemas donde varios usuarios y dispositivos entran en juego. Para solucionar dichos problemas, pueden utilizarse sistemas locales que, aunque ofrezcan buenos resultados en términos de calidad de los mismos, no son tan efectivos en cuanto a la interacción y posibilidades de uso. Existen múltiples técnicas que pueden ser empleadas para resolver este tipo de problemas, desde CORBA a sistemas multiagente, pasando por servicios web y SOA, entre otros. Todas estas mitologías tienen sus ventajas e inconvenientes, que se analizan en este documento, para explicar, finalmente, la nueva arquitectura presentada como una solución para los problemas generados en entornos distribuidos. La nueva arquitectura aquí se llama OBaMADE, que es el acrónimo del inglés Organization Based Multiagent Architecture for Distributed Environments (Arquitectura Multiagente Basada en Organizaciones para Entornos Distribuidos). Se trata de una arquitectura multiagente basasa en el paradigma de las organizaciones de agente, donde los agentes que forman parte de la arquitectura se estructuran en organizaciones para mejorar sus capacidades organizativas. La capacidad de razonamiento de la arquitectura está basada en la metodología de razonamiento basado en casos, que se ha implementado en una de las organizaciones internas de la arquitectura por medio de agentes que crean servicios que responden a las solicitudes externas de los usuarios. La arquitectura OBaMADE se ha aplicado de forma exitosa a dos casos de estudio diferentes, en los que se han demostrado sus capacidades predictivas. Aplicando OBaMADE a estos casos de estudio se han obtenido resultados esperanzadores y, al ser sistemas complejos, se han demostrado las capacidades tanto de abstracción como de generalización de la arquitectura presentada. Sin embargo, esta arquitectura está diseñada para poder ser aplicada a más tipo de problemas de entornos distribuidos. Debe ser aplicada a más variadas situaciones y a otros campos de conocimiento para desarrollar completamente el potencial de esta arquitectura

Metadata-driven data integration

Cotutela: Universitat Politècnica de Catalunya i Université Libre de Bruxelles, IT4BI-DC programme for the joint Ph.D. degree in computer science.Data has an undoubtable impact on society. Storing and processing large amounts of available data is currently one of the key success factors for an organization. Nonetheless, we are recently witnessing a change represented by huge and heterogeneous amounts of data. Indeed, 90% of the data in the world has been generated in the last two years. Thus, in order to carry on these data exploitation tasks, organizations must first perform data integration combining data from multiple sources to yield a unified view over them. Yet, the integration of massive and heterogeneous amounts of data requires revisiting the traditional integration assumptions to cope with the new requirements posed by such data-intensive settings. This PhD thesis aims to provide a novel framework for data integration in the context of data-intensive ecosystems, which entails dealing with vast amounts of heterogeneous data, from multiple sources and in their original format. To this end, we advocate for an integration process consisting of sequential activities governed by a semantic layer, implemented via a shared repository of metadata. From an stewardship perspective, this activities are the deployment of a data integration architecture, followed by the population of such shared metadata. From a data consumption perspective, the activities are virtual and materialized data integration, the former an exploratory task and the latter a consolidation one. Following the proposed framework, we focus on providing contributions to each of the four activities. We begin proposing a software reference architecture for semantic-aware data-intensive systems. Such architecture serves as a blueprint to deploy a stack of systems, its core being the metadata repository. Next, we propose a graph-based metadata model as formalism for metadata management. We focus on supporting schema and data source evolution, a predominant factor on the heterogeneous sources at hand. For virtual integration, we propose query rewriting algorithms that rely on the previously proposed metadata model. We additionally consider semantic heterogeneities in the data sources, which the proposed algorithms are capable of automatically resolving. Finally, the thesis focuses on the materialized integration activity, and to this end, proposes a method to select intermediate results to materialize in data-intensive flows. Overall, the results of this thesis serve as contribution to the field of data integration in contemporary data-intensive ecosystems.Les dades tenen un impacte indubtable en la societat. La capacitat d’emmagatzemar i processar grans quantitats de dades disponibles és avui en dia un dels factors claus per l’èxit d’una organització. No obstant, avui en dia estem presenciant un canvi representat per grans volums de dades heterogenis. En efecte, el 90% de les dades mundials han sigut generades en els últims dos anys. Per tal de dur a terme aquestes tasques d’explotació de dades, les organitzacions primer han de realitzar una integració de les dades, combinantles a partir de diferents fonts amb l’objectiu de tenir-ne una vista unificada d’elles. Per això, aquest fet requereix reconsiderar les assumpcions tradicionals en integració amb l’objectiu de lidiar amb els requisits imposats per aquests sistemes de tractament massiu de dades. Aquesta tesi doctoral té com a objectiu proporcional un nou marc de treball per a la integració de dades en el context de sistemes de tractament massiu de dades, el qual implica lidiar amb una gran quantitat de dades heterogènies, provinents de múltiples fonts i en el seu format original. Per això, proposem un procés d’integració compost d’una seqüència d’activitats governades per una capa semàntica, la qual és implementada a partir d’un repositori de metadades compartides. Des d’una perspectiva d’administració, aquestes activitats són el desplegament d’una arquitectura d’integració de dades, seguit per la inserció d’aquestes metadades compartides. Des d’una perspectiva de consum de dades, les activitats són la integració virtual i materialització de les dades, la primera sent una tasca exploratòria i la segona una de consolidació. Seguint el marc de treball proposat, ens centrem en proporcionar contribucions a cada una de les quatre activitats. La tesi inicia proposant una arquitectura de referència de software per a sistemes de tractament massiu de dades amb coneixement semàntic. Aquesta arquitectura serveix com a planell per a desplegar un conjunt de sistemes, sent el repositori de metadades al seu nucli. Posteriorment, proposem un model basat en grafs per a la gestió de metadades. Concretament, ens centrem en donar suport a l’evolució d’esquemes i fonts de dades, un dels factors predominants en les fonts de dades heterogènies considerades. Per a l’integració virtual, proposem algorismes de rescriptura de consultes que usen el model de metadades previament proposat. Com a afegitó, considerem heterogeneïtat semàntica en les fonts de dades, les quals els algorismes de rescriptura poden resoldre automàticament. Finalment, la tesi es centra en l’activitat d’integració materialitzada. Per això proposa un mètode per a seleccionar els resultats intermedis a materialitzar un fluxes de tractament intensiu de dades. En general, els resultats d’aquesta tesi serveixen com a contribució al camp d’integració de dades en els ecosistemes de tractament massiu de dades contemporanisLes données ont un impact indéniable sur la société. Le stockage et le traitement de grandes quantités de données disponibles constituent actuellement l’un des facteurs clés de succès d’une entreprise. Néanmoins, nous assistons récemment à un changement représenté par des quantités de données massives et hétérogènes. En effet, 90% des données dans le monde ont été générées au cours des deux dernières années. Ainsi, pour mener à bien ces tâches d’exploitation des données, les organisations doivent d’abord réaliser une intégration des données en combinant des données provenant de sources multiples pour obtenir une vue unifiée de ces dernières. Cependant, l’intégration de quantités de données massives et hétérogènes nécessite de revoir les hypothèses d’intégration traditionnelles afin de faire face aux nouvelles exigences posées par les systèmes de gestion de données massives. Cette thèse de doctorat a pour objectif de fournir un nouveau cadre pour l’intégration de données dans le contexte d’écosystèmes à forte intensité de données, ce qui implique de traiter de grandes quantités de données hétérogènes, provenant de sources multiples et dans leur format d’origine. À cette fin, nous préconisons un processus d’intégration constitué d’activités séquentielles régies par une couche sémantique, mise en oeuvre via un dépôt partagé de métadonnées. Du point de vue de la gestion, ces activités consistent à déployer une architecture d’intégration de données, suivies de la population de métadonnées partagées. Du point de vue de la consommation de données, les activités sont l’intégration de données virtuelle et matérialisée, la première étant une tâche exploratoire et la seconde, une tâche de consolidation. Conformément au cadre proposé, nous nous attachons à fournir des contributions à chacune des quatre activités. Nous commençons par proposer une architecture logicielle de référence pour les systèmes de gestion de données massives et à connaissance sémantique. Une telle architecture consiste en un schéma directeur pour le déploiement d’une pile de systèmes, le dépôt de métadonnées étant son composant principal. Ensuite, nous proposons un modèle de métadonnées basé sur des graphes comme formalisme pour la gestion des métadonnées. Nous mettons l’accent sur la prise en charge de l’évolution des schémas et des sources de données, facteur prédominant des sources hétérogènes sous-jacentes. Pour l’intégration virtuelle, nous proposons des algorithmes de réécriture de requêtes qui s’appuient sur le modèle de métadonnées proposé précédemment. Nous considérons en outre les hétérogénéités sémantiques dans les sources de données, que les algorithmes proposés sont capables de résoudre automatiquement. Enfin, la thèse se concentre sur l’activité d’intégration matérialisée et propose à cette fin une méthode de sélection de résultats intermédiaires à matérialiser dans des flux des données massives. Dans l’ensemble, les résultats de cette thèse constituent une contribution au domaine de l’intégration des données dans les écosystèmes contemporains de gestion de données massivesPostprint (published version

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

The Multi-Agent Transport Simulation MATSim

"The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.

Semantic Web and Adaptivity: Towards a New Model

AbstrAct The chapter aims at proposing a model that gives an abstraction to the functionalities and data involved in adaptive applications for the Semantic Web. As the quantity of provided information on the Web is getting larger, the need for adaptation in software is getting more and more necessary in order to maximize the productivity of individuals, and more issues are emerging that have to be considered in the new generation of hypertext systems. With the advent of Semantic Web, adaptation can be performed autonomously and in runtime, making the whole process of information adapting transparent to the user

Scalable Ontological EAI and e-Business Integration

Integration of enterprise applications (EAI) and e-business integration are time-consuming and expensive. This thesis proposes pattern mining to determine identical object classes. Processes are integrated based on declared integration goals and known software behavior. A model-driven approach ensures consistent use of behavioral knowledge from development in integration. The contributions were applied to the CCTS Modeler Warp 10 and SAP NetWeaver CE (composition environment) developed at SAP