Selecting optimum maintenance activity plans by a unique simulation-multivariate approach

This study presents an integrated computer simulation-multivariate approach for selection of optimum maintenance activity plans. First, maintenance activities are modelled by computer simulation. Production and maintenance functions are estimated using historical data. Then, simulation is carried out for different scenarios, which are combinations of periodic maintenance and number of maintenance crew. Several outputs including machines and operators’ availability, reliability, efficiency and queue length are computed. Four multivariate methods, namely data envelopment analysis (DEA), artificial neural network (ANN), principal component analysis (PCA) and numerical taxonomy (NT) are used to select the optimum policy. Finally, statistical methods are used to select the most reliable method for selecting the optimum scenarios.

[1]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[2]  N. Petersen,et al.  Chance constrained efficiency evaluation , 1995 .

[3]  Jun Ni,et al.  Maintenance scheduling in manufacturing systems based on predicted machine degradation , 2008, J. Intell. Manuf..

[4]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[5]  Mehdi Hosseinabadi Farahani,et al.  An integrated GA-DEA algorithm for determining the most effective maintenance policy for a k -out-of- n problem , 2013, Journal of Intelligent Manufacturing.

[6]  Ali Azadeh,et al.  A Meta heuristic approach for performance assessment of production units , 2009, Expert Syst. Appl..

[7]  Ali Azadeh,et al.  An integrated multi-criteria Taguchi computer simulation-DEA approach for optimum maintenance policy and planning by incorporating learning effects , 2013 .

[8]  Shengyong Chen,et al.  Efficient multi-objective tabu search for emergency equipment maintenance scheduling in disaster rescue , 2013, Optim. Lett..

[9]  J. Cubbin,et al.  Public Sector Efficiency Measurement: Applications of Data Envelopment Analysis , 1992 .

[10]  A D Bates,et al.  Energy coupling in Escherichia coli DNA gyrase: the relationship between nucleotide binding, strand passage, and DNA supercoiling. , 1996, Biochemistry.

[11]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[12]  G. Kane.  Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[13]  Vladica Mijailovic.  Probabilistic method for planning of maintenance activities of substation components , 2003 .

[14]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[15]  John A. Buzacott,et al.  Equipment reliability and maintenance , 1985 .

[16]  Chung-Ho Wang,et al.  Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm , 2014, J. Intell. Manuf..

[17]  Hongzhou Wang,et al.  A survey of maintenance policies of deteriorating systems , 2002, Eur. J. Oper. Res..

[18]  C. Richard Cassady,et al.  Integrating preventive maintenance planning and production scheduling for a single machine , 2005, IEEE Transactions on Reliability.

[19]  Wonsuk Park,et al.  Robust multi-objective maintenance planning of deteriorating bridges against uncertainty in performance model , 2013, Adv. Eng. Softw..

[20]  Joe Zhu,et al.  Data envelopment analysis vs. principal component analysis: An illustrative study of economic performance of Chinese cities , 1998, Eur. J. Oper. Res..

[21]  Y. S. Sherif,et al.  Optimal maintenance models for systems subject to failure–A Review , 1981 .

[22]  Ali Azadeh,et al.  Location optimization of solar plants by an integrated hierarchical DEA PCA approach , 2008 .

[23]  Shozo Takata.  Life Cycle Maintenance , 1999 .

[24]  Andrew Higgins.  Scheduling of railway track maintenance activities and crews , 1998, J. Oper. Res. Soc..

[25]  Quey-Jen Yeh.  The Application of Data Envelopment Analysis in Conjunction with Financial Ratios for Bank Performance Evaluation , 1996 .

[26]  Ali Azadeh,et al.  Integration of DEA and AHP with computer simulation for railway system improvement and optimization , 2008, Appl. Math. Comput..

[27]  Dan M. Frangopol,et al.  Bridge Annual Maintenance Prioritization under Uncertainty by Multiobjective Combinatorial Optimization , 2005 .

[28]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[29]  R. Färe,et al.  An activity analysis model of the environmental performance of firms—application to fossil-fuel-fired electric utilities , 1996 .

[30]  Lionel Amodeo,et al.  Bi-objective optimization algorithms for joint production and maintenance scheduling: application to the parallel machine problem , 2009, J. Intell. Manuf..

[31]  Uwe Jensen.  Stochastic Models of Reliability and Maintenance: An Overview , 1996 .

[32]  Matthew R. Horrocks,et al.  A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach , 2006 .

[33]  Toshio Nakagawa,et al.  Bibliography for Reliability and Availability of Stochastic Systems , 1976 .

[34]  Mohammad Sheikhalishahi,et al.  An integrated simulation-data envelopment analysis approach for maintenance activities planning , 2014, Int. J. Comput. Integr. Manuf..

[35]  A. Alan B. Pritsker,et al.  Simulation with Visual SLAM and AweSim , 1997 .

[36]  J. M. Worm,et al.  Model based decision support for planning of road maintenance , 1996 .

[37]  Alaa Chateauneuf,et al.  Opportunistic policy for optimal preventive maintenance of a multi-component system in continuous operating units , 2009, Comput. Chem. Eng..

[38]  Richard M. Feldman,et al.  A survey of preventive maintenance models for stochastically deteriorating single-unit systems , 1989 .

[39]  S. M. Asadzadeh,et al.  A flexible neural network-fuzzy data envelopment analysis approach for location optimization of solar plants with uncertainty and complexity , 2011 .

[40]  H. Pham,et al.  Invited reviewImperfect maintenance , 1996 .

[41]  Lionel Amodeo,et al.  Bi-Objective Ant Colony Optimization approach to optimize production and maintenance scheduling , 2010, Comput. Oper. Res..

[42]  E. Wailand Bessent,et al.  Determining the Comparative Efficiency of Schools through Data Envelopment Analysis , 1980 .

[43]  M. Sheikhalishahi,et al.  An integrated fuzzy simulation–fuzzy data envelopment analysis approach for optimum maintenance planning , 2014, Int. J. Comput. Integr. Manuf..

[44]  Shozo Takata,et al.  Life cycle maintenance planning method in consideration of operation and maintenance integration , 2012 .

[45]  Yousef Shafahi,et al.  Bus maintenance systems and maintenance scheduling: model formulations and solutions , 2002 .

[46]  Mikkel Thorup,et al.  On the approximability of numerical taxonomy (fitting distances by tree metrics) , 1996, SODA '96.

[47]  Dragan Banjevic,et al.  Multi-threaded simulated annealing for a bi-objective maintenance scheduling problem , 2012 .

[48]  M. Sheikhalishahi,et al.  A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem , 2013 .

[49]  Rommert Dekker,et al.  Applications of maintenance optimization models : a review and analysis , 1996 .