The aim of this paper is to propose a safety management framework for construction companies. A literature review was carried out to identify significant factors that would improve safety performance. Two management tools—namely, the balanced scorecard and quality function deployment (QFD)—were used to construct the framework. Strategic goals were established for each of the following perspectives of the balanced scorecard: financial and cultural, employee, process, and learning. Afterwards, a questionnaire was prepared using the QFD approach. The goals in the financial and cultural perspective were defined as the safety-related needs of the organization ("customer requirements" in the original QFD approach); and the goals in the remaining perspectives included the actions that the organization could take to meet its needs. Results of the questionnaire were used to set the final strategic goals in the balanced scorecard. Safety performance measures and initiatives were used to accomplish the goals in the balanced scorecard. Key words: safety management, balanced scorecard, quality function deployment.
Arthur Koestler 's observations about the similarities in the structures and behaviours of manufacturing environment, biological beings and social groups led to a new era in production design and control. However, the later research ignored the roots of the holonic idea.In this paper, we propose a new model which maps the structures and behaviours of biological cells onto the manufacturing environment, thereby turning back to Koestler. We describe the common organelles of biological cells and then provide corresponding manufacturing units based on a cell design being used in automotive industry. We claim that this model gives a more realistic, reconfigurable, scalable and reliable view of today's production systems. Furthermore, the availability of diverse biological structures offers a wide range of architectures applicable to different manufacturing environments.
Absnon-Factory of the future is emerging with the existence of now modeling and application tools that can both simulate and manage the whole production process in an autonomous, intelligent and interactive manner. Holonic modeling and its wftwsre correspondence agent oriented technology provides UP with these toeis. Especially the w e of learning algdthmi trying to optimize the behaviors of software agenls within a dynamic environment is the key faclor in reaching the required properties. In this paper, we use the well known Q learning algorithm of reinforcement learning (RL) in evaluating production orders within a supply chain management (SCM) framewark and making decisions with respect to these evaluations. We introduce our SCM model and show that RL performs bener than trsditionai tools for dynamic problem solving in daily business. We also show cases where RL fails to perform emciently.
In this paper, we solve the problem of candidate access point selection in 802.11 networks, when there is more than one access point available to a station. We use the QBSS (quality of service enabled basic service set) Load Element of the new WLAN standard 802.11e as prior information and deploy a decision making algorithm based on reinforcement learning. We show that using reinforcement learning, wireless devices can reach more efficient decisions compared to static methods of decision making which opens the way to a more autonomic communication environment. We also present how the reinforcement learning algorithm reacts to changing situations enabling self adaptation. The authors Burak Simsek and Katinka Wolter would like to thank German Research Foundation (DFG) for support of this work under grant number WO 898/1-2.
Although autonomic networking has beeen discussed in related research activities in the last few years, there is neither a commonly accepted model nor a common understanding of the necessary components of an autonomic networking environment, which are also practically applicable. We present a simple model based on the notion of self that is perceived by human beings and deduce its components from the originating point which is the need for autonomic behavior. We also introduce three use cases that we implemented for solving different problems of IEEE 802.11e networks.
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