Ant colony optimisation for community pharmacy dispensing process based on in-field observations
Naybour, M.; Remenyte-Prescott, R.; Boyd, M.
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.
|Start Date||Sep 22, 2019|
|Publication Date||Sep 26, 2019|
|Book Title||Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019)|
|Institution Citation||Naybour, M., Remenyte-Prescott, R., & Boyd, M. (2019). Ant colony optimisation for community pharmacy dispensing process based on in-field observations. In M. Beer, & E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference (ESREL 2019)|
|Keywords||Min Max ant system, Ant colony optimisation, Coloured petri net, Community pharmacy, Patient safety, Dispensing efficiency|
|Related Public URLs||https://esrel2019.org/#/|
Ant Colony Optimisation