Ordinal optimization based metaheuristic algorithm for optimal inventory policy of assemble-to-order systems

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• 作者：Shih-Cheng Horng ; ^{schong@cyut.edu.tw} ; Shieh-Shing Lin ^{sslin@mail.sju.edu.tw}
• 关键词：Assemble-to-order system ; Continuous-review base-stock policy ; Ordinal optimization ; Extreme learning machine ; Teaching-learning-based optimization ; Ranking and selection
• 刊名：Applied Mathematical Modelling
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：42
• 期：Complete
• 页码：43-57
• 全文大小：1635 K
• 卷排序：42

文摘

Assemble-to-order system operating with continuous-review base-stock policy. Formulate the assemble-to-order system as a stochastic simulation optimization problem. Ordinal optimization based metaheuristic algorithm with three modules, meta-modeling, exploration and exploitation. A meta-model based on the extreme learning machine. Large ATO system with 12 items on 8 products and moderately sized ATO system with 8 items on 5 products.