234 research outputs found

    From supply chains to demand networks. Agents in retailing: the electrical bazaar

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    A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

    Efficient Communication in Agent-based Autonomous Logistic Processes

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    Transportation of goods plays a vital role for the success of a logistics network. The ability to transport goods quickly and cost effectively is one of the major requirements of the customers. Dynamics involved in the logistics process like change or cancellation of orders or uncertain information about the orders add to the complexity of the logistic network and can even reduce the efficiency of the entire logistics process. This brings about a need of integrating technology and making the system more autonomous to handle these dynamics and to reduce the complexity. Therefore, the distributed logistics routing protocol (DLRP) was developed at the University of Bremen. In this thesis, DLRP is extended with the concept of clustering of transport goods, two novel routing decision schemes and a negotiation process between the cluster of goods and the vehicle. DLRP provides the individual logistic entities the ability to perform routing tasks autonomously e.g., discovering the best route to the destination at the given time. Even though DLRP seems to solve the routing problem in real-time, the amount of message flooding involved in the route discovery process is enormous. This motivated the author to introduce a cluster-based routing approach using software agents. The DLRP along with the clustering algorithm is termed as the cluster-based DLRP. In the latter, the goods are first clustered into groups based on criteria such as the common destination. The routing is now handled by the cluster head rather than the individual transport goods which results in a reduced communication volume in the route discovery. The latter is proven by evaluating the performance of the cluster-based DLRP approach compared to the legacy DLRP. After the routing process is completed by the cluster heads, the next step is to improve the transport performance in the logistics network by identifying the best means to transport the clustered goods. For example, to have better utilization of the transport capacity, clusters can be transported together on a stretch of overlapping route. In order to make optimal transport decisions, the vehicle calculates the correlation metric of the routes selected by the various clusters. The correlation metric aids in identifying the clusters which can be transported together and thereby can result in better utilization of the transport resources. In turn, the transportation cost that has to be paid to the vehicle can be shared between the different clusters. The transportation cost for a stretch of route is calculated by the vehicle and offered to the cluster. The latter can decide based upon the transportation cost or the selected route whether to accept the transport offer from the vehicle or not. In this regard, different strategies are developed and investigated. Thereby a performance evaluation of the capacity utilization of the vehicle and the transportation cost incurred by the cluster is presented. Finally, the thesis introduces the concept of negotiation in the cluster based routing methods. The negotiation process enhances the transport decisions by giving the clusters and the vehicles the flexibility to negotiate the transportation cost. Thus, the focus of this part of the thesis is to analyse the negotiation strategies used by the logistics entities and their role in saving negotiation time while achieving a favorable transportation cost. In this regard, a performance evaluation of the different proposed strategies is presented, which in turn gives the logistics practitioners an overview of the best strategy to be deployed in various scenarios. Clustering of goods aid in the negotiation process as on the one hand, a group of transport goods have a stronger basis for negotiation to achieve a favorable transportation price from the vehicle. On the other hand it makes it easier for the vehicle to select the packages for transport and helps the vehicle to operate close to its capacity. In addition, clustering enables the negotiation process to be less complex and voluminous. From the analytical considerations and obtained results in the three parts of this thesis, it can be concluded that efficient transport decisions, though very complex in a logistics network, can be simplified to a certain extent utilizing the available information of the goods and vehicles in the network

    Interactive Multiagent Adaptation of Individual Classification Models for Decision Support

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    An essential prerequisite for informed decision-making of intelligent agents is direct access to empirical knowledge for situation assessment. This contribution introduces an agent-oriented knowledge management framework for learning agents facing impediments in self-contained acquisition of classification models. The framework enables the emergence of dynamic knowledge networks among benevolent agents forming a community of practice in open multiagent systems. Agents in an advisee role are enabled to pinpoint learning impediments in terms of critical training cases and to engage in a goal-directed discourse with an advisor panel to overcome identified issues. The advisors provide arguments supporting and hence explaining those critical cases. Using such input as additional background knowledge, advisees can adapt their models in iterative relearning organized as a search through model space. An extensive empirical evaluation in two real-world domains validates the presented approach

    SAJaS: enabling JADE-based simulations

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    Multi-agent systems (MAS) are widely acknowledged as an appropriate modelling paradigm for distributed and decentralized systems, where a (potentially large) number of agents interact in non-trivial ways. Such interactions are often modelled defining high-level interaction protocols. Open MAS typically benefit from a number of infrastructural components that enable agents to discover their peers at run-time. On the other hand, multi-agent-based simulations (MABS) focus on applying MAS to model complex social systems, typically involving a large agent population. Several MAS development frameworks exist, but they are often not appropriate for MABS; and several MABS frameworks exist, albeit sharing little with the former. While open agent-based applications benefit from adopting development and interaction standards, such as those proposed by FIPA, MABS frameworks typically do not support them. In this paper, a proposal to bridge the gap between MAS simulation and development is presented, including two components. The Simple API for JADE-based Simulations (SAJaS) enhances MABS frameworks with JADE-based features. While empowering MABS modellers with modelling concepts offered by JADE, SAJaS also promotes a quicker development of simulation models for JADE programmers. In fact, the same implementation can, with minor changes, be used as a large scale simulation or as a distributed JADE system. In its current version, SAJaS is used in tandem with the Repast simulation framework. The second component of our proposal consists of a MAS Simulation to Development (MASSim2Dev) tool, which allows the automatic conversion of a SAJaS-based simulation into a JADE MAS, and vice-versa. SAJaS provides, for certain kinds of applications, increased simulation performance. Validation tests demonstrate significant performance gains in using SAJaS with Repast when compared with JADE, and show that the usage of MASSim2Dev preserves the original functionality of the system. © Springer-Verlag Berlin Heidelberg 2015

    Dynamics in Logistics

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    This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

    Air Force Institute of Technology Research Report 2001

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    This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

    A Scalable Runtime Platform for Multiagent-Based Simulation

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    Abstract. Using purely agent-based platforms for any kind of simulation requires to address the following challenges: (1) scalability (efficient scheduling of agent cycles is difficult), (2) efficient memory management (when and which data should be fetched, cached, or written to/from disk), and (3) modelling (no generally accepted meta-models exist: what are essential concepts, what just implementation details?). While dedicated professional simulation tools usually provide rich domain libraries and advanced visualisation techniques, and support the simulation of large scenarios, they do not allow for "agentization" of single components. We are trying to bridge this gap by developing a distributed, scalable runtime platform for multiagent simulation, MASeRaTi, addressing the three problems mentioned above. It allows to plug-in both dedicated simulation tools (for the macro view ) as well as the agentization of certain components of the system (to allow a micro view ). If no agent-related features are used, its performance should be as close as possible to the legacy system used

    Dynamics in Logistics

    Get PDF
    This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

    Modeling & Simulation Education for the Acquisition and T&E Workforce: FY07 Deliverable Package

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    This report was prepared for CAPT Mike Lilienthal, PhD, CPE, and funded by ASN (RDA) CHENG and the Modeling and Simulation Coordination Office (MSCO).This technical report presents the deliverables for calendar year 2007 for the "Educating the Modeling and Simulation Workforce" project performed for the DoD Modeling and Simulation Steering Committee. It includes the results for spirals one and two. Spiral one is an analysis of the educational needs of the program manager, systems engineer, and test and evaluation workforces against a set of educational skill requirements developed by the project team. This is referred to as the 'learning matrix'. Spiral two is a set of module and course matrices, along with delivery options, that meets the educational needs indentified in spiral one. This is referred to as the 'learning architecture'. Supporting materials, such as case studies and a handbook, are included. These documents serve as the design framework for spirals three and four, to be completed in CY2008, and which involve the actual production and testing of the courses in the learning architecture and their longitudinal assessment. This report includes the creative work of a seven university consortium and a group of M&S stake-holders, together comprising over 60 personnel.ASN (RDA) CHENG and the Modeling and Simulation Coordination Office (MSCO).This report was prepared for CAPT Mike Lilienthal, PhD, CPE, and funded by ASN (RDA) CHENG and the Modeling and Simulation Coordination Office (MSCO)
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