A distributed evolutionary simulation optimization approach for the configuration of multiproduct kanban systems

The performance of multiproduct kanban systems is strongly dependent on a set of numerical and non-numerical parameters, such as the number of kanbans between machines, the transport lot sizes, the safety storage sizes and the sequencing rules. Configuring such systems consists of determining a value for each parameter in order to optimize a performance criterion. The authors propose a distributed simulation optimization method based on evolutionary principles, which is capable of simultaneously taking into account various parameters involved in the configuration of multiproduct kanban systems. In order to improve the quality of the results and to reduce computing time, concurrent searches are managed through a network of workstations. This approach is illustrated by the configuration of a kanban system comprising four machines in line, which process three types of product.

[1]  Erick Cantú-Paz,et al.  A Summary of Research on Parallel Genetic Algorithms , 1995 .

[2]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[3]  Jack P. C. Kleijnen,et al.  Modelisation et simulation dans l'étude de systèmes de production geres en Kanban , 1997 .

[4]  Jonathan F. Bard,et al.  Determining the number of kanbans in a multiproduct, multistage production system , 1991 .

[5]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[6]  Felix Breitenecker,et al.  Coupling Simulators with the Model Interconnection Concept and PVM , 1995, EUROSIM.

[7]  Dirk Van Gucht,et al.  Parallel Genetic Algorithms Applied to the Traveling Salesman Problem , 1991, SIAM J. Optim..

[8]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[9]  Jack P. C. Kleijnen,et al.  Configuring a Pull Production Control Strategy Through a Generic Model , 1997 .

[10]  Blair J. Berkley.  Effect of buffer capacity and sequencing rules on single-card kanban system performance , 1993 .

[11]  Y. Dallery,et al.  Queueing Network Modeling And Analysis Of Kanban Systems , 1992, Proceedings of the Third International Conference on Computer Integrated Manufacturing,.

[12]  John M. Usher,et al.  Integration of evolutionary programming and simulation to optimize a pull production system , 1996 .

[13]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[14]  Andrew Kusiak,et al.  Overview of Kanban systems , 1996 .

[15]  Blair J. Berkley.  Setting minimum performance levels for two-card kanban-controlled lines , 1993 .

[16]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[17]  H. Pierreval,et al.  Using evolutionary algorithms and simulation for the optimization of manufacturing systems , 1997 .

[18]  M.C. Fu,et al.  Simulation optimization , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[19]  Heinrich Braun,et al.  On Solving Travelling Salesman Problems by Genetic Algorithms , 1990, PPSN.

[20]  M. C. Jothishankar,et al.  Metamodelling a Just‐in‐time Kanban System , 1993 .

[21]  Thomas Bäck,et al.  An Overview of Evolutionary Computation , 1993, ECML.

[22]  Lee J. Krajewski,et al.  Kanban, MRP, and Shaping the Manufacturing Environment , 1987 .

[23]  Ahmet Satir,et al.  A kanban-based simulation study of a mixed model just-in-time manufacturing line , 1995 .

[24]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

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

[26]  Damodar Y. Golhar,et al.  The just-in-time philosophy : a literature review , 1991 .

[27]  Mitsutoshi Kojima,et al.  Optimal numbers of two kinds of kanbans in a JIT production system , 1995 .

[28]  Alexander Reinefeld,et al.  Parallel search in discrete optimization problems , 1996, Simul. Pract. Theory.

[29]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

[30]  Marc Gravel,et al.  A review of optimisation models of Kanban-based production systems , 1994 .

[31]  Hsu-Pin Wang,et al.  Optimum number of kanbans between two adjacent workstations in a JIT system , 1991 .