Optimal Maintenance Scheduling for Multi-Component E-Manufacturing System
Arab, Ali (2009) Optimal Maintenance Scheduling for Multi-Component E-Manufacturing System. Masters thesis, Universiti Putra Malaysia.
During the recent years, development of information technology caused to develop a new industrial system which is called e-Manufacturing system. Thanks to the webenabled manufacturing technologies, the lead times are being minimized to their extreme level, and the minimum amount of inventory is kept, though the products are being made-to order. Under these circumstances, achieving near-zero downtime of the plant floor’s equipments is a crucial factor which mitigates the risk of facing unmet demands. Many researches carried out to schedule maintenance actions in short term, but none of them have utilized all of planning horizon to spread maintenance actions along available time. In this research a method of enhanced maintenance scheduling of multi-component e-Manufacturing systems has been developed. In this multi-component system, importance of all machines is considered and the benefit of the entire system in term of produced parts is taken into account (versus benefits of single machine). In proposed system, the predicted machines degradation information, online information about work in process (WIP) inventory (at inventory buffer of each work station) as well as production line’s dynamism are taken into account. All of makespans of planning horizon have been utilized to improve scheduling efficiency and operational productivity by maximizing the system throughputs. A state-of-the-art method which is called simulation optimization has been utilized to implement the proposed scheduling method. The production system is simulated by ProModel software. It plays the role of objective function of the maintenance scheduling optimization problem. Using a production related heuristic method which is called system value method, the value of each workstation is determined. These values are used to define the objective function’s parameters. Then, using genetic algorithm-based software which is called SimRunner and has been embedded by ProModel, the scheduling optimization procedure is run to find optimum maintenance schedule. This process is carried out for nine generated scenarios. At the end, the results are benchmarked by two commonly used maintenance scheduling methods to magnify the importance of proposed intelligent maintenance scheduling in the multi-component e-Manufacturing systems. The results demonstrate that the proposed optimal maintenance scheduling method yields much better system value rather than sequencing methods. Furthermore, it indicates that when the mean time to repairs are longer, this method is more efficient. The results in the simulated testbed indicate that the developed scheduling method using simulation optimization functions properly and can be applied in other cases.
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