Selection of maritime safety control options for NUC ships using a hybrid group decision-making approach

Maritime safety control is an essential step to mitigate risk in the well-known formal safety assessment framework. The selection of safety control options for NUC (not under control) ships is a challenge due to many influencing factors, together with the different preference formats on the attributes among the multiple involved organizations. This paper proposes a hybrid group decision-making approach to facilitate NUC ship safety control by incorporating fuzzy logic, consistency-based linear programming and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The kernel of the new method is to use fuzzy logic to obtain the attributes values by integrating the associated influencing factors, to employ consistency-based linear programming model to gain the interval weights of attributes, and to introduce TOPSIS for final decision-making. Consequently, this work provides a practical decision framework for NUC ship safety control.

[1]  Maria Hänninen,et al.  Bayesian network model of maritime safety management , 2014, Expert Syst. Appl..

[2]  Jakub Montewka,et al.  Modeling the risk of ship grounding—a literature review from a risk management perspective , 2014 .

[3]  Kevin X. Li,et al.  Ship safety index , 2014 .

[4]  Tayfur Altiok,et al.  Risk Analysis of the Vessel Traffic in the Strait of Istanbul , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  C. Guedes Soares,et al.  Uncertainty in predictions of oil spill trajectories in open sea , 2007 .

[6]  Zhongliang Yue,et al.  Group decision making with multi-attribute interval data , 2013, Inf. Fusion.

[7]  Jakub Montewka,et al.  A framework for risk analysis of maritime transportation systems: A case study for oil spill from tankers in a ship–ship collision , 2015 .

[8]  Jakub Montewka,et al.  A framework for risk assessment for maritime transportation systems - A case study for open sea collisions involving RoPax vessels , 2014, Reliab. Eng. Syst. Saf..

[9]  Jin Wang,et al.  A subjective approach for ballast water risk estimation , 2013 .

[10]  J. Wang,et al.  Formal safety assessment of containerships , 2001 .

[11]  Di Zhang,et al.  An accident data–based approach for congestion risk assessment of inland waterways: A Yangtze River case , 2014 .

[12]  C. Guedes Soares,et al.  Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions , 2011 .

[13]  Renato A. Krohling,et al.  Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea , 2011, Expert Syst. Appl..

[14]  Zeshui Xu,et al.  A method for multiple attribute decision making with incomplete weight information in linguistic setting , 2007, Knowl. Based Syst..

[15]  Wen-Kai Hsu,et al.  An assessment model of safety factors for product tankers in coastal shipping , 2015 .

[16]  Hakan Akyildiz,et al.  A FSA based fuzzy DEMATEL approach for risk assessment of cargo ships at coasts and open seas of Turkey , 2015 .

[17]  Michael Havbro Faber,et al.  Framework for integrated risk assessment , 2010 .

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

[19]  J Gouveia,et al.  Oil spill incidents in Portuguese waters , 2010 .

[20]  Jin Wang,et al.  Formal safety assessment of cruise ships , 2004 .

[21]  Muhammad Usman,et al.  A modified CREAM to human reliability quantification in marine engineering , 2013 .

[22]  Xinping Yan,et al.  Safety Management Performance Assessment for Maritime Safety Administration (MSA) by Using Generalized Belief Rule Base Methodology , 2014 .

[23]  C. Guedes Soares,et al.  Bottom damage scenarios for the hull girder structural assessment , 2013 .

[24]  Zaili Yang,et al.  A subjective risk management approach for modelling of failure induced ship vibrations , 2011 .

[25]  Syamantak Bhattacharya The effectiveness of the ISM Code: A qualitative enquiry , 2012 .

[26]  Zhang Li,et al.  Fuzzy logic-based approach for identifying the risk importance of human error , 2010 .

[27]  Zaili Yang,et al.  A new risk quantification approach in port facility security assessment , 2014 .

[28]  Xinping Yan,et al.  Effectiveness of maritime safety control in different navigation zones using a spatial sequential DEA model: Yangtze River case. , 2015, Accident; analysis and prevention.

[29]  Jin Wang,et al.  Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River , 2013, Reliab. Eng. Syst. Saf..

[30]  S Bonsall,et al.  Use of Fuzzy Evidential Reasoning in Maritime Security Assessment , 2009, Risk analysis : an official publication of the Society for Risk Analysis.

[31]  John Quigley,et al.  Risk analysis of damaged ships – a data-driven Bayesian approach , 2012 .

[32]  Xinping Yan,et al.  A spatial–temporal forensic analysis for inland–water ship collisions using AIS data , 2013 .

[33]  Jin Wang,et al.  Approximate TOPSIS for vessel selection under uncertain environment , 2011, Expert Syst. Appl..

[34]  C. Guedes Soares,et al.  Numerical assessment of factors affecting the survivability of damaged ro–ro ships in waves , 2009 .

[35]  F. Goerlandt,et al.  A probabilistic model for accidental cargo oil outflow from product tankers in a ship-ship collision. , 2014, Marine pollution bulletin.

[36]  P. Silveira,et al.  Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal , 2013, Journal of Navigation.

[37]  Ernestos Tzannatos,et al.  Analysis of accidents in Greek shipping during the pre- and post-ISM period , 2009 .

[38]  T. L. Saaty A Scaling Method for Priorities in Hierarchical Structures , 1977 .

[39]  Metin Celik,et al.  A Hybrid Decision-Making Approach to Measure Effectiveness of Safety Management System Implementations On-Board Ships , 2014 .

[40]  Bing Wu,et al.  Analysis of the operational energy efficiency for inland river ships , 2013 .

[41]  Jin Wang,et al.  A subjective modelling tool applied to formal ship safety assessment , 2000 .

[42]  Dracos Vassalos,et al.  Development of Bayesian network models for risk-based ship design , 2013 .

[43]  Bekir Sahin,et al.  A Novel Process Model for Marine Accident Analysis by using Generic Fuzzy-AHP Algorithm , 2014, Journal of Navigation.

[44]  Jean-François Balmat,et al.  A decision-making system to maritime risk assessment , 2011 .

[45]  Seda Yanik Ugurlu,et al.  Fuzzy rule-based demand forecasting for dynamic pricing of a maritime company , 2014, Knowl. Based Syst..

[46]  Jean-François Balmat,et al.  MAritime RISk Assessment (MARISA), a fuzzy approach to define an individual ship risk factor , 2009 .

[47]  Jin Wang,et al.  Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA , 2008, IEEE Transactions on Reliability.

[48]  Maria Hänninen,et al.  Influences of variables on ship collision probability in a Bayesian belief network model , 2012, Reliab. Eng. Syst. Saf..

[49]  Jian Chen,et al.  MAGDM Linear-Programming Models With Distinct Uncertain Preference Structures , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[50]  C. Guedes Soares,et al.  Monte Carlo simulation of damaged ship survivability , 2005 .

[51]  Zeshui Xu,et al.  Multiple-Attribute Group Decision Making With Different Formats of Preference Information on Attributes , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).