Browsing by Author "Alrabghi, Abdullah"
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Item Open Access A novel framework for simulation-based optimisation of maintenance systems(DAAAM International, 2016-03-31) Alrabghi, Abdullah; Tiwari, AshutoshThe maintenance function in manufacturing has been gaining growing interest and significance. Simulation based optimisation has a high potential in supporting maintenance managers to make the right decisions in complex maintenance systems. Surveys in maintenance optimisation have repeatedly reported the need of a framework that provides adequate level of details to guide both academics and practitioners in optimising maintenance systems. The purpose of the current study is to address this gap by developing a novel framework that supports decision making for maintenance in manufacturing systems. The framework is developed by synthesising research attempts to optimise maintenance by simulation, examining existing maintenance optimisation frameworks and capturing framework requirements from review papers in the area as well as publications on future maintenance applications. As a result, the framework addresses current issues in maintenance such as complexity, multi-objective optimisation and uncertainty. The framework is represented by a standard flowchart to facilitate its use.Item Open Access A review of simulation-based optimisation in maintenance operations(IEEE, 2013-06-10) Alrabghi, Abdullah; Tiwari, AshutoshThis paper aims to report the state of the art of research in simulation-based optimisation of maintenance operations by systematically classifying the published literature and outlining various tools and techniques used by researchers to model and optimise maintenance operations. The authors investigate the critical elements and aspects of maintenance systems and how well they are represented in the literature as well as various approaches to problem formulation. On this basis, the paper identifies the current gaps and discusses future prospects. It is observed that discrete event is the most widely used simulation technique while non-traditional optimisation algorithms such as genetic algorithms and simulated annealing are the most reported optimisation techniques. Little attention has been paid to the discussion and analysis of different elements in the maintenance environment and their effect on the maintenance system behaviour. There is a need for verifying suggested models through real life case studies.Item Open Access Simulation based optimization of joint maintenance and inventory for multi-components manufacturing systems(IEEE, 2014-01-27) Alrabghi, Abdullah; Tiwari, Ashutosh; Alabdulkarim, Abdullah A.Maintenance and spare parts management are interrelated and the literature shows the significance of optimizing them jointly. Simulation is an efficient tool in modeling such a complex and stochastic problem. In this paper, we optimize preventive maintenance and spare provision policy under continuous review in a non-identical multi-component manufacturing system through a combined discrete event and continuous simulation model coupled with an optimization engine. The study shows that production dynamics and labor availability have a significant impact on maintenance performance. Optimization results of Simulated Annealing, Hill Climb and Random solutions are compared. The experiments show that Simulated annealing achieved the best results although the computation time was relatively high. Investigating multi-objective optimization might provide interesting results as well as more flexibility to the decision maker.Item Open Access Simulation-based optimisation of maintenance systems: Industrial case studies(Elsevier, 2017-06-20) Alrabghi, Abdullah; Tiwari, Ashutosh; Savill, MarkInvestigating the optimum blend of maintenance strategies for a given manufacturing system is a continuing concern amongst maintenance academics and professionals. Recent evidence suggests that little research is conducted on the simulation optimisation of maintenance in industrial systems. This study was designed to make an important contribution to the field of simulation-based optimisation of maintenance by presenting two empirical case studies: a tyre re-treading factory and a petro-chemical plant. It is one of the first to optimise various maintenance strategies simultaneously with their parameters in industrial manufacturing systems while considering production dynamics. Stochastic Discrete Event Simulation models were developed and connected to a Multi-Objective Optimisation engine. Various maintenance strategies were investigated including Corrective Maintenance, Preventive Maintenance, Opportunistic Maintenance and Condition-Based Maintenance. The results of this research suggest that over-looking the optimisation of maintenance on the strategic level may lead to sub-optimal solutions. In addition, it appears that traditional trade-offs between maintenance cost and production throughput are not present in some maintenance systems. This is an interesting observation that requires further investigation and experimentation.Item Open Access State of the art in simulation-based optimisation for maintenance systems(Elsevier, 2014-12-29) Alrabghi, Abdullah; Tiwari, AshutoshRecently, more attention has been directed towards improving and optimising maintenance in manufacturing systems using simulation. This paper aims to report the state of the art in simulation-based optimisation of maintenance by systematically classifying the published literature and outlining main trends in modelling and optimising maintenance systems. The authors investigate application areas and published real case studies as well as researched maintenance strategies and policies. Much of the research in this area is focusing on preventive maintenance and optimising preventive maintenance frequency that will lead to the minimum cost. Discrete event simulation was the most reported technique to model maintenance systems whereas modern optimisation methods such as Genetic Algorithms was the most reported optimisation method in the literature. On this basis, the paper identifies the current gaps and discusses future prospects. Further research can be done to develop a framework that guides the experimenting process with different maintenance strategies and policies. More real case studies can be conducted on multi-objective optimisation and condition based maintenance especially in a production context.