1. APPLYING FUZZY LINGUISTIC QUANTIFIER TO SELECT SUPPLY CHAIN PARTNERS AT DIFFERENT PHASES OF PRODUCT LIFE CYCLE Advisor: Prof. Chu, Ta Chung 朱大中 Student: Dao, Xuan Tung 桃春松 (M977Z202)

2. INTRODUCTION Owing to rapidly growing global competition, enterprises are increasingly focusing on their core competencies. The focal company faces the challenge of creating alliances with more suppliers to create outsourcing synergy and provide heterogeneous products for customers. An effective supplier assessment and selection process is essential for improving the performance of a focal company and its supply chains The weights not only represented the importance of attributes or criteria that decisively influenced the evaluation result, but also correlated with product life cycle and even more with enterprise product development strategy.

3. Cycle time also influenced supplier selection and even performance representation, and the future development of long-term relationships and commitments The competitive criteria generally differ during different phases of product life cycle (e.g. availability and technology are needed at the ‘‘introduction’’ phase, and cost, quality and speed are needed at the ‘‘maturity’’ phase). This investigation aims to develop a supplier selection procedure by considering different fuzzy linguistic environments and different product life cycle phases.

4. MATHEMATICAL MODEL Supplier performance is related to two factors: Feature Attribute (FA; inter-attribute) of a contract Behavior Characteristic (BC; intra-attribute) of a supplier For subsequent trade-off computing purposes, a multiple attribute matrix is defined to represent the fuzzy preference relation of supplier BC factors. The multiple attribute matrix is converted into the fuzzy multiple attribute matrix using the fuzzy membership function, where: suppliers i = 1,…,m; FA k = 1,…,5 and BC j = 1,…,n 1. Constructing a multiple attribute matrix for supply chain performance and Developing into fuzzy preference relation

5. Table 1. The core of factors on supply chain performance (FA and BC)

6. Table 2. Linguistic interval scale Fig.1 Linguistic rating on membership function corresponding to fuzzy number. For positive BC: For negative BC: where: On converting into fuzzy preference relation: (1) (2)

7. A FLQG-OWA operator of dimension n is a mapping to generation function 2. Fitting supply chain strategy with fuzzy linguistic quantifier (3) (4) where guided a monotonically non-decreasing fuzzy linguistic quantifier Q with an associated n vector such that where. where denotes the jth largest of. The aggregation weighted vector W is a mapping to membership function Q(r)

8. Table 3. Product attributes and supply chain strategy on product life cycle Fig.2 Monotonically non-decreasing fuzzy linguistic quantifier.

9. Orness is a good measurement for characterizing the degree to which the aggregation is an or-like (max-like) or and-like (min-like) operation. Entropy represents the measurement for characterizing the degree to which information on the individual objectives in the aggregation process is used. 3. Optimizing FLQG-OWA operator The concept and purpose of optimization is based on the premise that the current orness should be kept constant to implement an amendment process for maximizing the entropy. Maximize subject to (5) (6) (7)

10. The Lagrange multiplier method can be used to obtain the maximal entropy aggregation weighted vector W *, which can aggregate the maximum information from objectives. Eq.(7) can be simplified as Eq.(8) and (9) (8) (9) The initial value of W thus is replaced by the new W * thus optimizing the FLQG-OWA operator.

11. Eq. (10) illustrates aggregating BCs within FA. Eq. (11) illustrates aggregating whole FAs. 4. Aggregating on optimal FLQG-OWA operator is the jth largest of the (10) (11)

12. ALGORITHM Fig.3 Procedure of algorithm on proposing approach.

13. THANK YOU FOR YOUR ATTENTION!