Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach
Abstract
:1. Introduction
1.1. Background
- Predicting or detecting opportunities for optimisation of service levels, technical resourcing, spare parts management and servicing costs, as well as service times;
- Forecasting optimisation based on changes in healthcare service demand or system shocks, such as due to the COVID-19 pandemic.
1.2. State of Maintenance Management of Healthcare Facilities in Low-Income Economies
1.3. Research Question
- what is the level of spares parts that must be maintained in the store;
- what is the number of technicians that must be hired; and
- what is the service frequency rate
2. Literature Review
2.1. Challenges in Maintenance of Facilities
2.2. Existing Tools and Techniques Developed within Maintenance Management Research
2.3. Operational Contexts
2.4. Previous Approaches
2.5. DES-Based Tools Applied within Healthcare
3. Methodology
3.1. Approach and Research Tools
3.2. Modelling Framework
3.2.1. Define Scope
3.2.2. Build Process Model and Translate to Digital Model
3.2.3. Define System Variables
- maintenance strategy per equipment group (that is, maintenance type and frequency);
- spares inventory stocking and replenishment policy; and
- human resources.
3.2.4. Define Bounds and Constraints
3.2.5. Formulate Objective Function
- is the mean time to repair asset of type k, including queuing time;
- is the mean time to execute a preventive schedule on asset of type k, including any waiting; and
- is the mean time to execute a preventive maintenance schedule on asset of type k, including queuing time.
3.2.6. Set Up the Discrete Event Simulation
3.2.7. Set Parameters for Simulated Annealing
3.2.8. Execution
4. Results
4.1. Initial Conditions
4.2. Output
4.3. Search History
4.4. Analysis
5. Discussion
- determine what the input parameters must be in order to ensure optimal performance, not just of the maintenance function but of the health facility in general, including operating margin and customer service level;
- determine the optimum frequency for preventive maintenance activities on a given asset;
- show visual outputs that can be studied for insights and that may reveal hidden relationships among variables;
- give information that can assist managers to formulate a store’s spares policy that is in sync with operations and maintenance;
- provide what-if analysis feedback that managers can use to improve the quality of strategic and tactical decision making; and
- work in any facility with discrete assets without any further customisation.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ABS | Agent-based simulation |
DES | Discrete event simulation |
KPI | Key performance indicator |
MTBF | Mean time between failures |
MTTR | Mean time to repair |
SD | System dynamics |
SSR | Solution space reduction |
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Variable Groups | Independent | Dependent | Control |
---|---|---|---|
People related |
|
|
|
Spares related |
|
|
|
Equipment related |
|
|
|
Operations related | - |
|
|
Simulation related |
|
|
|
Simulation Parameter | Value | Explanation |
---|---|---|
| Dynamic 20 0.75 |
|
| 50 |
|
Asset Group | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|
Units per group | 4 | 4 | 3 | 6 | 5 | 7 | 10 | 2 | 8 | 0 |
MTBF [weeks] | 30 | 10 | 32 | 9 | 23 | 22 | 29 | 10 | 17 | 8 |
Preventive maintenance interval [weeks] | 52 | 52 | 52 | never | never | never | never | never | never | never |
Predictive maintenance interval [weeks] | 13 | 13 | 13 | 13 | 26 | 26 | 26 | 26 | 26 | 13 |
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Mwanza, J.; Telukdarie, A.; Igusa, T. Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach. Modelling 2023, 4, 224-250. https://doi.org/10.3390/modelling4020013
Mwanza J, Telukdarie A, Igusa T. Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach. Modelling. 2023; 4(2):224-250. https://doi.org/10.3390/modelling4020013
Chicago/Turabian StyleMwanza, Joseph, Arnesh Telukdarie, and Tak Igusa. 2023. "Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach" Modelling 4, no. 2: 224-250. https://doi.org/10.3390/modelling4020013
APA StyleMwanza, J., Telukdarie, A., & Igusa, T. (2023). Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach. Modelling, 4(2), 224-250. https://doi.org/10.3390/modelling4020013