A fuzzy genetic algorithm approach for analysing maintenance cost of high risk liquefied natural gas carrier systems under uncertainty

A Fuzzy Genetic Algorithm (FGA) is used to treat uncertainties associated with unit costs of maintenance of Liquefied Natural Gas (LNG) carrier systems such as a containment system and a transfer arm. A Fuzzy Rule Base (FRB) is established to identify the unit costs of maintenance of the LNG containment system and transfer arm. It includes 125 LNG carrier maintenance cost rules, with technical consultancy cost, maintenance duration, and spare part cost as the antecedents and maintenance cost as the consequent. The outcome from the FRB is used to optimise a risk model using a Genetic Algorithm (GA) approach to find the optimal maintenance cost of each system with provided information on their respective time of interest, failure probability, failure frequency and maintenance cost of the whole LNG carrier system.

[1]  Jin Wang,et al.  Application of genetic algorithm to risk-based maintenance operations of liquefied natural gas carrier systems , 2011 .

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

[3]  T. G. Theofanous,et al.  Risk assessment of membrane type LNG storage tanks in Korea-based on fault tree analysis , 2005 .

[4]  Chang Wook Ahn,et al.  On the practical genetic algorithms , 2005, GECCO '05.

[5]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[6]  Jin Wang,et al.  A fuzzy-logic-based approach to qualitative safety modelling for marine systems , 2001, Reliab. Eng. Syst. Saf..

[7]  Tao Jiang,et al.  Techniques and Applications of Fuzzy Theory in Generalized Defuzzification Methods and Their Utilization in Parameter Learning Techniques , 1999 .

[8]  Williams,et al.  Human Error Assessment and Management in Port Operations using Fuzzy AHP , 2006 .

[9]  John Andrews,et al.  Reliability and Risk Assessment , 1994 .

[10]  Jian-Bo Yang,et al.  Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean , 2009, Expert Syst. Appl..

[11]  Michelle Michot Foss,et al.  LNG safety and security , 2008 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  K P Soman,et al.  Fuzzy Fault Tree Analysis using Resolution Identity , 1993 .

[14]  John Crocker,et al.  Effectiveness of maintenance , 1999 .

[15]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[16]  Christine M. Anderson-Cook.  Practical Genetic Algorithms (2nd ed.) , 2005 .

[17]  Chee Kiong Soh,et al.  Fuzzy Controlled Genetic Algorithm Search for Shape Optimization , 1996 .

[18]  Ajit Srividya,et al.  Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach , 2007, Reliab. Eng. Syst. Saf..

[19]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[20]  Ian Jenkinson,et al.  The use of fuzzy set modelling for maintenance planning in a manufacturing industry , 2010 .

[21]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[22]  Laurence Tianruo Yang,et al.  Fuzzy Logic with Engineering Applications , 1999 .

[23]  Bart Kosko,et al.  Fuzzy Systems as Universal Approximators , 1994, IEEE Trans. Computers.

[24]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[25]  Jin Wang,et al.  Technology and Safety of Marine Systems , 2003 .

[26]  Hans Löfsten.  Management of industrial maintenance – economic evaluation of maintenance policies , 1999 .

[27]  Rose Baker,et al.  Developing and Testing the Delay-Time Model , 1993 .

[28]  David Coley,et al.  Introduction to Genetic Algorithms for Scientists and Engineers , 1999 .

[29]  Chee Kiong Soh,et al.  Fuzzy Logic Integrated Genetic Programming for Optimization and Design , 2000 .

[30]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .