The search for the optimal number of kanbans in unstable assembly-tree layout systems under intensive loading conditions

The JIT system and its operational techniques have shown noticeable advantages when applied in an ideal factory. The steadiness of demand and production times and the reduction of set-up times represent the key factor necessary in order to correctly execute JIT. Ideal environments are designed to work with smooth and stable demand patterns, constant and balanced processing times, small lot sizes and without scraps and reworks. However, these conditions are difficult to realise in real productive systems. In these contexts, the increase of operational costs, owing to the growth of inventories, necessary to match demand, often causes the failure of many JIT implementations. For these reasons, during the past years researchers have been investigating the issues related to JIT implementation in unsteady productive organisations. In this area, the kanban system, responsible for the exact propagation of information and for inventory control, is the most widely researched control mechanism. Literature proposes various kanban systems; in all cases the determination of the number of kanbans depends both on the management method chosen at each stage of the process as well as on the fluctuation of operative variables. This study deals with the problem of choosing the optimal number of kanbans in a multi-stage productive environment organised in an assembly-tree layout. In particular, this paper proposes a heuristic procedure to determine the number of kanbans and compares it with the traditional methods applied in manufacturing contexts.

[1]  S. David Wu,et al.  A new heuristic method for mixed model assembly line balancing problem , 2003 .

[2]  Patrick R. McMullen,et al.  An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives , 2001, Artif. Intell. Eng..

[3]  Fulya Altiparmak,et al.  A comparison of the performance of artificial intelligence techniques for optimizing the number of kanbans , 2002, J. Oper. Res. Soc..

[4]  Y. Monden Toyota Production System: Practical Approach to Production Management , 1983 .

[5]  Tamás Kis,et al.  On the complexity of the car sequencing problem , 2004, Oper. Res. Lett..

[6]  Feryal Erhun,et al.  An overview of design and operational issues of kanban systems , 1999 .

[7]  Marimuthu Palaniswami,et al.  Traditional heuristic versus Hopfield neural network approaches to a car sequencing problem , 1996 .

[8]  Ruhul A. Sarker,et al.  An optimal batch size for a JIT manufacturing system , 2002 .

[9]  Asbjoern M. Bonvik,et al.  A comparison of production-line control mechanisms , 1997 .

[10]  Katsuhisa Ohno,et al.  Algorithms for sequencing mixed models on an assembly line in a JIT production system , 1997 .

[11]  Patrick R. McMullen,et al.  A simulated annealing approach to mixed-model sequencing with multiple objectives on a just-in-time line , 2000 .

[12]  Mehmet Savsar Simulation analysis of a pull-push system for an electronic assembly line , 1997 .

[13]  Ömer Faruk Baykoç,et al.  Simulation modelling and analysis of a JIT production system , 1998 .

[14]  Felix T.S. Chan Effect of kanban size on just-in-time manufacturing systems , 2001 .

[15]  Fulya Altiparmak,et al.  The optimization of number of kanbans with genetic algorithms, simulated annealing and tabu search , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  Victor R. Prybutok,et al.  The relationship between JIT practices and type of production system , 2001 .

[17]  Rosemary R. Fullerton,et al.  AN EXAMINATION OF THE RELATIONSHIPS BETWEEN JIT AND FINANCIAL PERFORMANCE , 2003 .

[18]  Mahmoud M. Yasin,et al.  Organizational modifications to support JIT implementation in manufacturing and service operations , 2003 .

[19]  Antonio Costa,et al.  A comparative analysis of sequencing heuristics for solving the Toyota Goal Chasing problem , 2004 .

[20]  Alf Kimms,et al.  Sequencing JIT Mixed-Model Assembly Lines Under Station-Load and Part-Usage Constraints , 2001, Manag. Sci..

[21]  Rosemary R. Fullerton,et al.  The production performance benefits from JIT implementation , 2001 .

[22]  Alf Kimms,et al.  Algorithms for the car sequencing and the level scheduling problem , 2006, J. Sched..

[23]  Zhaoying Zhou,et al.  A note on Toyota's goal of sequencing mixed models on an assembly line , 1999 .

[24]  Shigeji Miyazaki,et al.  Sequencing Method for Products in Consideration of Assembly Time Based on New Objective Function , 1999 .

[25]  Mayank Chaturvedi,et al.  Simulation modelling and analysis of a JIT production system , 1992 .

[26]  René Gélinas The Just-In-Time implementation project , 1999 .