27,436 research outputs found

    Estimation of cellular manufacturing cost components using simulation and activity-based costing

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    It can be difficult estimating all of the cost components that are attributed to a machined part. This problem is more pronounced when a factory uses group technology manufacturing cells as opposed to a functional or process layout of a job shop. This paper describes how activity-based costing (ABC) concepts can be integrated into a discrete-event simulation model of a U-shaped manufacturing cell producing a part family with four members. The simulation model generates detailed Bills of Activity for each part type and includes specific information about the cost drivers and cost pools. The enhanced model output can be used for cost estimation and analysis, manufacturing cell design, part scheduling and other manufacturing decision processes that involve economic considerations. Although the scope of this effort is restricted to a small scale manufacturing cell, the costing concepts have general applicability to manufacturing operations at all levels

    Design and development of a hybrid control system for flexible manufacturing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University

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    Irregular Pagination MisnumberedFlexible Manufacturing Systems (FMS) appeared upon the manufacturing scene in the early 1970s, installations presently number in the thousands. However, many current installations in fact lack flexibility, do not operate in real-time and are prohibitively expensive. Therefore there are obvious benefits to be gained from making improvements to existing flexible manufacturing systems. Research conducted for this thesis focused on two major areas. The implementation of the FMS control system on a SCADA package and the development of an auction based scheduling system. This entailed the development of a hybrid control model composed of three distinct layers; factory, cell and intelligent entity. Key portions of both the factory and cell controllers were then implemented so as to create a minimal system. This has been completed to the point where the auction algorithm has been implemented and tested in an appropriate framework. In achieving the goals mentioned above a number of novel design concepts have been utilised. There are two which are most important, these are the use of low cost modules for the construction of a flexible co-operative manufacturing system, and the ability of this system to operate in a physically distributed area via a Local Area Network. Meaning it is inherently adaptable and resistant to failure. These novel design concepts were ingrained throughout the entire three layered control model. It is felt that this research has succeeded in demonstrating the possibility of implementing a FMS control system on a low cost SCADA package using low cost software and computing elements. The ability of the distributed, auction-based approach to operate successfully within this system, has also been demonstrated through simulation

    A framework for smart production-logistics systems based on CPS and industrial IoT

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    Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

    Survey of dynamic scheduling in manufacturing systems

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    Scheduling of non-repetitive lean manufacturing systems under uncertainty using intelligent agent simulation

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    World-class manufacturing paradigms emerge from specific types of manufacturing systems with which they remain associated until they are obsolete. Since its introduction the lean paradigm is almost exclusively implemented in repetitive manufacturing systems employing flow-shop layout configurations. Due to its inherent complexity and combinatorial nature, scheduling is one application domain whereby the implementation of manufacturing philosophies and best practices is particularly challenging. The study of the limited reported attempts to extend leanness into the scheduling of non-repetitive manufacturing systems with functional shop-floor configurations confirms that these works have adopted a similar approach which aims to transform the system mainly through reconfiguration in order to increase the degree of manufacturing repetitiveness and thus facilitate the adoption of leanness. This research proposes the use of leading edge intelligent agent simulation to extend the lean principles and techniques to the scheduling of non-repetitive production environments with functional layouts and no prior reconfiguration of any form. The simulated system is a dynamic job-shop with stochastic order arrivals and processing times operating under a variety of dispatching rules. The modelled job-shop is subject to uncertainty expressed in the form of high priority orders unexpectedly arriving at the system, order cancellations and machine breakdowns. The effect of the various forms of the stochastic disruptions considered in this study on system performance prior and post the introduction of leanness is analysed in terms of a number of time, due date and work-in-progress related performance metrics

    An on-demand fixture manufacturing cell for mass customisation production systems.

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    Master of Science in Engineering. University of KwaZulu-Natal, Durban, 2017.Increased demand for customised products has given rise to the research of mass customisation production systems. Customised products exhibit geometric differences that render the use of standard fixtures impractical. Fixtures must be configured or custom-manufactured according to the unique requirements of each product. Reconfigurable modular fixtures have emerged as a cost-effective solution to this problem. Customised fixtures must be made available to a mass customisation production system as rapidly as parts are manufactured. Scheduling the creation/modification of these fixtures must now be treated together with the production scheduling of parts on machines. Scheduling and optimisation of such a problem in this context was found to be a unique avenue of research. An on-demand Fixture Manufacturing Cell (FxMC) that resides within a mass customisation production system was developed. This allowed fixtures to be created or reconfigured on-demand in a cellular manufacturing environment, according to the scheduling of the customised parts to be processed. The concept required the research and development of such a cell, together with the optimisation modelling and simulation of this cell in an appropriate manufacturing environment. The research included the conceptualisation of a fixture manufacturing cell in a mass customisation production system. A proof-of-concept of the cell was assembled and automated in the laboratory. A three-stage optimisation method was developed to model and optimise the scheduling of the cell in the manufacturing environment. This included clustering of parts to fixtures; optimal scheduling of those parts on those fixtures; and a Mixed Integer Linear Programming (MILP) model to optimally synchronise the fixture manufacturing cell with the part processing cell. A heuristic was developed to solve the MILP problem much faster and for much larger problem sizes – producing good, feasible solutions. These problems were modelled and tested in MATLAB¼. The cell was simulated and tested in AnyLogic¼. The research topic is beneficial to mass customisation production systems, where the use of reconfigurable modular fixtures in the manufacturing process cannot be optimised with conventional scheduling approaches. The results showed that the model optimally minimised the total idle time of the production schedule; the heuristic also provided good, feasible solutions to those problems. The concept of the on-demand fixture manufacturing cell was found to be capable of facilitating the manufacture of customised products

    Discrete Event Simulation Modelling for Dynamic Decision Making in Biopharmaceutical Manufacturing

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    With the increase in demand for biopharmaceutical products, industries have realised the need to scale up their manufacturing from laboratory-based processes to financially viable production processes. In this context, biopharmaceutical manufacturers are increasingly using simulation-based approaches to gain transparency of their current production system and to assist with designing improved systems. This paper discusses the application of Discrete Event Simulation (DES) and its ability to model the various scenarios for dynamic decision making in biopharmaceutical manufacturing sector. This paper further illustrates a methodology used to develop a simulation model for a biopharmaceutical company, which is considering several capital investments to improve its manufacturing processes. A simulation model for a subset of manufacturing activities was developed that facilitated ‘what-if’ scenario planning for a proposed process alternative. The simulation model of the proposed manufacturing process has shown significant improvement over the current process in terms of throughout time reduction, better resource utilisation, operating cost reduction, reduced bottlenecks etc. This visibility of the existing and proposed production system assisted the company in identifying the potential capital and efficiency gains from the investments therefore demonstrating that DES can be an effective tool for making more informed decisions. Furthermore, the paper also discusses the utilisation of DES models to develop a number of bespoke productivity improvement tools for the company

    Spatial-temporal data modelling and processing for personalised decision support

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    The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

    Organizational alternatives for flexible manufacturing systems

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    There is an increasing importance of different productive architectures related to worker involvement in the decision making, where is given due attention to the intuitive capabilities and the human knowledge in the optimization and flexibilization of manufacturing processes. Thus having reference point architecture of a flexible manufacturing and assembling system existent at UNINOVA-CRI, we will present some exploratory hypothesis about applicability of the concept of hybridization and its repercussions on the definition of jobs, in those organizations and in the formation of working teams.flexibility; robotics; work organization; manufacturing industry
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