Modelling Interdependency Among Attributes in MCDM: Its Application in Port Performance Measurement

The measurement of port and terminal performance may require an essential understanding of the cause-effect relationship among the influencing factors and criteria. Port performance indicators (PPIs) can interact with and feedback themselves (inner dependency) and/or each other (outer dependency). However previous studies have done little on the analysis of interdependency among the PPIs. This chapter aims to propose a new conceptual PPIs’ interdependency model using a hybrid approach of a fuzzy logic based evidential reasoning (FER), a decision making trial and evaluation laboratory (DEMATEL) and an analytic hierarchy process (AHP). The combined approach of DEMATEL and AHP is applied to calculate the weights of dependent PPIs which are used as a part of the FER model to measure and analyse the performance of six container terminals in Korea from different port stakeholders’ perspectives. The empirical results indicate that the hybrid approach offers a diagnostic instrument to container terminals in identifying the particular areas for improvement to enhance their competitiveness.

[1]  S. Pettit,et al.  An assessment of the integration of seaports into supply chains using a structural equation model , 2013 .

[2]  Jose L. Tongzon,et al.  DETERMINANTS OF PORT PERFORMANCE AND EFFICIENCY , 1995 .

[3]  Khalid Bichou,et al.  A logistics and supply chain management approach to port performance measurement , 2004 .

[4]  Dong-Wook Song,et al.  Port integration in global supply chains: measures and implications for maritime logistics , 2009 .

[5]  Da Ruan,et al.  Logistics tool selection with two-phase fuzzy multi criteria decision making: A case study for personal digital assistant selection , 2012, Expert Syst. Appl..

[6]  Dorothy E. Leidner,et al.  An Empirical Examination of the Influence of Organizational Culture on Knowledge Management Practices , 2005, J. Manag. Inf. Syst..

[7]  Peter Bernard Marlow,et al.  Measuring lean ports performance , 2003 .

[8]  H Journee,et al.  Survey on environmental monitoring requirements of European ports. , 2009, Journal of environmental management.

[9]  Khalid Bichou,et al.  Chapter 24 Review of Port Performance Approaches and a Supply Chain Framework to Port Performance Benchmarking , 2006 .

[10]  Mary R. Brooks,et al.  Measuring port effectiveness in user service delivery: What really determines users' evaluations of port service delivery? , 2013 .

[11]  A. Gabus,et al.  Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility , 1973 .

[12]  Zaili Yang,et al.  Modelling port choice in an uncertain environment , 2014 .

[13]  J. Barney.  Firm Resources and Sustained Competitive Advantage , 1991 .

[14]  P. Panayides,et al.  Global supply chain and port/terminal: integration and competitiveness , 2008 .

[15]  Jei-Zheng Wu,et al.  Applying Analytic Network Process (ANP) to Rank Critical Success Factors of Waterfront Redevelopment , 2013 .

[16]  Jafar Razmi,et al.  Assessing the impact of information technology on firm performance considering the role of intervening variables: organizational infrastructures and business processes reengineering , 2007 .

[17]  Jian-Bo Yang,et al.  The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty , 2006, Eur. J. Oper. Res..

[18]  T. Saaty.  Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP) , 2004 .

[19]  Jian-Bo Yang,et al.  On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty , 2002, IEEE Trans. Syst. Man Cybern. Part A.

[20]  Anthony Kenneth Charles Beresford,et al.  Port evolution and performance in changing logistics environments , 2011 .

[21]  Kevin Cullinane,et al.  A stochastic frontier model of the efficiency of major container terminals in Asia: assessing the influence of administrative and ownership structures , 2002 .

[22]  Chung Yu,et al.  A STUDY ON INTEGRATED PORT PERFORMANCE COMPARISON BASED ON THE CONCEPT OF BALANCED SCORECARD , 2003 .

[23]  Y Roll,et al.  Port performance comparison applying data envelopment analysis (DEA) , 1993 .

[24]  Anthony Kenneth Charles Beresford,et al.  An Application of AHP on Transhipment Port Selection: A Global Perspective , 2004 .

[25]  Mary R. Brooks,et al.  Chapter 25 Issues in Measuring Port Devolution Program Performance: A Managerial Perspective , 2006 .

[26]  R. Kaplan,et al.  Measuring the strategic readiness of intangible assets. , 2004, Harvard business review.

[27]  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..

[28]  Jin Wang,et al.  Use of hybrid multiple uncertain attribute decision making techniques in safety management , 2009, Expert Syst. Appl..

[29]  Jian-Bo Yang,et al.  Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties , 2001, Eur. J. Oper. Res..

[30]  J. Tanner.  UNCTAD 4: Fourth United Nations conference on trade and development, Nairobi 5-28 may, 1976 , 1976 .

[31]  Gwo-Hshiung Tzeng,et al.  Airline safety measurement using a hybrid model , 2007 .

[32]  Richard P. Bagozzi,et al.  Assessing Construct Validity in Organizational Research , 1991 .

[33]  P. Langen,et al.  Clustering and performance: the case of maritime clustering in The Netherlands , 2002 .

[34]  E. Peris-Mora,et al.  Development of a system of indicators for sustainable port management. , 2005, Marine pollution bulletin.