Browsing by Author "Prajapat, Neha"

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    A 3D immersive discrete event simulator for enabling prototyping of factory layouts
    (Elsevier, 2015-10-27) Oyekan, John; Hutabarat, Windo; Turner, Christopher J.; Tiwari, Ashutosh; Prajapat, Neha; Ince, Nadir; Gan, Xiao-Peng; Waller, Tony
    There is an increasing need to eliminate wasted time and money during factory layout design and subsequent construction. It is presently difficult for engineers to foresee if a certain layout is optimal for work and material flows. By exploiting modelling, simulation and visualisation techniques, this paper presents a tool concept called immersive WITNESS that combines the modelling strengths of Discrete Event Simulation (DES) with the 3D visualisation strengths of recent 3D low cost gaming technology to enable decision makers make informed design choices for future factories layouts. The tool enables engineers to receive immediate feedback on their design choices. Our results show that this tool has the potential to reduce rework as well as the associated costs of making physical prototypes.
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    Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing
    (Institute of Electrical and Electronics Engineers, 2017-01-19) Hutabarat, Windo; Oyekan, John; Turner, Christopher J.; Tiwari, Ashutosh; Prajapat, Neha; Gan, Xiao-Peng; Waller, Anthony
    Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.
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    Impact of model fidelity in factory layout assessment using immersive discrete event simulation
    (Operational Research Society, 2016-04-13) Petti, Alessandro; Hutabarat, Windo; Oyekan, John; Turner, Christopher J.; Tiwari, Ashutosh; Prajapat, Neha; Gan, Xiao-Peng
    Discrete Event Simulation (DES) can help speed up the layout design process. It offers further benefits when combined with Virtual Reality (VR). The latest technology, Immersive Virtual Reality (IVR), immerses users in virtual prototypes of their manufacturing plants to-be, potentially helping decision-making. This work seeks to evaluate the impact of visual fidelity, which refers to the degree to which objects in VR conforms to the real world, using an IVR visualisation of the DES model of an actual shop floor. User studies are performed using scenarios populated with low- and high-fidelity models. Study participant carried out four tasks representative of layout decision-making. Limitations of existing IVR technology was found to cause motion sickness. The results indicate with the particular group of naïve modellers used that there is no significant difference in benefits between low and high fidelity, suggesting that low fidelity VR models may be more cost-effective for this group.
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    Layout Optimization of a repair facility using discrete event simulation
    (Elsevier, 2016-11-16) Prajapat, Neha; Waller, Tony; Young, Joseph; Tiwari, Ashutosh
    Technological advancements in the field of simulation have enabled production managers to model and simulate their facilities under various scenarios, in order to optimize system performance. In particular the reconfiguration of factory layouts can be time consuming and expensive; Discrete Event Simulation (DES) can be used to model and assess various scenarios to assist production managers with layout planning. Significant benefits can be achieved through the use of DES for factory layout optimization including: decreased lead times, reduced manufacturing costs, efficient materials handling and increased profit. This paper presents the development of a DES model in WITNESS for the analysis and factory layout optimization of a repair facility. The aim of the model is to allow decision makers to assess various layouts and configurations with a view to optimize production. The model has been built with a link to an Excel spreadsheet to enable data input and the visualization of Key Performance Indicators (KPIs). Specific functions have been built into the simulation model to set and save new layouts within Excel to facilitate layout optimization. The model will be used to optimize the factory configuration.
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    Survey on the use of computational optimisation in UK engineering companies
    (Elsevier, 2015-01-22) Tiwari, Ashutosh; Hoyos, Paula Noriega; Hutabarat, Windo; Turner, Christopher J.; Ince, Nadir; Gan, Xiao-Peng; Prajapat, Neha
    The aim of this work is to capture current practices in the use of computational optimisation in UK engineering companies and identify the current challenges and future needs of the companies. To achieve this aim, a survey was conducted from June 2013 to August 2013 with 17 experts and practitioners from power, aerospace and automotive Original Equipment Manufacturers (OEMs), steel manufacturing sector, small- and medium-sized design, manufacturing and consultancy companies, and optimisation software vendors. By focusing on practitioners in industry, this work complements current surveys in optimisation that have mainly focused on published literature. This survey was carried out using a questionnaire administered through face-to-face interviews lasting around 2 h with each participant. The questionnaire covered 5 main topics: (i) state of optimisation in industry, (ii) optimisation problems, (iii) modelling techniques, (iv) optimisation techniques, and (v) challenges faced and future research areas. This survey identified the following challenges that the participant companies are facing in solving optimisation problems: large number of objectives and variables, availability of computing resources, data management and data mining for optimisation workflow, over-constrained problems, too many algorithms with limited help in selection, and cultural issues including training and mindset. The key areas for future research suggested by the participant companies are as follows: handling large number of variables, objectives and constraints particularly when solution robustness is important, reducing the number of iterations and evaluations, helping the users in algorithm selection and business case for optimisation, sharing data between different disciplines for multi-disciplinary optimisation, and supporting the users in model development and post-processing through design space visualisation and data mining.