Conceptual Architecture for Agent-Based Modelling of Supplier Selection Conducted by a Supply Chain Dyad

Within the fourth stage of industrialization, artificial intelligence and in particular the multi-agent systems paradigm is highly adopted. Within the agent approach, the industrial resources are defined as intelligent agents that negotiate with each other to implement dynamic reconfiguration and reach agility and higher customer satisfaction. In this paper a smart configuration of the agent-based system for multi-product dyadic supplier selection is proposed. The objective is to select suppliers for multiple products simultaneously in a vertical collaboration context while involving the customer of the purchasing company and considering its preferences. Negotiation experiments are conducted for initial validation of the proposed conceptual architecture

An Approach of Decision-Making Support Based on Collaborative Agents for a Large Distribution Sector

International audienceThis paper applies the multi-agent systems paradigm to collaborative coordination and negotiation in a global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders for which the quantity of product cannot be delivered partially or completely from the available inventory. This type of orders can be generated by unexpected swings in demand or unexpected exceptions (problem of production, problem of transportation, etc.). This paper proposes a first architecture and discusses an industrial case study

Agent-based distributed manufacturing control: a state-of-the-art survey

Manufacturing has faced significant changes during the last years, namely the move from a local economy towards a global and competitive economy, with markets demanding for highly customized products of high quality at lower costs, and with short life cycles. In this environment, manufacturing enterprises, to remain competitive, must respond closely to customer demands by improving their flexibility and agility, while maintaining their productivity and quality. Dynamic response to emergence is becoming a key issue in manufacturing field because traditional manufacturing control systems are built upon rigid control architectures, which cannot respond efficiently and effectively to dynamic change. In these circumstances, the current challenge is to develop manufacturing control systems that exhibit intelligence, robustness and adaptation to the environment changes and disturbances. The introduction of multi-agent systems and holonic manufacturing systems paradigms addresses these requirements, bringing the advantages of modularity, decentralization, autonomy, scalability and re- usability. This paper surveys the literature in manufacturing control systems using distributed artificial intelligence techniques, namely multi-agent systems and holonic manufacturing systems principles. The paper also discusses the reasons for the weak adoption of these approaches by industry and points out the challenges and research opportunities for the future

Coalition based approach for shop floor agility – a multiagent approach

Dissertation submitted for a PhD degree in Electrical Engineering, speciality of Robotics and Integrated Manufacturing from the Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaThis thesis addresses the problem of shop floor agility. In order to cope with the disturbances and uncertainties that characterise the current business scenarios faced by manufacturing companies, the capability of their shop floors needs to be improved quickly, such that these shop floors may be adapted, changed or become easily modifiable (shop floor reengineering). One of the critical elements in any shop floor reengineering process is the way the control/supervision architecture is changed or modified to accommodate for the new processes and equipment. This thesis, therefore, proposes an architecture to support the fast adaptation or changes in the control/supervision architecture. This architecture postulates that manufacturing systems are no more than compositions of modularised manufacturing components whose interactions when aggregated are governed by contractual mechanisms that favour configuration over reprogramming. A multiagent based reference architecture called Coalition Based Approach for Shop floor Agility – CoBASA, was created to support fast adaptation and changes of shop floor control architectures with minimal effort. The coalitions are composed of agentified manufacturing components (modules), whose relationships within the coalitions are governed by contracts that are configured whenever a coalition is established. Creating and changing a coalition do not involve programming effort because it only requires changes to the contract that regulates it

SCS: 60 years and counting! A time to reflect on the Society's scholarly contribution to M&S from the turn of the millennium.

The Society for Modeling and Simulation International (SCS) is celebrating its 60th anniversary this year. Since its inception, the Society has widely disseminated the advancements in the field of modeling and simulation (M&S) through its peer-reviewed journals. In this paper we profile research that has been published in the journal SIMULATION: Transactions of the Society for Modeling and Simulation International from the turn of the millennium to 2010; the objective is to acknowledge the contribution of the authors and their seminal research papers, their respective universities/departments and the geographical diversity of the authors' affiliations. Yet another objective is to contribute towards the understanding of the overall evolution of the discipline of M&S; this is achieved through the classification of M&S techniques and its frequency of use, analysis of the sectors that have seen the predomination application of M&S and the context of its application. It is expected that this paper will lead to further appreciation of the contribution of the Society in influencing the growth of M&S as a discipline and, indeed, in steering its future direction

Cyber-physical systems in food production chain

The article reviews the state-of-the-science in the field of cyber-physical systems (CPSs). CPSs are intelligent systems that include physical, biological and computational components using engineering networks. CPSs are able to integrate into production processes, improve the exchange of information between industrial equipment, qualitatively transform production chains, and effectively manage business and customers. This is possible due to the ability of CPSs to manage ongoing processes through automatic monitoring and controlling the entire production process and adjusting the production to meet customer preferences. A comprehensive review identified key technology trends underlying CPSs. These are artificial intelligence, machine learning, big data analytics, augmented reality, Internet of things, quantum computing, fog computing, 3D printing, modeling and simulators, automatic object identifiers (RFID tags). CPSs will help to improve the control and traceability of production operations: they can collect information about raw materials, temperature and technological conditions, the degree of food product readiness, thereby increasing the quality of food products. Based on the results, terms and definitions, and potential application of cyber-physical systems in general and their application in food systems in particular were identified and discussed with an emphasis on food production (including meat products).The article reviews the state-of-the-science in the field of cyber-physical systems (CPSs). CPSs are intelligent systems that include physical, biological and computational components using engineering networks. CPSs are able to integrate into production processes, improve the exchange of information between industrial equipment, qualitatively transform production chains, and effectively manage business and customers. This is possible due to the ability of CPSs to manage ongoing processes through automatic monitoring and controlling the entire production process and adjusting the production to meet customer preferences. A comprehensive review identified key technology trends underlying CPSs. These are artificial intelligence, machine learning, big data analytics, augmented reality, Internet of things, quantum computing, fog computing, 3D printing, modeling and simulators, automatic object identifiers (RFID tags). CPSs will help to improve the control and traceability of production operations: they can collect information about raw materials, temperature and technological conditions, the degree of food product readiness, thereby increasing the quality of food products. Based on the results, terms and definitions, and potential application of cyber-physical systems in general and their application in food systems in particular were identified and discussed with an emphasis on food production (including meat products)

Ontoloji tabanlı çok-etmenli sanal fabrika sisteminin tasarımı ve geliştirilmesi.

Major developments in computers and information technologies, enable industrial and mechanical engineers to establish new net based, virtual collaboration platforms for enterprises. Benefiting from virtual enterprise platform enterprises will be able to combine their resources and capabilities on project based collaborations meanwhile protect their independent mainstream policies and secure their secret information. This concept is called virtual enterprise(VE). Virtual Enterprise (VE) is a collaboration model between multiple business partners in a value chain. The VE model is particularly feasible and appropriate for Small and Medium Enterprises (SME) and industry parks containing multiple SMEs that have different vertical competencies. One of the main targets of this research is to create an Ontology based Multi Agent Virtual Enterprise (OMAVE) System to prepare an appropriate platform for collaboration between technology start-ups in techno-parks and SMEs in Organized Industrial Zones in order to produce high value added high-tech products. OMAVE aims to help SMEs to shift from classic trend of manufacturing part pieces towards high-tech, innovative and research based products. In this way and to reach this goal a new semantic data infrastructure to enhance Re-Configurability and Flexibility of virtual enterprise systems has been developed. In order to support flexibility in Virtual Enterprise business processes and enhance its integration to enterprises' available manufacturing systems (e.g. MRP) an ontology based domain model of VE system has been established. OWL DL semantic data structure of VE by defining concepts, axioms, rules and functions in VE system has been developed. TDB data store to keep VE data and information in form of triples developed. SPARQL semantic RDF query language is used to handle and manipulate data on developed system data store. This architecture supports structure flexibility for developed VE infrastructure and improve reusability of data and knowledge in VE life cycle. To establish a multi agent based partner selection platform different agent types have been developed. These agents collaborate and compete with each other to select the most appropriate partner for the forthcoming VE project consortium. The agent based auctioning platform is coupled with a Fuzzy-AHP-TOPSIS multi criteria decision making algorithm to evaluate incoming bids from agents and rank proposals in each iteration. It is also important to notice that here, agents interaction's semantic is provided by an agent ontology. This agent ontology provides concepts, properties and all message formats for agents to settle a common language in interactions between agents. Implementing concurrent engineering, collaborative design and Product Life Cycle Management (PLM) concepts by integrating Dassault systems web based CATIA/ENOVIA V6 design and PLM tools to OMAVE system. To test and verify these achievements a case study to produce a test product by using developed OMAVE tools is established. This test product manufactured by contributions of SMEs from OSTIM organized Industrial Zone Aviation and Defense Cluster.Ph.D. - Doctoral Progra

Design and implementation of a multi-agent opportunistic grid computing platform

Opportunistic Grid Computing involves joining idle computing resources in enterprises into a converged high performance commodity infrastructure. The research described in this dissertation investigates the viability of public resource computing in offering a plethora of possibilities through seamless access to shared compute and storage resources. The research proposes and conceptualizes the Multi-Agent Opportunistic Grid (MAOG) solution in an Information and Communication Technologies for Development (ICT4D) initiative to address some limitations prevalent in traditional distributed system implementations. Proof-of-concept software components based on JADE (Java Agent Development Framework) validated Multi-Agent Systems (MAS) as an important tool for provisioning of Opportunistic Grid Computing platforms. Exploration of agent technologies within the research context identified two key components which improve access to extended computer capabilities. The first component is a Mobile Agent (MA) compute component in which a group of agents interact to pool shared processor cycles. The compute component integrates dynamic resource identification and allocation strategies by incorporating the Contract Net Protocol (CNP) and rule based reasoning concepts. The second service is a MAS based storage component realized through disk mirroring and Google file-system’s chunking with atomic append storage techniques. This research provides a candidate Opportunistic Grid Computing platform design and implementation through the use of MAS. Experiments conducted validated the design and implementation of the compute and storage services. From results, support for processing user applications; resource identification and allocation; and rule based reasoning validated the MA compute component. A MAS based file-system that implements chunking optimizations was considered to be optimum based on evaluations. The findings from the undertaken experiments also validated the functional adequacy of the implementation, and show the suitability of MAS for provisioning of robust, autonomous, and intelligent platforms. The context of this research, ICT4D, provides a solution to optimizing and increasing the utilization of computing resources that are usually idle in these contexts