- 學位論文
基因演算法與ABC分析法最低庫存量配適度之最佳化模式
Using GA &ABC Analysis Compare Safe Inventory Difference for Optimal Inventory Model
摘要
本研究以中小型製造業手工肥皂產業為參考對象，利用基因演算法與ABC分析法進行物料分類管理及再訂購點最佳化。本研究中藉由基因演算法與ABC分析法模擬最佳解與分類模式的特性，將變異數加入基因演算法之中，並統計出每週期物料所需使用消耗量，基因演算法模擬新的再訂購點範圍與傳統模式中的ABC分析法進行比較，探討兩者再訂購點模式中是否有差異性。新的變異數的加入後能改善最佳化之再訂購點，而再訂購點顧名思義代表實際最低存貨量，此存貨量設定為最低限度，低於此存量時必須進行物料進補貨。 本研究中所加入新的變異數是期望給予再訂購點增加緩衝的時間，而服務水準上分別設定為0.95、0.8、0.6三種以及加入緩衝存量為5%與10%進行模擬，此緩衝存量設定為新的變異數函數，藉由不同服務水準以及緩衝存量搭配進行模擬比較，找出適合的組合的搭配及差異，藉由本研究使庫存管理減低發生物料缺乏風險之機率，改善傳統模式中的缺點，給予企業有新的決策參考模式。
並列摘要
That study, Small and Medium-Sized Enterprises as the research object. Using Genetic Algorithms(GA) and ABC Analysis for material classification management and reorder point optimization. That study of Genetic Algorithms(GA) and ABC analysis to classification method and optimize the solution, and statistics of the weekly consumption of materials required for use. Genetic Algorithms(GA) to simulate the new reorder point range and the traditional model of ABC Analysis to compare, Observed differences between the two modes. This research contains a new Variance to increase Cushion time on reorder point. On service standards were set at 0.95 and 0.8 and 0.6, Buffer stock is set to 5% and 10%.By different service levels and buffer stock compared with the simulation. Because of this study, the lack of materials inventory management to reduce the risk of the occurrence probability of improving the shortcomings of the traditional model, to give businesses a new decision-making model.