投資組合的概念已發展成一決策方法，長期以來廣泛應用於評估及評選各產業中多屬性之最佳組合，但僅對於單階組合結構的問題做選擇與探討，決策者往往將較大且複雜的問題分解成多個子問題分別討論，並藉由投資組合的評選將多個子問題合成原始問題的最佳方案。
本研究的目的在於發展可同時考量多層投資組合決策問題的超投資組合架構，多層決策問題為由最終決策到各單位決策中經多次組合選擇關係的問題，超投資組合可定義為包含多個垂直及水平投資組合相互作用特性的問題架構，由多個倒推式投資組合(backward-type portfolio)垂直整合而成。以組合屬性考量，倒推式投資組合基本組合屬性分為獨立、相關、綜效三類，本研究以雙層投資組合決策層級為主體，探討垂直組合屬性交互影響的作用與特性，歸納出除了原有組合屬性外還增加相互依賴特性，將單層決策維度擴展成多層維度以解決更大型組合決策問題。
本研究以菜單設計問題為例，而評選單一菜餚組合而成一餐再組合成一份菜單的決策過程即為雙層決策問題，在此採用超投資組合概念架構餐點組合問題，並應用多屬性混整數線性規劃模型考量多層多項屬性以評估餐點組合效益，有效地解決實際養老院菜單設計的問題。研究中發現超投資組合適用於多屬性及多層級問，配合啟發式演算法可有效且快速解決大型且複雜的決策問題。

Portfolio selection has been developed as a decision method for centuries and it is adopted in many fields for evaluating and selecting portfolios of multi-attribute item. Many studies have dealt this problem by decomposing complicated problems and focusing on single layer of portfolio to solve the subporblems.
This study aims to develop a “hyper-portfolio” selection framework for the multi-layer portfolio decision problem. Multi-layer portfolio problem is defined as a combination of portfolios which are the combinations of items. In particular, Take, for example, menu consists of combination of interrelated meals which consist of combinations of interrelated food items. The proposed generic hyper-portfolio selection framework is based on backward-type selection. The vertical interactions between two layers of portfolio are derived with portfolio interactions between portfolio components. In hyper-portfolio, identified independent, interrelated and synergistic attributes perform similar properties in portfolio selection; in addition, interdependent attribute is proposed for inseparable affiliation of portfolio elements.
This thesis applies the proposed framework for menu design problem with nine food item attributes during time horizon. Based on the item attributes, the portfolio selection is for meal design; furthermore, the hyper-portfolio selection evaluates menu performance by a multi-criteria mixed-integer-linear-programming (MILP) model. Finally, the thesis illustrates the hyper-portfolio MILP model calculating process, and a real nurse house menu design case. We find that hyper-portfolio selection outperforms in dealing with multi-attribute and hierarchical decision problems.

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