Journal of Intelligent Manufacturing

, Volume 19, Issue 2, pp 175–189 | Cite as

Analysing the performance of an automated pathology specimen handling system

  • R. L. Burdett
  • E. Kozan


This paper determines the performance of a pathology specimen handling system currently under development that performs two fundamental functions, sample tube sorting and sample tube aliquoting. In order to do this a novel capacity model and a simulation model have been developed to analyse the performance of the machine. A capacity model and a simulation model are necessary in order to measure the efficiency of the conveyor system as a means of transferring pucks and tubes between modules as each is insufficient on its own. Furthermore strategies for controlling the machine and eliminating deadlocks are also developed in order to optimise the machine performance and to make it robust. From numerical investigations the best number of pucks and the best puck queue sizes are determined in order to maximise throughput. The results show that a complex relationship exists between the number of pucks, the puck queue sizes and the primary tube arrivals and keeping these components in balance is essential in maintaining system performance.


Conveyor systems Medical equipment Simulation Capacity analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Altiparmak F., Dengiz B., Bulgak A.A. (2007). Buffer allocation and performance modelling in ansynchronous assembly system operations: An artificial neural network metamodelling approach. Applied Soft Computing 7:946–959CrossRefGoogle Scholar
  2. Bozer Y.A., Hsieh Y.J. (2004). Expected waiting times at loading stations in discrete space closed loop conveyors. European Journal of Operational Research 155:516–532CrossRefGoogle Scholar
  3. Burdett R., Kozan E. (2006). Techniques for absolute capacity determination in railways. Transportation Research B 40(8): 616–632CrossRefGoogle Scholar
  4. Chan F.T.S., Chan H.K. (2004). Analysis of dynamic control strategies of an FMS under different scenarios. Robotics and Computer Integrated Manufacturing 20:423–437CrossRefGoogle Scholar
  5. Kost, G. G., Zdanowicz, R. (2005). Modelling of manufacturing systems and robot motions. Journal of Materials Processing Technology, 164–165, 1369–1378.Google Scholar
  6. Kozan E., Burdett R. (2005). A railway capacity determination model and rail access charging methodologies. Transportation Planning and Technology 28(1):27–45CrossRefGoogle Scholar
  7. Kumar S., Sridharan N.R. (2007). Simulation modelling and analysis of tool sharing and part scheduling decisions in single stage multimachine flexible manufacturing systems. Robotics and Computer Integrated Manufacturing 23:361–370CrossRefGoogle Scholar
  8. Odrey N.G., Mejia G. (2005). An augmented petri net approach for error recovery in manufacturing systems control. Robotics and Computer Integrated Manufacturing 21:346–354CrossRefGoogle Scholar
  9. Potts C.N., Whitehead J.D. (2001). Workload balancing and loop layout in the design of a flexible manufacturing system. European Journal of Operational Research 129:326–336CrossRefGoogle Scholar
  10. Russo M.F., Sasso A. (2005). Modelling, analysis, simulation and control of laboratory automation systems using petri nets: Part 1. modelling. Journal of the Association for Laboratory Automation 10(3):172–181CrossRefGoogle Scholar
  11. Schmidt L.C., Jackman J. (2000). Modelling recirculating conveyors with blocking. European Journal of Operational Research 124:422–436CrossRefGoogle Scholar
  12. Tay M.L., Chua P.S.K., Sim S.K., Gao Y. (2005). Development of a flexible and programmable parts feeding system. International Journal of Production Economics 98:227–237CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia

Personalised recommendations