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Ant colony optimisation of a community pharmacy dispensing process using Coloured Petri-Net simulation and UK pharmacy in-field data

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  • Matthew Naybour
  • Rasa Remenyte-Prescott
  • Matthew Boyd

Abstract

There are 11,619 community pharmacies in England which dispense over 1 billion prescriptions each year, providing essential primary care to NHS (National Health Service) patients. These pharmacies are facing pressure from a number of sources including funding cuts and high demands on services, while trying to deliver the highest standards of care. This paper presents an optimisation of a Coloured Petri Net (CPN) community pharmacy simulation model using an Ant Colony Optimisation (ACO) method. The CPN method was proposed by Naybour et al . Quantitative data from UK community pharmacies was collected by the authors and incorporated into the CPN simulation model. The optimisation is made up of a choice of how many staff to employ, which prescription checking strategy to use, and which staff work pattern to implement. This method aims to provide decision makers with a set of optimal pharmacy configurations at different cost levels. This can help to support pharmacy safety, efficiency, and improve decision making processes. It has been demonstrated how reliability modelling techniques traditionally used in safety-critical industries, can be used to carry out safety and efficiency analyses of healthcare systems, such as dispensing processes in community pharmacies, illustrated in this contribution.

Suggested Citation

  • Matthew Naybour & Rasa Remenyte-Prescott & Matthew Boyd, 2024. "Ant colony optimisation of a community pharmacy dispensing process using Coloured Petri-Net simulation and UK pharmacy in-field data," Journal of Risk and Reliability, , vol. 238(1), pages 29-43, February.
    • Handle: RePEc:sae:risrel:v:238:y:2024:i:1:p:29-43
    DOI: 10.1177/1748006X221135459
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    References listed on IDEAS

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    1. Sally Brailsford & Walter Gutjahr & Marion Rauner & Wolfgang Zeppelzauer, 2007. "Combined Discrete-event Simulation and Ant Colony Optimisation Approach for Selecting Optimal Screening Policies for Diabetic Retinopathy," Computational Management Science, Springer, vol. 4(1), pages 59-83, January.
    2. D Martens & T Van Gestel & M De Backer & R Haesen & J Vanthienen & B Baesens, 2010. "Credit rating prediction using Ant Colony Optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 561-573, April.
    3. Tatjana Stojković & Olaf Rose & Ronja Woltersdorf & Valentina Marinković & Tanja Manser & Ulrich Jaehde, 2018. "Prospective systemic risk analysis of the dispensing process in German community pharmacies," International Journal of Health Planning and Management, Wiley Blackwell, vol. 33(1), pages 320-332, January.
    4. K A Dowsland & J M Thompson, 2005. "Ant colony optimization for the examination scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 426-438, April.
    5. X-Y Li & Y P Aneja & F Baki, 2010. "An ant colony optimization metaheuristic for single-path multicommodity network flow problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(9), pages 1340-1355, September.
    6. K Katsaliaki & N Mustafee, 2011. "Applications of simulation within the healthcare context," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(8), pages 1431-1451, August.
    7. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
    8. Ana R Vila-Parrish & Julie S Ivy & Russell E King & Steven R Abel, 2012. "Patient-based pharmaceutical inventory management: a two-stage inventory and production model for perishable products with Markovian demand," Health Systems, Taylor & Francis Journals, vol. 1(1), pages 69-83, June.
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