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De Meyer A.,Catholic University of Leuven | Snoeck M.,Catholic University of Leuven | Cattrysse D.,Center for Industrial Management | Van Orshoven J.,Catholic University of Leuven
Environmental Modelling and Software | Year: 2016

This paper presents a generic and flexible reference data model meant as the blueprint of the database component of information and decision support systems related to different types of biomass-based supply chains (e.g. first to fourth generation biomass for production of bioenergy and biomaterials). The data model covers the biomass types and handling operations as characterised by their attributes and mutual relationships resulting from a life cycle inventory analysis. The data model enables the identification of the possible operation sequences in the specified chain. This functionality is demonstrated in a case study in which biomass from tall herb communities and mesotrophic grasslands is supplied for biogas or compost production. A comparative analysis has pointed out that the data model includes the required object types to add specific attributes of biomass supply chain simulation and optimisation models (such as spatial and temporal dimensions). © 2016 Elsevier Ltd.


Dennis V.,Center for Industrial Management | Paul-Armand V.,Center for Industrial Management | Duflou J.,Center for Industrial Management
Proceedings of the ASME Design Engineering Technical Conference | Year: 2012

Although Biologically-Inspired Design (BID) is gaining popularity, state-of-the-art approaches for systematic BID are still limited by the required interactive work which is proportional to the applied biological database size. This interactive work, depending on the adopted methodology, might encompass model instantiation for each strategy in the biological database, classification into a predefined scheme or extensive result filtering. This contribution presents a first scalable approach to systematic BID with the potential to leverage large numbers of biological strategies. First, a focused webcrawler, based on a combination of Support Vector Machines (SVM), continuously searches for biological strategies on the Internet. The solution to this needle-in-ahaystack task is shown to produce biological strategies interesting for cross-domain Design-by-Analogy (DbA). These resources are then automatically positioned into Ask Nature's well-known Biomimicry Taxonomy; a 3-level hierarchical classification scheme that enables designers to identify biological strategies relevant to their specific design problem. This paper details the architecture of the proposed system, and presents results indicating the feasibility of the applied approach. Copyright © 2012 by ASME.


Duflou J.R.,Center for Industrial Management | Sutherland J.W.,Purdue University | Dornfeld D.,University of California at Berkeley | Herrmann C.,TU Braunschweig | And 4 more authors.
CIRP Annals - Manufacturing Technology | Year: 2012

This paper aims to provide a systematic overview of the state of the art in energy and resource efficiency increasing methods and techniques in the domain of discrete part manufacturing, with attention for the effectiveness of the available options. For this purpose a structured approach, distinguishing different system scale levels, is applied: starting from a unit process focus, respectively the multi-machine, factory, multi-facility and supply chain levels are covered. Determined by the research contributions reported in literature, the de facto focus of the paper is mainly on energy related aspects of manufacturing. Significant opportunities for systematic efficiency improving measures are identified and summarized in this area. © 2012 CIRP.


Shirazi S.A.A.,Center for Industrial Management | Pintelon L.,Center for Industrial Management
International Journal of Care Pathways | Year: 2012

Lean Thinking and Six Sigma are quality management tools developed in an industrial context, where they have proven their usefulness for decades. More recently, these tools entered health care. Quite some literature on the application of these tools in health care has appeared since. This paper reviews this literature; more than 120 papers are studied and classified according to the nature of the paper: review, case study, strategy or tool description. Many case study papers were found. For these papers further analysis was made on the focus quality issue, the tools used and the area of implementation.


Muchiri P.N.,Dedan Kimathi University of Technology | Pintelon L.,Center for Industrial Management | Martin H.,Open University Nederland | Chemweno P.,Center for Industrial Management
International Journal of Production Research | Year: 2014

Equipment maintenance and system reliability are important factors affecting the organisations ability to provide quality and timely services to customer. While maintenance remains an important function to manufacturing, it is only recently that attempts have been made to quantify its impact on equipment performance. In this research, an approach of linking maintenance with equipment performance is developed using simulation modelling. The modelling approach involves defining probabilistic models and assumptions affecting system performance, such as: the probabilistic model for the initial failure rate/intensity of the equipment; the probabilistic model for the system deterioration and corresponding effect; the probabilistic model for the random times of corrective maintenance (CM) and preventive maintenance (PM) that takes into the account the types of maintenance plans/policies and the potential dependency between CM and PM times; and the probabilistic model for the random effects of CM and PM on the reliability of the equipment. Using a continuous manufacturing equipment, the model is used to analyse the impact of deterioration, failures and maintenance (policies, timing and efficiency) on equipment performance. It is shown that modelling the effect maintenance provides a basis of evaluating maintenance efforts with the potential application in performance evaluation and decision support while investigating opportunities for manufacturing equipment performance improvement. © 2013 © 2013 Taylor & Francis.

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