Pusat Pengajian Kejuruteraan Mekanikal - Tesis

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Browsing Pusat Pengajian Kejuruteraan Mekanikal - Tesis by Author "Hwang, Jia Qi"

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    Profit optimization of preventive maintenance frequency, lot size and buffer for a two stages serial system
    (2020-05-01)
    Hwang, Jia Qi
    In the manufacturing industry, a complex and mutual relationship exists between the maintenance and production functions due to its shared resource consumption and the influence on profit. However, the assumptions of considering only one type of machine failures and good quality output during the in-control state affect the realism of a production model. The dependency between the machines in the multi-stages system and its impact on the production and maintenance planning are often overlooked as well. Hence, this research aims to optimize the number of Preventive Maintenance (PM) activities, lot size and buffer size for the machines in a serial configuration that has different characteristics using a profit maximization approach. A model was constructed in Discrete Event Simulation (DES) software, WITNESS Horizon 21.0. The production cost including the manufacturing cost and inventory cost, the maintenance cost of PM, minimal repair and restoration together with the losses due to defective output and lost sales are included in the profit maximization model. Then, the model was validated based on the data from a carton box production line followed by the optimization with WITNESS Experimenter. From the result of the numerical experimentation, the near-optimal profit point (5305.963k mu) is the result of the combination of 2200 units of lot size, 20 units of buffer size, seven PM activities for M1 and five PM activities for M2. This result is the second-highest profit in the exhaustive search of the decision variables. Moreover, the xperimentation time is 85.6% lower than the computation time of all the combinations of the decision variables. And so, the proposed model can be used as a guide for decision making involving maintenance and production in the industry for its capability to determine a near-optimal point at a shorter computational time than the time needed to simulate all the value of decision variables.