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Constructing A Portfolio Optimization Model for Vendor Selection and Order Allocation under Delivery Uncertainty
Thesis

Constructing A Portfolio Optimization Model for Vendor Selection and Order Allocation under Delivery Uncertainty

Chia-Yen Lee
Masters, 國立清華大學, 工業工程與工程管理學系
2005

Abstract

廠商評選 訂單配置 外包 產能規劃 多準則決策分析 組合最佳化 不確定狀況下的決策問題 穩健最佳化 Vendor Selection Order Allocation Outsourcing Capacity Planning Multi-Criteria Decision Making Portfolio Optimization Decision under Uncertainty Robust Optimization
Recently, outsourcing has played an important role in manufacturing strategy while enterprises have paid more attention to their own core competence. Particularly, specialized outsourcing not only lowers manufacturing costs of the overall supply chain but also further reduces production risk, shortens cycle time, and improve quality to ensure the ultimate objective of long-term profitability and sustainable operation. Thus, the vendor selection and order allocation (VSOA) becomes a well-known and critical problem. The vendor selection and order allocation problem simultaneously considers many complicated factors and qualitative and quantitative attributes. It is indeed a highly complex semi-structured problem. Added to this, the outsourcing process in a real environment always exists uncertainty and vagueness which makes the decision-making more difficult. This research incorporates multi-criteria decision analysis with portfolio optimization to construct a decision framework for the vendor selection and order allocation problem with performance and risk evaluation under delivery uncertainty. The objective of the proposed framework is to allocate appropriate orders to proper vendor and make effective tradeoff among the performance, risk and cost objectives. The methodologies of this research framework include decision analysis, feature selection, performance evaluation, risk identification and quantification, portfolio optimization, and simulation output analysis. Finally, a robust solution can be generated to assist in decision-making via step by step analysis. We conduct a numerical study to illustrate and validate the proposed framework. The result shows that suggested order allocation can take higher performance, minimize total cost, and achieve risk diversification.

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