A new dynamic cell formation model considering machine sequence and labor-intensive situation

This paper presents a new two-phase dynamic cellular manufacturing model in a labor-intensive situation. In the first phase the formation of machines in cells is selected. The output of this phase has been used as an input for the second phase to select the best number of operators. In the first phase objectives are minimizing total cost of material handling, relocation, constant and varying machine utilization, and minimizing machine workload imbalanced simultaneously. In the second phase the objective is minimizing the number of workers. The first phase is solved using hybrid particle swarm optimization (PSO) and epsilon constraint. The second phase is optimized using simulation with Visual Slam. Results are compared with GAMS solver and commercial CPLEX solver.

[1]  Christophe Caux,et al.  Cell formation with alternative process plans and machine capacity constraints: A new combined approach , 2000 .

[2]  C. R. Bector,et al.  Cell formation considering fuzzy demand and machine capacity , 1997 .

[3]  Kai-Ling Mak,et al.  Production Scheduling and Cell Formation for Virtual Cellular Manufacturing Systems , 2002 .

[4]  Alluru Gopala Krishna,et al.  Multi-objective optimisation of surface grinding operations using scatter search approach , 2006 .

[5]  Inyong Ham,et al.  Group Technology: Applications to Production Management , 2011 .

[6]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[7]  Chang-Chun Tsai,et al.  Optimization of manufacturing cell formation with a multi-functional mathematical programming model , 2006 .

[8]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Asoo J. Vakharia,et al.  Cell formation in group technology: review, evaluation and directions for future research , 1998 .

[10]  Hark Hwang,et al.  Routes selection for the cell formation problem with alternative part process plans , 1996 .

[11]  Stella Sofianopoulou,et al.  An efficient ant colony optimization system for the manufacturing cells formation problem , 2008 .

[12]  Ali Azadeh,et al.  An integrated fuzzy DEA–fuzzy C-means–simulation for optimization of operator allocation in cellular manufacturing systems , 2010 .

[13]  Lan Hu,et al.  Formation of manufacturing cells based on material flows , 2005 .

[14]  R. Ramesh,et al.  Concurrent optimization of assembly tolerances for quality with position control using scatter search approach , 2007 .

[15]  Jamal Arkat,et al.  Applying simulated annealing to cellular manufacturing system design , 2007 .

[16]  Nima Safaei,et al.  A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system , 2008, Eur. J. Oper. Res..

[17]  Jun-Geol Baek,et al.  A machine cell formation algorithm for simultaneously minimising machine workload imbalances and inter-cell part movements , 2005 .

[18]  Jiafu Tang,et al.  Optimization of the multi-objective dynamic cell formation problem using a scatter search approach , 2009 .