Browsing by Author "Khalil, R. A. (Riham A.)"

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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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    Autonomous Planning using the basic principles of Gene Transcription Regulatory Control
    (Scientific and 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 and 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 and Francis, 2008) Stockton, David; Ardon-Finch, J. P. (Jason P.); Khalil, R. A. (Riham A.)
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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, 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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    Design of flexible manpower line walk cycles for a fixed number of operators.
    (Taylor and 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.
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    Designing multi-component flexible manpower lines.
    (JSAE, 2005) Stockton, David; Khalil, R. A. (Riham A.); Newman, S. T.; Ardon-Finch, J. P. (Jason P.)
    The use of multi-component flexible manpower lines (MCFMLs) is seen as one method of enabling component suppliers to deliver the cost reductions and increased flexibility of service levels expected by automotive manufacturers. This paper describes research undertaken to develop an advanced genetic algorithm optimization/simulation toolkit that is capable of automatically generating optimized MCFML designs. In order to deal effectively with the high levels of product and process variability that exist within MCFMLs research is reported that is aimed at developing methods for measuring the effects of variability on individual workstations along such lines. (author abst.)
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    Designing multi-component flexible manpower lines.
    (Society of Automotive Engineers of Japan, Inc., 2006) Khalil, R. A. (Riham A.); Stockton, David; Newman, S. T.; Ardon-Finch, J. P. (Jason P.)
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    Effect of breakdown on the flow processing systems.
    (Sheffield Hallam University, 2004) Khalil, R. A. (Riham A.); Stockton, David
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    Effect of variation on the flow processing systems.
    (Proceeding 21st International Manufacturing Conference (IMC21), 2004) Khalil, R. A. (Riham A.); Stockton, David
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    Elimination of variation in the flow line.
    (Professional Engineering Publishing, 2003) Khalil, R. A. (Riham A.); Stockton, David
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    End-of-life re-manufacturing using process sequence cell layouts.
    (2005) Khalifa, S.; Stockton, David; Lindley, R.; Khalil, R. A. (Riham A.)
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    Estimating level of job contents variability within flow processing systems workstations.
    (Taylor and Francis, 2009) Khalil, R. A. (Riham A.); Stockton, David
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    Implementing lean in software development operations.
    (2009) Al-Kaabi, Mohamed; Khalil, R. A. (Riham A.); Stockton, David
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    Improving the sales demand forecasting process
    (2008) Miller, Simon; Khalil, R. A. (Riham A.); Stockton, David
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    Integration of Design of Experiments with Discrete event Simulation for Problem Identification
    (2010) Kang, Parminder; Khalil, R. A. (Riham A.); Stockton, David
    Lean is acknowledged as one of the key tools for manufacturing organizations and service industry to survive in the competitive environment. Lean approach focuses on continuous improvement by eliminating process waste. Lean creative problem solving plays a vital role in process improvement. One of main focus is the effect of Lean problem solving in production line performance measurements. The current research focuses on Lean creative problem solving techniques by examining the root cause of the problem. Lean creative problem solving method was developed by using simulation modelling techniques.
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    Integration of discrete event simulation with an automated problem identification.
    (IMECS– International Multi-Conference of Engineers and Computer Scientists, 2010-03) Khalil, R. A. (Riham A.); Kang, Parminder; Stockton, David