Track: Manufacturing

Abstract

Abstract

Manufacturing industry that produces various plastic packaging products experience high frequency of machine breakdowns that consequently cause tardiness in completing orders. Based on importance, orders are given different weights of priorities. The earliest due date rule method that is currently used for production scheduling, is causing delays in multiple job completions, increasing average tardiness. Currently, both corrective maintenance and production scheduling are planned separately, even though both are intertwined. Given the strong correlation between the two, this study seeks to integrate preventive maintenance and production scheduling to generate production schedules that can minimize the mean weighted expected tardiness. Data processing starts by identifying machines with highest downtime value and identifying critical machine components that are causing downtimes. Based on data collection and processing, Injection Machine 650-ton has the highest down time. By calculating Preventive Maintenance, we obtain time intervals for inspection and preventive replacements for each component. Following that, orders are sorted using single machine scheduling, resulting in a mean weighted expected tardiness of 78 hours. By integrating preventive maintenance and production scheduling, the mean weighted expected tardiness resulted in 43 hours. After using genetic algorithm, the mean weighted expected tardiness further declines to 32 hours.

Keywords

corrective maintenance, preventive maintenance, production scheduling, minimizing mean weighted expected tardiness, genetic algorithm.