Models@run.time:foundations, applications, and roadmaps

Traditionally, research on model-driven engineering (MDE) has mainly focused on the use of models at the design, implementation, and verification stages of development. This work has produced relatively mature techniques and tools that are currently being used in industry and academia. However, software models also have the potential to be used at runtime, to monitor and verify particular aspects of runtime behavior, and to implement self-* capabilities (e.g., adaptation technologies used in self-healing, self-managing, self-optimizing systems). A key benefit of using models at runtime is that they can provide a richer semantic base for runtime decision-making related to runtime system concerns associated with autonomic and adaptive systems. This book is one of the outcomes of the Dagstuhl Seminar 11481 on models@run.time held in November/December 2011, discussing foundations, techniques, mechanisms, state of the art, research challenges, and applications for the use of runtime models. The book comprises four research roadmaps, written by the original participants of the Dagstuhl Seminar over the course of two years following the seminar, and seven research papers from experts in the area. The roadmap papers provide insights to key features of the use of runtime models and identify the following research challenges: the need for a reference architecture, uncertainty tackled by runtime models, mechanisms for leveraging runtime models for self-adaptive software, and the use of models at runtime to address assurance for self-adaptive systems.

Green operations initiatives in the automotive industry:an environmental reports analysis and benchmarking study

Purpose: The purpose of this paper is to focus on investigating and benchmarking green operations initiatives in the automotive industry documented in the environmental reports of selected companies. The investigation roadmaps the main environmental initiatives taken by the world's three major car manufacturers and benchmarks them against each other. The categorisation of green operations initiatives that is provided in the paper can also help companies in other sectors to evaluate their green practices. Design/methodology/approach: The first part of the paper is based on existing literature on the topic of green and sustainable operations and the "unsustainable" context of automotive production. The second part relates to the roadmap and benchmarking of green operations initiatives based on an analysis of secondary data from the automotive industry. Findings: The findings show that the world's three major car manufacturers are pursuing various environmental initiatives involving the following green operations practices: green buildings, eco-design, green supply chains, green manufacturing, reverse logistics and innovation. Research limitations/implications: The limitations of this paper start from its selection of the companies, which was made using production volume and country of origin as the principal criteria. There is ample evidence that other, smaller, companies are pursuing more sophisticated and original environmental initiatives. Also, there might be a gap between what companies say they do in their environmental reports and what they actually do. Practical implications: This paper helps practitioners in the automotive industry to benchmark themselves against the major volume manufacturers in three different continents. Practitioners from other industries will also find it valuable to discover how the automotive industry is pursuing environmental initiatives beyond manufacturing, apart from the green operations practices covering broadly all the activities of operations function. Originality/value: The originality of the paper is in its up-to-date analysis of environmental reports of automotive companies. The paper offers value for researchers and practitioners due to its contribution to the green operations literature. For instance, the inclusion of green buildings as part of green operations practices has so far been neglected by most researchers and authors in the field of green and sustainable operations. © Emerald Group Publishing Limited.

Self-adaptive probabilistic roadmap generation for intelligent virtual agents

Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.