||A Study of Fuzzy Multi-Criteria Decision Making Model for ICT Resource Planning
||Institute of Information Management
Multiple criteria decision making
隨著資訊與通信科技(information and communications technology, ICT)公司提供更趨多元化的服務，對通信品質的要求更為嚴格，有效的電力規劃，才能提升對新技術發展的應變能力，進而創造最大利潤與競爭力。過去電力規劃賴於資深員工的內隱知識及經驗，但組織常面臨資深員工退休領域知識傳承的問題，如台灣最大的ICT公司-中華電信，因此本研究建構以專家內隱知識與經驗為基礎之系統化電力規劃模型，採用凱利方格分析法取得具共識之評估準則，並透過多準則決策方法之TOPSIS 正負理想解概念，用以評估營運績效。
本研究適用於群體決策情境，藉由決策者之間的共識的變異程度，來決定整合策略，並提出first aggregation與last aggregation兩種整合策略，first aggregation即是將個別決策者的決策矩陣整合為群體矩陣，last aggregation則是將別決策者正、負理想解的分離測度整合為群體分離度，進而由此兩種整合策略探討個別決策與群體結果的共識度。
It is critical for information and communications technology (ICT) companies to carry our effective power planning, in order to support the growing number of services they provide, and this traditionally relies on the tacit knowledge and experience of senior staff. The loss of such domain knowledge resulting from the retirement of older staff is thus an important issue for organizations such as Chunghwa Telecomm (CHT), the largest ICT operator in Taiwan. This study therefore develops a novel power planning model using a multi-criteria operational performance evaluation, based on the senior staff’s tacit domain knowledge and experience.
A group version of the repertory grid and fuzzy TOPSIS approaches is applied to elicit a set of evaluation criteria that senior staff agree on, and then the priorities of the telecom rooms are evaluated against this. In extending TOPSIS to a group decision environment, the implementation of aggregation in the TOPSIS procedure would vary the final decision. A novel decision aggregation strategy with respect to the degree of variation among decision makers is thus proposed: (1) first aggregation: aggregating the individual decisions into a group, (2) last aggregation: aggregating the individual separation measures to a group.
In addition, a new factor, reflecting the attitudes of the decision makers with respect to the degree of strictness, is defined to determine the superiority and inferiority of each alternative compared to the others. Furthermore, a quantitative assessment is carried out to analyze its impact on the ranking results objectively. The proposed model may help ICT organizations to more effectively manage their power resources, and thus obtain competitive advantages.
List of Figures VII
List of Tables VIII
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Motivation 2
1.3 Research Objectives 5
Chapter 2 Literature Review 7
2.1 Repertory Grids 7
2.2 TOPSIS 8
2.3 Group Decisions 9
Chapter 3 Research Methodology 16
3.1 Phase I: Operational performance measure 18
3.2 Phase II: Decision aggregation 22
3.2.1 First Aggregation 23
3.2.2 Last Aggregation 26
3.3 Individual Decision 27
Chapter 4 Experimental Results and Analysis 30
4.1 Phase I: Operational performance measure 30
4.2 Phase II: Decision aggregation 33
4.3 Phase III: Cluster alternatives 35
Chapter 5 Discussion 36
5.1 The result of the last aggregation strategy 36
5.2 The results of individual decision 38
5.3 Discussion of attitude factor 39
5.4 Quantitative assessment of aggregation strategies 40
5.5 Ranking analysis of λ values 42
5.6 Derive a planning policy for the power resources 43
Chapter 6 Conclusion and Future Work 46
6.1 Conclusion 46
6.2 Impact of decision analysis on decision makers 47
6.3 Managerial implications of P2M 49
6.4 Limitations and directions for future research 50
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