A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards
Operating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9989389/ |
| _version_ | 1818293885557800960 |
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| author | Aisha Tayyab Ullah Saif |
| author_facet | Aisha Tayyab Ullah Saif |
| author_sort | Aisha Tayyab |
| collection | DOAJ |
| description | Operating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to cancellations. Thus, this study considers the operating room’s capacity and downstream units while making surgery-related decisions. A mixed integer linear programming model for integrated planning consisting of two stages is proposed. The first stage model maximizes the scheduled surgical time of all operating rooms. In contrast, the second stage model aims to minimize the makespan of patients in operating rooms by incorporating sequence-dependent setup time and the capacity constraints of all resources under consideration at both stages. A two-stage genetic artificial bee colony algorithm (TGABC) hybrid of genetic algorithm and artificial bee colony algorithm is proposed to solve the model. Taguchi design of experiments is employed to fine-tune the parameters of the proposed TGABC algorithm. Experiments are designed to evaluate the performance of the proposed TGABC algorithm with generated instances mimicking real data for different-sized problems, and results are presented. The proposed method is compared with the exact method and three standard metaheuristics. It provides near-optimal results in comparatively shorter CPU time. Moreover, it outperforms the genetic algorithm, artificial bee colony algorithm, and simulated annealing in terms of solution quality compared on the considered test instances. |
| first_indexed | 2024-12-13T03:22:58Z |
| format | Article |
| id | doaj.art-148b8915e3b84b69ba8e700cd72c3466 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T03:22:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-148b8915e3b84b69ba8e700cd72c34662022-12-22T00:01:19ZengIEEEIEEE Access2169-35362022-01-011013110913112710.1109/ACCESS.2022.32297099989389A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream WardsAisha Tayyab0https://orcid.org/0000-0002-0382-9223Ullah Saif1https://orcid.org/0000-0003-4772-7760Department of Industrial Engineering, University of Engineering and Technology, Taxila, PakistanDepartment of Industrial Engineering, University of Engineering and Technology, Taxila, PakistanOperating room planning and scheduling significantly affect all hospital areas, including the intensive care unit and downstream wards. Planning and scheduling operating rooms integrated with intensive care units and downstream wards can lead to more stable plans and schedules that are less prone to cancellations. Thus, this study considers the operating room’s capacity and downstream units while making surgery-related decisions. A mixed integer linear programming model for integrated planning consisting of two stages is proposed. The first stage model maximizes the scheduled surgical time of all operating rooms. In contrast, the second stage model aims to minimize the makespan of patients in operating rooms by incorporating sequence-dependent setup time and the capacity constraints of all resources under consideration at both stages. A two-stage genetic artificial bee colony algorithm (TGABC) hybrid of genetic algorithm and artificial bee colony algorithm is proposed to solve the model. Taguchi design of experiments is employed to fine-tune the parameters of the proposed TGABC algorithm. Experiments are designed to evaluate the performance of the proposed TGABC algorithm with generated instances mimicking real data for different-sized problems, and results are presented. The proposed method is compared with the exact method and three standard metaheuristics. It provides near-optimal results in comparatively shorter CPU time. Moreover, it outperforms the genetic algorithm, artificial bee colony algorithm, and simulated annealing in terms of solution quality compared on the considered test instances.https://ieeexplore.ieee.org/document/9989389/Artificial bee colony algorithmgenetic algorithmmetaheuristicoperating room in health servicesresource allocation |
| spellingShingle | Aisha Tayyab Ullah Saif A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards IEEE Access Artificial bee colony algorithm genetic algorithm metaheuristic operating room in health services resource allocation |
| title | A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards |
| title_full | A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards |
| title_fullStr | A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards |
| title_full_unstemmed | A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards |
| title_short | A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards |
| title_sort | two stage genetic artificial bee colony algorithm for solving integrated operating room planning and scheduling problem with capacity constraints of downstream wards |
| topic | Artificial bee colony algorithm genetic algorithm metaheuristic operating room in health services resource allocation |
| url | https://ieeexplore.ieee.org/document/9989389/ |
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