A comparative study of genetic algorithm components in simulation-based optimisation

In this paper, we present a comparative study of different stochastic components of genetic algorithms for simulation-based optimisation of the buffer allocation problem. We explore the effects of elements such as operators, fitness assignment strategies and elitism. Three different recombination operators, incorporated with constraint handling mechanisms such as repair and penalty functions, are examined. Under the shed of the experiments, we incorporate problem specific knowledge to further enhance the practicality of GA in decision making for buffer allocation problem.

[1]  W. Macready,et al.  No Free Lunch Theorems for , 1995 .

[2]  Frederick S. Hillier,et al.  Toward characterizing the optimal allocation of storage space in production line systems with variable processing times , 1993 .

[3]  O. Turchyn,et al.  Comparative Analysis of Metaheuristics Solving Combinatorial Optimization Problems , 2007, 2007 9th International Conference - The Experience of Designing and Applications of CAD Systems in Microelectronics.

[4]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[5]  Mohamed A. Ahmed.  A modification of the simulated annealing algorithm for discrete stochastic optimization , 2007 .

[6]  William L. Maxwell,et al.  The Role of Work-in-Process Inventory in Serial Production Lines , 1988, Oper. Res..

[7]  T. Lacksonen.  Empirical comparison of search algorithms for discrete event simulation , 2001 .

[8]  Christos Alexopoulos,et al.  A Comprehensive Review of Methods for Simulation Output Analysis , 2006, Proceedings of the 2006 Winter Simulation Conference.

[9]  Mark S. Hillier,et al.  Characterizing the optimal allocation of storage space in production line systems with variable processing times , 2000 .

[10]  Nutthapol Asadathorn,et al.  A decomposition approximation for assembly‐disassembly queueing networkswith finite buffer and blocking , 1999, Ann. Oper. Res..

[11]  William W. Cooper,et al.  FLEXIBILITY, ADAPTABILITY, AND EFFICIENCY IN MANUFACTURING SYSTEMS , 2009 .

[12]  Tayfur Altiok,et al.  Performance analysis of manufacturing systems , 1996 .

[13]  Fulya Altiparmak,et al.  Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach , 2007, Appl. Soft Comput..

[14]  Gang George Yin,et al.  Budget-Dependent Convergence Rate of Stochastic Approximation , 1995, SIAM J. Optim..

[15]  Ihsan Sabuncuoglu,et al.  Simulation optimization: A comprehensive review on theory and applications , 2004 .

[16]  Chang Liu,et al.  Buffer allocation via the genetic algorithm , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[17]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[18]  Günter Rudolph,et al.  Convergence of evolutionary algorithms in general search spaces , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[19]  JOHN H. HARRIS,et al.  An algorithm for optimal buffer placement in reliable serial lines , 1999 .

[20]  C..  The throughput rate of multistation unreliable production lines , 2003 .

[21]  Mitsuo Gen,et al.  Genetic Algorithms and Manufacturing Systems Design , 1996 .

[22]  Anton V. Eremeev,et al.  HBBA: hybrid algorithm for buffer allocation in tandem production lines , 2007, J. Intell. Manuf..

[23]  Akif Asil Bulgak,et al.  Analysis and design of split and merge unpaced assembly systems by metamodelling and stochastic search , 2006 .

[24]  Felix T.S. Chan,et al.  Comparative Evaluations of Buffer Allocation Strategies in a Serial Production Line , 2002 .

[25]  Stanley B. Gershwin,et al.  Efficient algorithms for buffer space allocation , 2000, Ann. Oper. Res..

[26]  Rajkumar Roy,et al.  Evolutionary computing in manufacturing industry: an overview of recent applications , 2005, Appl. Soft Comput..

[27]  Akif Asil Bulgak,et al.  Buffer size optimization in asynchronous assembly systems using genetic algorithms , 1995 .

[28]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[29]  Diomidis Spinellis,et al.  Large production line optimization using simulated annealing , 2000 .

[30]  Sigrún Andradóttir,et al.  Simulation optimization with countably infinite feasible regions: Efficiency and convergence , 2006, TOMC.

[31]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[32]  S. S. Chaudhry *,et al.  Application of genetic algorithms in production and operations management: a review , 2005 .

[33]  Ying-Chyi Chou,et al.  Buffer allocation in flow-shop-type production systems with general arrival and service patterns , 2002, Comput. Oper. Res..

[34]  H. T. Papadopoulos,et al.  Throughput rate of multistation reliable production lines with inter station buffers. (I) Exponential case , 1989 .

[35]  Frederick S. Hillier,et al.  On the optimal design of tandem queueing systems with finite buffers , 1995, Queueing Syst. Theory Appl..

[36]  Tayfur Altiok,et al.  Buffer capacity allocation for a desired throughput in production lines , 1998 .

[37]  B. Goyal.  Queuing Theory , 1999 .

[38]  Minghe Sun,et al.  Determining buffer location and size in production lines using tabu search , 1998, Eur. J. Oper. Res..