Browsing by Author "Stockton, David"

Now showing 1 - 20 of 65
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    Accelerating lean practice training using virtual reality.
    (2008) Khalil, R. A. (Riham A.); Stockton, David; Wright, N.; Gillis, C.
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    Advances in manufacturing technology - XXI : proceedings of the 5th international conference on manufacturing research (ICMR2007) : 11th - 13th September 2007
    (De Montfort University, 2007) Stockton, David; Khalil, R. A. (Riham A.); Baines, R. W. (Ray W.)
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    Application of neural networks in logistic systems.
    (2002) Ziarati, M.; Stockton, David; Uçan, Osman N.; Bilgili, E.
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    Applied advanced cost estimating technology for manufacturing processes.
    (2000) Baguley, Paul; Wang, Q.; Stockton, David
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    Autonomous Planning using the basic principles of Gene Transcription Regulatory Control
    (Scientific & Academic Publishing, 2012) Stockton, David; Khalil, R. A. (Riham A.); Mukhongo, Lawrence
    The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters requirements at a machine and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to process variability by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when variability process requirements is noted. The novelty of this work is the ability for processing machine to autonomous take decisions just as decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance metrics for each machine used. How they are used to deal with variability in process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved. This research demonstrated that the correct choice of values for finite capacity scheduling factors has a great impact on the performance measurements, such that, high throughput rate can be reached with a bigger batch size, lower percentage of rework, and shorter stoppages. Quicker movement of goods through the facility means better utilisation of assets. Better utilisation of assets creates additional capacity resulting from faster throughput thereby improving customer satisfaction through quicker delivery. It has also been shown that %waiting, %blocking and %stoppage makes it possible to examine different manufacturing constraints as well as the relationship between non-utilisation and different performance measurements.
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    Biological control processes and their application to manufacturing planning.
    (ICMR07 Conference Proceedings, De Montfort University, UK, 2007) Stockton, David; Schilstra, M; Khalil, R. A. (Riham A.); McAuley, M.
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    Control point policy optimization using genetic algorithms.
    (Taylor & Francis, 2008) Stockton, David; Khalil, R. A. (Riham A.); Ardon-Finch, J. P. (Jason P.)
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    Control point policy: part 1-efficiency within make-to-order environments.
    (Taylor & Francis, 2008) Stockton, David; Ardon-Finch, J. P. (Jason P.); Khalil, R. A. (Riham A.)
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    Cost model development using artificial neural networks.
    (MCB UP, 2001) Wang, Q.; Stockton, David
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    Cost model development using virtual manufacturing and data mining: Part II - Comparison of data mining algorithms
    (Springer, 2013) Stockton, David; Khalil, R. A. (Riham A.); Mukhongo, Lawrence
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    Cost model development using virtual manufacturing and data mining: part I—methodology development
    (Springer-Verlag, 2012-09-30) Stockton, David; Khalil, R. A. (Riham A.); Mukhongo, Lawrence
    This paper reports on research, the aim of which has been to investigate the use of virtual manufacturing and data mining techniques to automate the identification of manufacturing process time estimating relationships that form the basis of product and process cost models. Such models provide information that is critical to all stages of the product development process and to ensuring that development of cost-effective product design and production methods. Use of virtual manufacturing models and data mining techniques enables the majority of the activities involved in the cost model development process to be automated. Hence, reducing the time and effort required, reducing the current high level of reliance on expert judgment and enabling higher levels of cost detail to be estimated. The research reported, therefore, focuses on the use of virtual manufacturing to generate datasets which are then analysed using data mining to identify suitable cost models for manufacturing processes. Part I, of this two-part paper, describes the development of a model development methodology that makes use of virtual manufacturing models and data mining techniques and uses case study data to validate this methodology. Part II will then examine in detail the effectiveness of alternative data mining algorithms in terms of their ability to develop relationships that are (1) representative of the real causal relationships that exist and (2) provide a high level of estimating accuracy.
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    Cost modelling using neuro-fuzzy techniques.
    (2000) Wang, Q.; Stockton, David; Baguley, Paul
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    Database design for flexible machining lines.
    (2003) Wang, Q.; Stockton, David
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    Design and development of a factory of the future in Turkey.
    (IEEE, 2002) Ziarati, R.; Ziarati, M.; Uçan, Osman N.; Stockton, David
    The Factory of the Future programme of work embraces a number of collaborative research projects primarily concerned with factory automation. The current research encompasses the development of a laser device for machine tool calibration and a wireless network for application in manufacturing factories. A further work concerns research into design of a knowledge-based-system (KBS) for information automation as the basis for automating an entire manufacturing enterprise. The latter work is hoped to lead to the introduction of Intelligent Integrated Product Cycle (I2PC). This approach involves the development of a self-learning automated management system, which can be applied in a manufacturing enterprise, large or small. The paper makes special references to the I2 PC approach and the proposed neural network system and their application in automation of information and production processes within an enterprise.
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    Design and development of material and information flow for supply chaıns using genetic cellular networks.
    (Dogus University, 2002) Ziarati, M.; Stockton, David; Bilgili, E.; Uçan, Osman N.
    In a recent paper by authors (Ziarati and Ucan, January 2001) a Back Propagation-Artificial Neural Network (BP-ANN) was adapted for predicting the required car parts quantities in a real and major auto parts supplier chain. It was argued that due to the learning ability of neural networks, their speed and capacity to handle large amount of data, they have a potential for predicting components requirements and establishing associated scheduling throughout a given supply chain system.This paper should be considered a continuation of the first paper as the neural network approach introduced in this paper replaces the BP-ANN by a new method viz., Genetic Cellular Neural Network (GCNN). The latter approach requires by far less stability parameters and hence better suited to fast changing scenarios as in real supply chain applications.The model has shown promising outcomes in learning and predicting material demand in a supply chain, with high degree of accuracy.
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    Design and development of ships using an expert system applying a novel multi-layered neural networks.
    (Istanbul Technical University, 2009) Urkmez, S.; Ziarati, R.; Bilgili, E.; Ziarati, M.; Stockton, David
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    Design of flexible manpower line walk cycles for a fixed number of operators.
    (Taylor & Francis, 2005-01-01) Stockton, David; Ardon-Finch, J. P. (Jason P.); Khalil, R. A. (Riham A.)
    Flexible manpower lines (FMLs) are a form of flow process line in which operators are allocated 'walk cycles', i.e. a repetitive sequence in which to load and unload machine tools. The effective design of such lines is normally achieved with the expectation that operators without full walk cycles, i.e. those that do not require a full Takt time to accomplish, can complete their walk cycles at an adjoining FML. However, an alternative FML design strategy is possible in cases where no adjoining FML exists or it is not possible for operators to move between work areas. This strategy involves determining the minimum Takt time and the associated operator walk cycles at which the FML can operate under a fixed number of operators. To solve this type of problem, a genetic algorithm that make use of a novel crossover operator has been developed that can design FMLs. The genetic algorithm is capable of generating, for a specific Takt time and fixed number of operators, FMLs with high-quality, near-optimal operator walk cycles. Solutions for the fixed manpower case were then identified by performing multiple genetic algorithm runs to find the best walk cycles at various Takt times.