A genetic algorithm for dynamic facility planning in job shop manufacturing

Product variety, process improvement, and technology improvement can make the original layout plan inefficient. Therefore, improving the current facility layout appears to be a vital mission. This study proposes a model to address the dynamic facility planning problem in job shop manufacturing environment. Since the facility layout problem is an NP-hard problem, an optimal solution is difficult to obtain. This study apply a genetic algorithm (GA) in the proposed model to solve the facility layout problem, considering the handling cost, the facility moving cost, and the facility setup cost. The computational results show that the GA-based approach performs well. Based on the computational results, this study also applies cost–benefit analysis by management perspective for considering whether or not the planners rearrange the original layout.

[1]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[2]  Rong-Hwa Huang,et al.  Ant colony system for job shop scheduling with time windows , 2008 .

[3]  J. Framiñan,et al.  A fitness-based weighting mechanism for multicriteria flowshop scheduling using genetic algorithms , 2009 .

[4]  Jose M. Framiñan,et al.  An adaptive branch and bound approach for transforming job shops into flow shops , 2007, Comput. Ind. Eng..

[5]  Christian Hicks,et al.  A Genetic Algorithm tool for optimising cellular or functional layouts in the capital goods industry , 2006 .

[6]  Ángela M Gómez,et al.  Using genetic algorithms to resolve layout problems in facilities where there are aisles , 2003 .

[7]  Adedeji Badiru,et al.  FLEXPERT: facility layout expert system using fuzzy linguistic relationship codes , 1996 .

[8]  Sunderesh S. Heragu,et al.  Hope: A genetic algorithm for the unequal area facility layout problem , 1998, Comput. Oper. Res..

[9]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[10]  Muh-Cherng Wu,et al.  A project scheduling and staff assignment model considering learning effect , 2006 .

[11]  Manoj Kumar Tiwari,et al.  Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach , 2006 .

[12]  Rrk Sharma,et al.  A review of different approaches to the facility layout problems , 2006 .

[13]  G. H. Lee,et al.  Design of Components and Layout of Machines for Material Handling , 2001 .

[14]  Wing-Keung Wong.  A selection of a fabric-cutting system configuration in different types of apparel manufacturing environments , 2003 .

[15]  H. Ishibuchi,et al.  Multi-objective genetic algorithm and its applications to flowshop scheduling , 1996 .

[16]  A. Kazemi,et al.  A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach , 2009 .

[17]  Richard Muther,et al.  Systematic Layout Planning , 1985 .

[18]  Gertrude P. Pannirselvam,et al.  PROCESS SIMULATION FOR FACILITY LAYOUT , 1999 .

[19]  P. Asokan,et al.  A solution to the facility layout problem having passages and inner structure walls using particle swarm optimization , 2006 .

[20]  Seyed Jafar Sadjadi,et al.  A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study) , 2009 .

[21]  Tsung-Shin Hsu,et al.  Parallel work center scheduling with release dates, due dates, and resource-dependent processing times , 2009 .

[22]  Dan Zetu,et al.  Data input model for virtual reality-aided facility layout , 1998 .

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

[24]  Felix T.S. Chan,et al.  Evaluations of operational control rules in scheduling a flexible manufacturing system , 1999 .

[25]  Taho Yang,et al.  A hierarchical AHP/DEA methodology for the facilities layout design problem , 2003, Eur. J. Oper. Res..

[26]  Ping Ji,et al.  A genetic algorithm approach to optimising component placement and retrieval sequence for chip shooter machines , 2006 .

[27]  Vincent G. Duffy,et al.  Development of an Internet virtual layout system for improving workplace safety , 2003, Comput. Ind..

[28]  Felix T.S. Chan,et al.  A genetic algorithm for facility layout problems , 1998 .

[29]  Tung-I Tsai,et al.  A genetic algorithm for solving the single machine earliness/tardiness problem with distinct due dates and ready times , 2007 .

[30]  Mitsuo Gen,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation , 1996 .

[31]  Wing-Keung Wong,et al.  A genetic-algorithm-based optimization model for scheduling flexible assembly lines , 2008 .

[32]  Yoshiyasu Takefuji,et al.  A neural network approach to facility layout problems , 1996 .

[33]  Andrea Rossi,et al.  Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method , 2007 .

[34]  Young Hae Lee,et al.  A shape-based block layout approach to facility layout problems using hybrid genetic algorithm , 2002 .

[35]  Elwood S. Buffa,et al.  The Facilities Layout Problem in Perspective , 1966 .

[36]  Ravi Sethi,et al.  The Complexity of Flowshop and Jobshop Scheduling , 1976, Math. Oper. Res..

[37]  E. Shayan,et al.  Facilities layout design by genetic algorithms , 1998 .

[38]  Enrique López González,et al.  Genetic optimisation of a fuzzy distribution model , 2000 .

[39]  Shuo-Yan Chou,et al.  An efficient two-staged approach for generating block layouts , 2002, Comput. Oper. Res..