Machine Learning Algorithms in Cloud Manufacturing - A Review

Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment

Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture

Improving project management planning and control in service operations environment.

Projects have evidently become the core activity in most companies and organisations where they are investing significant amount of resources in different types of projects as building new services, process improvement, etc. This research has focused on service sector in attempt to improve project management planning and control activities. The research is concerned with improving the planning and control of software development projects. Existing software development models are analysed and their best practices identified and these have been used to build the proposed model in this research. The research extended the existing planning and control approaches by considering uncertainty in customer requirements, resource flexibility and risks level variability. In considering these issues, the research has adopted lean principles for planning and control software development projects. A novel approach introduced within this research through the integration of simulation modelling techniques with Taguchi analysis to investigate ‗what if‘ project scenarios. Such scenarios reflect the different combinations of the factors affecting project completion time and deliverables. In addition, the research has adopted the concept of Quality Function Deployment (QFD) to develop an automated Operations Project Management Deployment (OPMD) model. The model acts as an iterative manner uses ‗what if‘ scenario performance outputs to identify constraints that may affect the completion of a certain task or phase. Any changes made during the project phases will then automatically update the performance metrics for each software development phases. In addition, optimisation routines have been developed that can be used to provide management response and to react to the different levels of uncertainty. Therefore, this research has looked at providing a comprehensive and visual overview of important project tasks i.e. progress, scheduled work, different resources, deliverables and completion that will make it easier for project members to communicate with each other to reach consensus on goals, status and required changes. Risk is important aspect that has been included in the model as well to avoid failure. The research emphasised on customer involvement, top management involvement as well as team members to be among the operational factors that escalate variability levels 3 and effect project completion time and deliverables. Therefore, commitment from everyone can improve chances of success. Although the role of different project management techniques to implement projects successfully has been widely established in areas such as the planning and control of time, cost and quality; still, the distinction between the project and project management is less than precise and a little was done in investigating different levels of uncertainty and risk levels that may occur during different project phase.United Arab Emirates Governmen

Modular reactors: What can we learn from modular industrial plants and off site construction research

New modular factory-built methodologies implemented in the construction and industrial plant industries may bring down costs for modular reactors. A factory-built environment brings about benefits such as; improved equipment, tools, quality, shift patterns, training, continuous improvement learning, environmental control, standardisation, parallel working, the use of commercial off shelf equipment and much of the commissioning can be completed before leaving the factory. All these benefits combine to reduce build schedules, increase certainty, reduce risk and make financing easier and cheaper.Currently, the construction and industrial chemical plant industries have implemented successful modular design and construction techniques. Therefore, the objectives of this paper are to understand and analyse the state of the art research in these industries through a systematic literature review. The research can then be assessed and applied to modular reactors.The literature review highlighted analysis methods that may prove to be useful. These include; modularisation decision tools, stakeholder analysis, schedule, supply chain, logistics, module design tools and construction site planning. Applicable research was highlighted for further work exploration for designers to assess, develop and efficiently design their modular reactors

Paints supply chain optimisation

Imperial Users onl