M. Naybour
Ant colony optimisation for community pharmacy dispensing process based on in-field observations
Naybour, M.; Remenyte-Prescott, R.; Boyd, M.
Authors
Dr RASA REMENYTE-PRESCOTT R.REMENYTE-PRESCOTT@NOTTINGHAM.AC.UK
ASSOCIATE PROFESSOR
Professor MATTHEW BOYD matthew.boyd@nottingham.ac.uk
PROFESSOR OF MEDICINES SAFETY
Contributors
Michael Beer
Editor
Enrico Zio
Editor
Abstract
The community pharmacy dispensing process is an integral part of delivering effective primary care to patients around the world. However, dispensing error rates and related patient safety issues are always a concern in the sector, where studies have found dispensing error rates to be between 0.014% and 3.3% of items. In an attempt to identify the optimum performance within community pharmacies, a simulation model of small-medium sized UK pharmacies was built using a Colored Petri Net (CPN) method (Naybour et al., 2019). The model mimics how staff complete prescriptions and how errors occur during the dispensing process. Decisions by pharmacy managers need to be made about the number of staff to employ and their work pattern, as well as the prescription checking strategy, so that the pharmacy performance can be optimized without reducing process safety.
This paper focusses on the optimization aspect of pharmacy processes. An Ant Colony Optimization (ACO) algorithm is proposed and the results of the CPN simulations are integrated within this optimization framework. A multi-objective optimization is developed using a three-parameter utility function, including the number of prescriptions completed, the number of errors, and the average waiting time for customers. These parameters are outputs of the CPN simulations. The optimization routine can find a number of viable solutions for a range of cost and process reliability values.
In addition, this paper uses in-field data collected by the authors from observations of two pharmacies in the UK. A large number of times to complete each stage of the dispensing process have been collected and analyzed. The estimates of the distribution parameters were used in the CPN simulation and the ACO framework.
Citation
Naybour, M., Remenyte-Prescott, R., & Boyd, M. (2019, September). Ant colony optimisation for community pharmacy dispensing process based on in-field observations. Presented at 29th European Safety and Reliability Conference (ESREL 2019), Hannover, Germany
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 29th European Safety and Reliability Conference (ESREL 2019) |
Start Date | Sep 22, 2019 |
End Date | Sep 26, 2019 |
Acceptance Date | Apr 14, 2019 |
Publication Date | Sep 26, 2019 |
Deposit Date | Jun 21, 2019 |
Publicly Available Date | Jun 21, 2019 |
Pages | 4249-4257 |
Book Title | Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019) |
DOI | https://doi.org/10.3850/978-981-11-2724-3_+0465-cd |
Keywords | Min Max ant system, Ant colony optimisation, Coloured petri net, Community pharmacy, Patient safety, Dispensing efficiency |
Public URL | https://nottingham-repository.worktribe.com/output/2215875 |
Publisher URL | http://itekcmsonline.com/rps2prod/esrel2019/e-proceedings/html/0465.xml |
Related Public URLs | https://esrel2019.org/#/ |
Contract Date | Jun 21, 2019 |
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