This work examines the importance of interorganizational supply chain relationship from a network perspective. We have tried to find the answer of the following questions; how does knowledge sharing effect network value in an inter-organizational business network? What is the influence of knowledge sharing in creating new knowledge? This paper makes a significant and substantial contribution to knowledge generation on a number of fronts. Firstly, the study develops a substantive theory to understand the overall network level value creation in an inter-organizational business network. In recent years, a core theme within the value delivery attracts attention is supply or value chain management. In this work we have examined; which actors in the chain create value and which delivery process provides the best value for which customers? Secondly, the study contributes the better understanding of the influence of tacit knowledge sharing or experience sharing to create the network value. The role of the interaction has also explored regarding the new knowledge creation. Thirdly, the importance of “time” is considered in a different dimension for interaction process. As such, the study’s contribution consists of developing a rigorous framework to unpack the new theory of network level value creation, adding a new layer to the analysis through the role of knowledge sharing among the network member organizations, providing greater understanding about the knowledge sharing, new network knowledge creation and the overall network value for the whole business network.
We study the resource-bounded autonomous agents acting in complex multi-agent and multi-task environments. In particular, we investigate various models of the agent action selection in such environments. Designing such autonomous decision making agents for non-episodic and partially observable dynamic environments is particularly challenging, due to a high-level demand such environments pose in terms of agent’s necessary capabilities. We make an early attempt in modeling and designing agents for largescale multi-agent systems and complex environments, where individual agent’s behaviors are sufficiently simple to be scalable and practical, and where agents’ coordination and self-organization capabilities can still make agents (both individually and as ensembles) effective with respect to accomplishing their goals. The emphasis in this paper is on models of an agent’s local knowledge based individual behaviors. We propose several simple mathematical models for an agent’s local knowledge-based action selection. We illustrate the general ideas about bounded-resource autonomous agents acting in multi-agent, multi-task dynamic environments, and the proposed generic models of agents’ autonomous local-knowledge based action selection in such environments, with a concrete application example: modeling and simulation of a collection of autonomous unmanned aerial vehicles (UAVs) on a multi-task mission.
Process Patterns is an emergent approach and a valuable means for capitalization, reuse and management of process experiences and best practices. In the context of software engineering process, many formalisms and languages have been proposed to describe software process patterns. This multiplicity makes capitalization and/or reuse of process patterns, difficult to be achieved. This paper presents a comparative study of process pattern formalisms proposed in the literature and addresses some reflections to deal with problems arising from this survey, in order to propose a new framework for process patterns’ capitalization and reuse. Keywords— Software process patterns, process patterns’ description languages, patterns’ unification ontology, pattern warehousing, pattern reuse, pattern capitalization, pattern mining.
