APPLYING FUZZY AHP IN SELECTION OF TRANSPORT MODES FOR KINMEN MILITARY LOGISTICS

Kinmen is suited as an important tactical location for Taiwan, despite being a small island with scarce resources. A number of soldiers defend Kinmen for essential military reasons. Therefore, logistics in Kinmen are very important, especially with regard to the military. Generally, necessary goods and materials for Kinmen are transported from Taiwan by ship or air. However, inclement weather in Kinmen often causes delays and difficulties in transportation. This is a serious problem for Kinmen military logistics. To enhance and increase transportation performance, military logistics centers need to evaluate feasible transport modes based on efficiency and cost, and then select an optimal transport mode. In this study, we applied a fuzzy analytic hierarchy process (fuzzy AHP) in the selection of transport modes for Kinmen military logistics. The pairwise comparison comments on selecting candidate transport modes for Kinmen military logistics were from interviews with practical users (i.e., soldiers in Kinmen). By converting interviewees' comments into fuzzy pairwise comparison matrices, fuzzy AHP was utilized to prioritize these matrices in order to find an optimal transport mode for the Kinmen military to execute logistics effectively and efficiently.

[1]  R. F. Brown,et al.  PERFORMANCE EVALUATION , 2019, ISO 22301:2019 and business continuity management – Understand how to plan, implement and enhance a business continuity management system (BCMS).

[2]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[3]  W. Pedrycz,et al.  A fuzzy extension of Saaty's priority theory , 1983 .

[4]  Yu Jing,et al.  A discussion on Extent Analysis Method and applications of fuzzy AHP , 1999, Eur. J. Oper. Res..

[5]  Metin Celik,et al.  Application of fuzzy extended AHP methodology on shipping registry selection: The case of Turkish maritime industry , 2009, Expert Syst. Appl..

[6]  Chin-Shan Lu The impact of carrier service attributes on shipper–carrier partnering relationships: a shipper’s perspective , 2003 .

[7]  Gülçin Büyüközkan,et al.  A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers , 2012, Expert Syst. Appl..

[8]  Yueh-Hsiang Chen,et al.  Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP , 2008, Inf. Sci..

[9]  Yu-Jie Wang,et al.  A fuzzy multi-criteria decision-making model by associating technique for order preference by similarity to ideal solution with relative preference relation , 2014, Inf. Sci..

[10]  Gin-Shuh Liang,et al.  Fuzzy MCDM based on ideal and anti-ideal concepts , 1999, Eur. J. Oper. Res..

[11]  Cengiz Kahraman,et al.  Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey , 2004 .

[12]  Fritz Klocke,et al.  Evaluating alternative production cycles using the extended fuzzy AHP method , 1997, Eur. J. Oper. Res..

[13]  Chen-Tung Chen,et al.  Fuzzy Credibility Relation Method for Multiple Criteria Decision-Making Problems , 1997, Inf. Sci..

[14]  Ching-Hsue Cheng Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function , 1997 .

[15]  J. Kacprzyk,et al.  Group decision making and consensus under fuzzy preferences and fuzzy majority , 1992 .

[16]  Yu-Jie Wang,et al.  The evaluation of financial performance for Taiwan container shipping companies by fuzzy TOPSIS , 2014, Appl. Soft Comput..

[17]  Hsin-Pin Fu,et al.  The impact of market freedom on the adoption of third-party electronic marketplaces: A fuzzy AHP analysis , 2008 .

[18]  Sheng-Hshiung Tsaur,et al.  The evaluation of airline service quality by fuzzy MCDM. , 2002 .

[19]  Vilém Novák,et al.  Fuzzy Set , 2009, Encyclopedia of Database Systems.

[20]  Amy H. I. Lee,et al.  A Performance Evaluation Model Using FAHP/DEA and the Malmquist Productivity Index to Assess the Photovoltaics Industry in Taiwan , 2014 .

[21]  Yu-Jie Wang,et al.  A fuzzy multi-criteria decision-making model based on simple additive weighting method and relative preference relation , 2015, Appl. Soft Comput..

[22]  Hsuan-Shih Lee,et al.  A Fuzzy Multi-criteria Decision Making Model for the Selection of the Distribution Center , 2005, ICNC.

[23]  Chen-Tung Chen,et al.  Aggregation of fuzzy opinions under group decision making , 1996, Fuzzy Sets Syst..

[24]  Yufei Yuan Criteria for evaluating fuzzy ranking methods , 1991 .

[25]  L. C. Leung,et al.  On consistency and ranking of alternatives in fuzzy AHP , 2000, Eur. J. Oper. Res..

[26]  Hsuan-Shih Lee,et al.  On fuzzy preference relation in group decision making , 2005, Int. J. Comput. Math..

[27]  Zhongsheng Hua,et al.  On the extent analysis method for fuzzy AHP and its applications , 2008, Eur. J. Oper. Res..

[28]  Ravi Kant,et al.  A hybrid approach based on fuzzy DEMATEL and FMCDM to predict success of knowledge management adoption in supply chain , 2014, Appl. Soft Comput..

[29]  K. Nakamura Preference relations on a set of fuzzy utilities as a basis for decision making , 1986 .

[30]  Herman Akdag,et al.  The evaluation of hospital service quality by fuzzy MCDM , 2014, Appl. Soft Comput..

[31]  Cheng-Ru Wu,et al.  Using expert technology to select unstable slicing machine to control wafer slicing quality via fuzzy AHP , 2008, Expert Syst. Appl..

[32]  Alev Taskin Gumus,et al.  Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology , 2009, Expert Syst. Appl..