A hybrid job-shop scheduling system

The intention of the scheduling system developed at the Fraunhofer-Institute for Material Flow and Logistics is the support of a scheduler working in a job-shop. Due to the existing requirements for a job-shop scheduling system the usage of flexible knowledge representation and processing techniques is necessary. Within this system the attempt was made to combine the advantages of symbolic AI-techniques with those of neural networks

Types and Systems of Actors in Regional Development: Their Function and Regulatory Potential

Differentiation of actors derives primarily from the distribution of power and wealth in a society, and thus it has always played an exceptionally significant role. However, as a consequence of the hierarchical organisation of the society this differentiation was noticeably asymmetrical. The non-equivalence of partial subjects/actors of regional development has led to the understandable domination of ‘deterministic’ relations and the plurality of interests and then to the dominance of ‘competitive’ relations. Only gradually do interactions of a cooperative kind successively break through the growth of mutual interconnections, linkages and necessity of social elements and partial systems, and thus the increasing of organic nature of (geo)societal systems. This will be finally illustrated through the difference between ‘symmetric’ systems of actors in developed countries and the ‘asymmetric’ global system

Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges

Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to high complexity, interaction, parallelism and continuous change of roles and organisation between agents. In this paper we record our research experience on formal modelling of MAS. We review our research throughout the last decade, by describing the problems we have encountered and the decisions we have made towards resolving them and providing solutions. Much of this work involved membrane computing and classes of P Systems, such as Tissue and Population P Systems, targeted to the modelling of MAS whose dynamic structure is a prominent characteristic. More particularly, social insects (such as colonies of ants, bees, etc.), biology inspired swarms and systems with emergent behaviour are indicative examples for which we developed formal MAS models. Here, we aim to review our work and disseminate our findings to fellow researchers who might face similar challenges and, furthermore, to discuss important issues for advancing research on the application of membrane computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

The Widening and Deepening of Innovation Policy: What Conditions Provide for Effective Governance?

In relation to the gradual and steady introduction of the systemic perspective and of new public management techniques in innovation policy-making during the past decade, many countries in the developed and developing world have been substantially widening and deepening their innovation policies. The introduction of new and more sophisticated policy instruments (deepening) has been accompanied by an expansion of the realm of action for innovation policy (widening). The main argument of this paper is that this remarkable governmental activism and experimentalism raises important analytical questions about the conditions under which innovation policy contributes to an effective governance of the innovation system. Hence, this paper has two main purposes. Firstly, it characterises in an unambiguous way the widening and deepening trends in innovation policy, problematising their possible effects on governance. And, secondly, it develops an analytical toolbox based on a series of theoretical assumptions about the political conditions for effective governance of innovation systems.Innovation Policy; Innovation System; Governmental Activism; Governmental Experimentalism

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online