A Group Technology Based Methodology for Maintenance Scheduling

A novel Group Technology (GT) based methodology for maintenance scheduling of complex series–parallel production system is proposed. Hierarchical Clustering (HC) method is employed to group facilities according to their similarities in location, facility type, maintenance type, structural position and maintenance time. And weight allocation of these considered factors is optimized using Tabu Search (TS). Simulation results validate the methodology’s effectiveness in reducing maintenance cost.

[1]  Asoo J. Vakharia,et al.  A comparative investigation of hierarchical clustering techniques and dissimilarity measures applied to the cell formation problem , 1995 .

[2]  Amir Khanlari,et al.  Prioritizing equipments for preventive maintenance (PM) activities using fuzzy rules , 2008, Comput. Ind. Eng..

[3]  Joaquín A. Pacheco,et al.  A heuristic approach for a scheduling problem with periodic maintenance and sequence-dependent setup times , 2011, Comput. Math. Appl..

[4]  Jihong Yan,et al.  Reuse oriented group maintenance scheduling based on Hybrid Genetic Algorithm and Tabu Search , 2011, 2011 IEEE International Conference on Industrial Engineering and Engineering Management.

[5]  Abdelhakim Abdelhadi.  Preventive maintenance grouping using similarity coefficient methodology , 2010 .

[6]  Pinaki Mazumder,et al.  SAGA : a unification of the genetic algorithm with simulated annealing and its application to macro-cell placement , 1994, Proceedings of 7th International Conference on VLSI Design.

[7]  R. Radner,et al.  Opportunistic Replacement of a Single Part in the Presence of Several Monitored Parts , 1963 .

[8]  Zhigang Tian,et al.  Condition based maintenance optimization for multi-component systems using proportional hazards model , 2011, Reliab. Eng. Syst. Saf..

[9]  Tongdan Jin,et al.  Condition based maintenance optimization for wind power generation systems under continuous monitoring , 2011 .

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

[11]  Luca Podofillini,et al.  Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation , 2002, Reliab. Eng. Syst. Saf..

[12]  Zülal Güngör,et al.  Applying K-harmonic means clustering to the part-machine classification problem , 2009, Expert Syst. Appl..

[13]  I. Gertsbakh.  Optimal group preventive maintenance of a system with observable state parameter , 1984 .

[14]  Dong Ho Park,et al.  Cost minimization for periodic maintenance policy of a system subject to slow degradation , 2000, Reliab. Eng. Syst. Saf..

[15]  S. Sheu,et al.  A generalized group maintenance policy , 1997 .

[16]  Peter H. Ritchken,et al.  m,T Group Maintenance Policies , 1990 .