Soundar R. T. Kumara

Near linear time algorithm to detect community structures in large-scale networks

Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.

Supply-chain networks: a complex adaptive systems perspective

In this era, information technology is revolutionizing almost every domain of technology and society, whereas the ‘complexity revolution’ is occurring in science at a silent pace. In this paper, we look at the impact of the two, in the context of supply-chain networks. With the advent of information technology, supply chains have acquired a complexity almost equivalent to that of biological systems. However, one of the major challenges that we are facing in supply-chain management is the deployment of coordination strategies that lead to adaptive, flexible and coherent collective behaviour in supply chains. The main hurdle has been the lack of the principles that govern how supply chains with complex organizational structure and function arise and develop, and what organizations and functionality are attainable, given specific kinds of lower-level constituent entities. The study of Complex Adaptive Systems (CAS), has been a research effort attempting to find common characteristics and/or formal distinctions among complex systems arising in diverse domains (like biology, social systems, ecology and technology) that might lead to a better understanding of how complexity occurs, whether it follows any general scientific laws of nature, and how it might be related to simplicity. In this paper, we argue that supply chains should be treated as a CAS. With this recognition, we propose how various concepts, tools and techniques used in the study of CAS can be exploited to characterize and model supply-chain networks. These tools and techniques are based on the fields of nonlinear dynamics, statistical physics and information theory.

Effective Web Service Composition in Diverse and Large-Scale Service Networks

The main research focus of Web services is to achieve the interoperability between distributed and heterogeneous applications. Therefore, flexible composition of Web services to fulfill the given challenging requirements is one of the most important objectives in this research field. However, until now, service composition has been largely an error-prone and tedious process. Furthermore, as the number of available web services increases, finding the right Web services to satisfy the given goal becomes intractable. In this paper, toward these issues, we propose an AI planning-based framework that enables the automatic composition of Web services, and explore the following issues. First, we formulate the Web-service composition problem in terms of AI planning and network optimization problems to investigate its complexity in detail. Second, we analyze publicly available Web service sets using network analysis techniques. Third, we develop a novel Web-service benchmark tool called WSBen. Fourth, we develop a novel AI planning-based heuristic Web-service composition algorithm named WSPR. Finally, we conduct extensive experiments to verify WSPR against state-of-the-art AI planners. It is our hope that both WSPR and WSBen will provide useful insights for researchers to develop Web-service discovery and composition algorithms, and software.

Survivability of multiagent-based supply networks: a topological perspect

Supply chains involve complex Webs of interactions among suppliers, manufacturers, distributors, third-party logistics providers, retailers, and customers. Although fairly simple business processes govern these individual entities, real-time capabilities and global Internet connectivity make todays supply chains complex. Survivability is a critical factor in supply network design. Specifically, supply networks in dynamic environments, such as military supply chains during wartime, must be designed more for survivability than for cost effectiveness. We present a methodology for building survivable large-scale supply network topologies that can extend to other large-scale MASs. To create survivable-and hence dependable-multiagent systems, we must also consider the interplay between network topology and node functionalities.

A comparative illustration of AI planning-based web services composition

As the number of available web services proliferates, finding right web services to fulfill a given goal becomes an important task. In particular, a problem of combining multiple web services to satisfy a single task, known as web services composition problem, has received much attention recently, and various solutions have been proposed. Among many proposed solutions, however, it is not clear to use which one in what scenarios. In this paper, to this end, we present: (1) a taxonomy and decision guideline of available solution spaces; (2) an overview of syntactic and semantic matching approaches, and (3) a comparative illustration of three representative solutions from the perspective of e-service workflows.

Web Service Planner (WSPR): An Effective and Scalable Web Service Composition Algorithm

As the emergence of service-oriented architecture provides a major boost for e-commerce agility, the number of available Web services is rapidly increasing. However, when there are a large number of Web services available and no single Web service satisfies the given request, one has to “compose†multiple Web services to fulfill the goal. In this article, toward this problem, we present an AI planning-based Web service composition algorithm named as WSPR. We evaluate the efficiency and effectiveness of WSPR using two publicly available test sets—EEE05 and ICEBE05. In addition, we analyze the two test sets and suggest several improvements to benchmark Web service composition better.

A Methodology for Product Family Ontology Development using Formal Concept Analysis and Web Ontology Language

The use of ontologies for information sharing is well documented in the literature, but the lack of a comprehensive and systematic methodology for constructing product ontologies has limited the process of developing ontologies for design artifacts. In this paper we introduce the Product Family Ontology Development Methodology (PFODM), a novel methodology to develop formal product ontologies using the Semantic Web paradigm. Within PFODM, Formal Concept Analysis (FCA) is used first to identify similarities among a finite set of design artifacts based on their properties and then to develop and refine a product family ontology using Web Ontology Language (OWL). A family of seven one-time-use cameras is used to demonstrate the steps of the PFODM to construct such an ontology. The benefit of PFODM lies in providing a systematic and consistent methodology for constructing ontologies to support product family design. The resulting ontologies provide a hierarchical conceptual clustering of related design artifacts, which is particularly advantageous for product family design where parts, processes, and most important, information is intentionally shared and reused to reduce complexity, lead-time, and development costs. Potential uses of the resulting ontologies and FCA representations within product family design are also discussed.

Intelligent Computing Methods for Manufacturing Systems

Abstract Intelligent computation is taken to include the development and application of artificial intelligence (Al) methods i.e. tools that exhibit characteristics associated with intelligence in human behaviour. Many approaches have been proposed to apply Al methods, techniques and paradigms to the solution of manufacturing problems. This paper discusses current trends in applications of intelligent computing tools to manufacturing and reviews the motivation and basis for the utilisation of these systems. The topics of the paper were confined to four main issues of manufacturing systems: design, planning, production and system level activities. A discussion about intelligent manufacturing systems from these four basic functional view points was introduced, the relevant intelligent computing methods and their use in manufacturing were surveyed, and a number of significant research issues and applications were illustrated. The main developments that were observed comprise the integration of Al methods into CAD, CAPP, etc.; the improvement of the performance of present Al techniques; the development of hybrid Al systems; the elaboration and application of new Al paradigms in manufacturing. Intelligent systems in the future are expected to be integrated, modular, and hybrid in nature, and they may well include all the techniques described in this paper and further more.

Multiagent based dynamic resource scheduling for distributed multiple projects using a market mechanism

The resource scheduling problem in a multi-project environment extends job-shop scheduling problems by allowing for task dependency and multiple self-interested entities. In this paper we deal with short-term scheduling of resources, which are shared by multiple projects. In specific, we address the dynamic nature of the situation. We model this as a dynamic economy, where the multiple local markets are established and cleared over time, trading resource time slots (goods). Due to the dynamic and distributed nature of the economy, through our approach we can achieve higher levels of flexibility, scalability and adaptability. Unlike most market-based mechanisms, which are based on equilibrium concepts and iterative adjustment of resources prices, we propose a novel market mechanism called precedence cost tâtonnement (P-TâTO), which solves individual resource-constrained local resource scheduling in an optimal way, and searches for a precedence conflict-free schedule through a tâtonnement type procedure. In this paper, we discuss our dynamic economy model and some details of the market mechanism along with empirical analysis results.

Manufacturing in the Digital Age: Exploiting Information Technologies for Product Realization

Information technology (IT) is both the key enabler for future manufacturing enterprises and a transformer of organizations and markets. By reducing barriers to collaboration, compressing lead time, eliminating physical movement, and enriching decision-making, IT helps manufacturers achieve their goal of meeting customer needs better, quicker, and cheaper. By providing global reach and easy connectivity, information technology has fostered cooperation while increasing market competition, and heightened customer expectations. Advances in computer and communication technologies combined with rapid changes in organizations have created new opportunities for exploiting information technologies in the entire product realization process. This paper explores these opportunities, and identifies promising directions for both basic and applied research. We first review important trends in organizations, markets, and information technologies—from increasing customer involvement and opportunistic organizational alliances to global reach and connectivity, enterprise integration, and virtualization. Adopting a process viewpoint of the product realization cycle, we translate these trends into high-impact IT applications in design and operations that offer rich potential for applied research and development. Underlying these applications are four broad classes of intelligent information processes—intelligent search, diagnosis and prognosis, collaboration, coordination, and negotiation, and understanding and learning. And, software agents provide an ideal platform to implement these processes. We briefly review developments in these basic research fields, and identify necessary scientific advances that are most important from the manufacturing perspective. Our goal is to synthesize streams of thought from many related disciplines in engineering, science, and management, and develop a framework for examining how information technologies can facilitate and influence manufacturing.