| Summary: | 碩士 === 國立中央大學 === 工業管理研究所在職專班 === 104 === The objective of this study is to optimize the capacity allocation of the polishing production process in a marine hardware foundry. The critical factors affecting the supply of capacity including current size and the expandability of the facility, the commonness and standardization of the product, the complexity of the production process, the turnover rate of the labors and the stability of production equipment, etc. As a foundry founded twenty years ago, the overall number of equipment will not be increased dramatically unless plant expansion. The production management is familiar with all different kinds of products and can deal with complex production process very well. Therefore, the key factors in our foundry are the turnover of labors and the stability of equipment.
In most of the factories, the number of employees will not change significantly. However, our foundry located in coastal area and most of the employees are from inland. The commute might take them several days or a week, so they live in the dormitory in or nearby the foundry. The employees will take several days off and go home for family reunion in Songkran Festival (Thai New Year). The employees will stay at their hometown if they get a chance for a better job there. Therefore, the capacity of the foundry will be decreased for a period of time if the employees do not return from the holidays.
In addition, the breakdown of the equipment will also influence the output of the production. It is necessary to do maintenance regularly in order to ensure the machines are working. Capacity might decrease due to the maintenance.
In this study, a basic model will be built by Linear Programming. The polishing production cost, inventory cost and shortage cost of two cast parts and the purchasing cost and inventory cost of two accessories will be included in constraints. The optimization will be conducted in excel. Furthermore, two different scenarios with capacity restrictions will be discussed by adding capacity constraints to the basic model. The conclusion will help the controller adjust the production plan under predictable capacity restriction. With the best allocation of polishing capacity, the polishing production cost will be minimized and the profit will be raised.
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