Production Line Performance by Using Queuing Model

Abstract The efficiency of a production system is mainly characterized by batch size and throughput. To ensure system efficiencies, batch size and throughput must be in conformity to achieve optimum utilization. This paper discusses the use of Queuing Network Theory to study the effect of batch size and throughput in optimizing resource utilization, particularly machine resources in a manufacturing system. A factory in the manufacturing industry performing assembly operations in its production lines was the focus of this study. The result of the study was that when batch size and throughput are increased, the utilization also increases proportionately. Bottleneck will occur when the capacity is not enough to meet the demand requirement.

[1]  Maged Dessouky,et al.  An Agent-based Learning Approach for Teaching the Relationship Between Lot Size and Cycle Time , 2002 .

[2]  R.K. Jurgen,et al.  Sarnoff Labs: 'still crazy' but coping , 1988, IEEE Spectrum.

[3]  John R. English,et al.  Designing New Products , 1994 .

[4]  Seraj Yousef Abed,et al.  A simulation study to increase the capacity of a rusk production line , 2008 .

[5]  Russell D. Meller,et al.  The impact of batch retrievals on throughput performance of a carousel system serviced by a storage and retrieval machine , 2013 .

[6]  Brett A. Peters,et al.  Batch picking in narrow-aisle order picking systems with consideration for picker blocking , 2012, Eur. J. Oper. Res..

[7]  Mustufa Haider Abidi,et al.  Analysis of performance measures of flexible manufacturing system , 2012 .

[8]  Matloub Hussain,et al.  Analysis of the bullwhip effect with order batching in multi‐echelon supply chains , 2011 .

[9]  Gino Marchet,et al.  A model for design and performance estimation of pick‐and‐sort order picking systems , 2011 .

[10]  M. E. Merchant Production: a dynamic challenge , 1989 .

[11]  M. E. Merchant Data-driven automation. Production: a dynamic challenge , 1983, IEEE Spectrum.

[12]  Robert Pellerin,et al.  Integrated product specifications and productivity decision making in unreliable manufacturing systems , 2011 .

[13]  Danny J. Johnson,et al.  A framework for reducing manufacturing throughput time , 2003 .

[14]  Hartmut Stadtler,et al.  A lot-sizing and scheduling model for multi-stage flow lines with zero lead times , 2013, Eur. J. Oper. Res..

[15]  John A. Buzacott,et al.  Stochastic models of manufacturing systems , 1993 .

[16]  Mauro Gamberi,et al.  An analytical model to evaluating the implementation of a batch-production-oriented line , 2008 .

[17]  René M. B. M. de Koster,et al.  The impact of order batching and picking area zoning on order picking system performance , 2009, Eur. J. Oper. Res..

[18]  James J. Solberg,et al.  Capacity Planning with a Stochastic Workflow Model , 1981 .

[19]  Wallace J. Hopp,et al.  To Pull or Not to Pull: What Is the Question? , 2004, Manuf. Serv. Oper. Manag..

[20]  Tullio Tolio,et al.  Performance evaluation of production systems monitored by statistical process control and off-line inspections , 2009 .

[21]  Walid Abdul-Kader,et al.  Capacity improvement of an unreliable production line - an analytical approach , 2006, Comput. Oper. Res..

[22]  Jeffrey W. Herrmann,et al.  DESIGN FOR PRODUCTION: A TOOL FOR REDUCING MANUFACTURING CYCLE TIME , 2000 .

[23]  Silvanus T. Enns,et al.  Optimal lot-sizing with capacity constraints and auto-correlated interarrival times , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[24]  Stanley B. Gershwin,et al.  Manufacturing Systems Engineering , 1993 .

[25]  Che Hassan Che Haron,et al.  Application of spreadsheet and queuingnetwork model to capacity optimization inproduct development , 2009 .

[26]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[27]  Mikell P. Groover,et al.  Automation, Production Systems, and Computer-Integrated Manufacturing , 1987 .