A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication

Display Omitted A bi-objective cell formation problem considering machine duplication and alternative process routings is presented.A hybrid algorithm is developed to solve the problem.Comparisons are carried out between the proposed approach and the existing approaches. Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.

[1]  M. S. Akturk,et al.  Part-machine grouping using a multi-objective cluster analysis , 1996 .

[2]  Jaydeep Balakrishnan,et al.  Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: Formulation and comparison , 1995 .

[3]  Oscar Castillo,et al.  A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation , 2014, Expert Syst. Appl..

[4]  Yunfeng Wang,et al.  A genetic algorithm for cellular manufacturing design and layout , 2007, Eur. J. Oper. Res..

[5]  Prasun Das,et al.  Group technology based adaptive cell formation using predator-prey genetic algorithm , 2012, Appl. Soft Comput..

[6]  Ping-Teng Chang,et al.  A multisolution method for cell formation—Exploring practical alternatives in group technology manufacturing☆ , 2000 .

[7]  Shine-Der Lee,et al.  Joint determination of machine cells and linear intercell layout , 2004, Comput. Oper. Res..

[8]  Zülal Güngör,et al.  Modeling of a manufacturing cell design problem with fuzzy multi-objective parametric programming , 2009, Math. Comput. Model..

[9]  Oscar Castillo,et al.  Modification of the Bat Algorithm Using Type-2 Fuzzy Logic for Dynamical Parameter Adaptation , 2017, Nature-Inspired Design of Hybrid Intelligent Systems.

[10]  Felix T.S. Chan,et al.  Two-stage approach for machine-part grouping and cell layout problems , 2006 .

[11]  Anan Mungwattana,et al.  Design of Cellular Manufacturing Systems for Dynamic and Uncertain Production Requirements with Presence of Routing Flexibility , 2000 .

[12]  F. Boctor.  A Jinear formulation of the machine-part cell formation problem , 1991 .

[13]  Maghsud Solimanpur,et al.  A multi-objective genetic algorithm approach to the design of cellular manufacturing systems , 2004 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Tai-Hsi Wu,et al.  Hybrid simulated annealing algorithm with mutation operator to the cell formation problem with alternative process routings , 2009, Expert Syst. Appl..

[16]  Aydin Aghajani,et al.  A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis , 2014 .

[17]  M. Selim Akturk,et al.  Cellular manufacturing system design using a holonistic approach , 2000 .

[18]  Maghsud Solimanpur,et al.  Heuristic Approaches for Cell Formation in Cellular Manufacturing , 2010, J. Softw. Eng. Appl..

[19]  Marc Gravel,et al.  Efficient solutions to the cell-formation problem with multiple routings via a double-loop genetic algorithm , 1998, Eur. J. Oper. Res..

[20]  Michael Mutingi,et al.  A genetic algorithm approach to cellular manufacturing systems , 2001 .

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

[22]  Jamal Arkat,et al.  Multi-objective genetic algorithm for cell formation problem considering cellular layout and operations scheduling , 2012, Int. J. Comput. Integr. Manuf..

[23]  Marco Bortolini,et al.  An hybrid procedure for machine duplication in cellular manufacturing systems , 2011 .

[24]  Oscar Castillo,et al.  Dynamic parameter adaptation in Ant Colony Optimization using a fuzzy system for TSP problems , 2015, IFSA-EUSFLAT.

[25]  Mohammad Mohammadi,et al.  A novel approach for considering layout problem in cellular manufacturing systems with alternative processing routings and subcontracting approach , 2014 .

[26]  Nancy Lea Hyer,et al.  Cellular manufacturing in the U.S. industry: a survey of users , 1989 .

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

[28]  D. E. Goldberg,et al.  Genetic Algorithms in Search, Optimization & Machine Learning , 1989 .

[29]  Basilis Boutsinas,et al.  Production , Manufacturing and Logistics Machine-part cell formation using biclustering , 2013 .

[30]  Eduardo Vila Gonçalves Filho,et al.  A group genetic algorithm for the machine cell formation problem , 2006 .

[31]  Nadia Bhuiyan,et al.  A dynamic programming approach to GA-based heuristic for multi-period CF problems , 2014 .

[32]  John McAuley,et al.  Machine grouping for efficient production , 1972 .

[33]  Pedro M. Vilarinho,et al.  A simulated annealing approach for manufacturing cell formation with multiple identical machines , 2003, Eur. J. Oper. Res..

[34]  Mohammad Saidi-Mehrabad,et al.  An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis , 2011, Expert Syst. Appl..

[35]  Reza Tavakkoli-Moghaddam,et al.  A hybrid electromagnetism-like algorithm for dynamic inter/intra-cell layout problem , 2014, Int. J. Comput. Integr. Manuf..

[36]  Chien-Wei Wu,et al.  An efficient approach to determine cell formation, cell layout and intracellular machine sequence in cellular manufacturing systems , 2013, Comput. Ind. Eng..

[37]  Mauricio G. C. Resende,et al.  An evolutionary algorithm for manufacturing cell formation , 2004, Comput. Ind. Eng..

[38]  Ramasamy Panneerselvam,et al.  Hybrid Genetic Algorithm for Machine-Component Cell Formation , 2015 .

[39]  Chia-Hsien Lin,et al.  A PSO-based approach to cell formation problems with alternative process routings , 2012 .

[40]  Yong Yin,et al.  Similarity coefficient methods applied to the cell formation problem: a comparative investigation , 2005, Comput. Ind. Eng..

[41]  Tai-Hsi Wu,et al.  An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations , 2011, Comput. Ind. Eng..

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

[43]  Soroush Saghafian,et al.  Integrative Cell Formation and Layout Design in Cellular Manufacturing Systems , 2009 .

[44]  Herman R. Leep,et al.  Forming part families by using genetic algorithm and designing machine cells under demand changes , 2006, Comput. Oper. Res..

[45]  Divakar Rajamani,et al.  The trade-off between intracell and intercell moves in group technology cell formation , 2001 .

[46]  Tolga Bektas,et al.  Integrated cellular manufacturing systems design with production planning and dynamic system reconfiguration , 2009, Eur. J. Oper. Res..

[47]  Y. Won,et al.  Multiple criteria clustering algorithm for solving the group technology problem with multiple process routings , 1997 .

[48]  Wilson L. Price,et al.  Hybrid genetic approach for solving large-scale capacitated cell formation problems with multiple routings , 2006, Eur. J. Oper. Res..

[49]  Bharatendu Srivastava,et al.  Simulated annealing procedures for forming machine cells in group technology , 1994 .

[50]  J. King,et al.  Machine-component group formation in group technology: review and extension , 1982 .