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Intra-hospital patient transportation system planning using bi-level decision model

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Języki publikacji
EN
Abstrakty
EN
Background: The intra-hospital patient transportation is an important aspect of patient care. It is about the transfer of patients between different healthcare units in the hospital. Many tasks are required for transferring the patients from one to another unit depending on available resources and the needs of the patients, such as types of supporting equipment, transfer routes, and supporters. Limited and unprepared resources for transferring the patients, such as lack of supporting equipment and available supporters, may impact the patient treatment and service quality. Therefore, these resources should be managed effectively in order to minimize these impacts. The case study hospital located in Chiang Mai province, northern Thailand is currently encountering the problem in managing and planning the intra-hospital transportation process. Therefore, this research aimed to propose a mathematical model for planning the intra-hospital transportation system in this case study hospital. Methods: Our research proposed a bi-level mathematical model to tackle the intra-hospital transportation planning problems. The system is represented by a deterministic model using integer linear programming. The first level of the mathematical model is for identifying the locations and setting them as transportation depots. The second level of the model is to optimize the number of resources used for intra-hospital patient transportation. The model was then validated by using two sets of instances via LINGO solver. Results: This research proposed a bi-level mathematical model that could help to manage the intra-hospital transportation challenges in the case study hospital. Furthermore, the outcomes from the test with two instances were depots positioned at a set of feasible locations. The model was used to designate resources to each depot for the instance, such as wheelchairs, stretchers, oxygen tanks, and employees. In each case, the outcomes are dependent on varying service timings and demands. Conclusion: This research used the deterministic mathematical model for planning the intra-hospital transportation system consisting of the location assignment and resource allocation. The model, in addition, can solve with the exact method. Consequently, we can ensure that the presented model can apply to real situations in further study.
Czasopismo
Rocznik
Strony
237--246
Opis fizyczny
Bibliogr. 18 poz., tab.
Twórcy
  • Graduate Program in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Thailand
  • Centre of Healthcare Engineering System, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Thailand
  • Centre of Healthcare Engineering System, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Thailand
Bibliografia
  • 1. Boonmee C., Arimura M., Kasemset C., 2021b. Post-disaster waste management with carbon tax policy consideration. Energy Reports, 7: 89-97. https://doi.org/10.1016/j.egyr.2021.05.077
  • 2. Boonmee C., Kasemset C., 2020. The Multi-Objective Fuzzy Mathematical Programming Model for Humanitarian Relief Logistics. Industrial Engineering & Management Systems, 19(1): 197-210. https://doi.org/10.7232/iems.2020.19.1.197
  • 3. Boonmee C., Kasemset C., 2019. The Improvement of Healthcare Management in Thailand via IE Tools: A Survey. Proceedings of International Conference on Industrial Engineering and Operations Management (Bangkok, Thailand, March 5-7, 2019): 264-274. http://www.ieomsociety.org/ieom2019/papers/80.pdf
  • 4. Boonmee C., Pisutha-Arnond N., Chattinnawat W., Muangwong P., Nobnop W., Chitapanarux I., 2021a. Decision Support System for Radiotherapy Patient Scheduling: Thai Cancer Center Case Study. Proceedings of 2021 5th International Conference on Medical and Health Informatics: 168–175. https://doi.org/10.1145/3472813.3473185
  • 5. Bouabdallah M. N., Fondrevelle J., Rached M., Bahroun Z., 2013. Organization and management of hospital patient transportation system. Proceeding of 2013 International Conference on Control, Decision and Information Technologies (CoDIT): 125-130. https://doi.org/10.1109/CoDIT.2013.6689531
  • 6. Daskin M.S., 2008. What you should know about location modeling. Naval Research Logistics (NRL), 55(4): 283-294. https://doi.org/10.1002/nav.20284
  • 7. Etezadi T., Beasley J.E., 1983. Vehicle Fleet Composition. The Journal of the Operational Research Society, 34(1): 87-91. https://doi.org/10.2307/2581607
  • 8. Fermín Cueto P., Gjeroska I., Solà Vilalta A., Anjos M.F., 2021. A solution approach for multi-trip vehicle routing problems with time windows, fleet sizing, and depot location. Networks, 78(4): 503-522. https://doi.org/10.1002/net.22028
  • 9. Gass S.I., 1983. Decision-Aiding Models: Validation, Assessment, and Related Issues for Policy Analysis. Operations Research, 31(4): 603-631. https://doi.org/10.1287/opre.31.4.603
  • 10. Gopal K., 2016. Modeling and Optimization of Hospital Transportation System. Doctoral dissertation, University of Akron. http://rave.ohiolink.edu/etdc/view?acc_num=akron1481314351566885
  • 11. Kasemset C., Boonmee C., Arakawa M., 2020. Traffic Information Sign Location Problem: Optimization and Simulation. Industrial Engineering & Management Systems, 19(1): 228-241. https://doi.org/10.7232/iems.2020.19.1.228
  • 12. Kuchera D., Rohleder T.R., 2011. Optimizing the patient transport function at Mayo Clinic. Quality Management in Health Care, 20(4): 334-342. https://doi.org/10.1097/QMH.0b013e318231a84f
  • 13. Lu J., Han J., Hu Y., Zhang G., 2016. Multilevel decision-making: A survey. Information Sciences, 346-347: 463-487. https://doi.org/10.1016/j.ins.2016.01.084
  • 14. Naesens K., Gelders L., 2009. Reorganising a service department: Central Patient Transportation. Production Planning & Control, 20(6): 478-483. https://doi.org/10.1080/09537280902938621
  • 15. Phongthiya T., Kasemset C., Poomsuk S., Lertcharoenpaisan W., 2021. Application of Simulation Technique in Improvement of Intra-hospital Patient Transfer: A Provincial Hospital Center in Northern Thailand. Proceedings of 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): 314-318. https://doi.org/10.1109/IEEM50564.2021.9672893
  • 16. Séguin S., Villeneuve Y., Blouin-Delisle C.H., 2019. Improving patient transportation in hospitals using a mixed-integer programming model. Operations Research for Health Care, 23: 100202. https://doi.org/10.1016/j.orhc.2019.100202
  • 17. Turan B., Schmid V., Doerner K.F., 2011. Models for intra-hospital patient routing. Proceedings of 3rd IEEE International Symposium on Logistics and Industrial Informatics, 51-60. https://doi.org/10.1109/LINDI.2011.6031162
  • 18. Wayan Suletra I., Priyandari Y., Jauhari W.A., 2018. Capacitated set-covering model considering the distance objective and dependency of alternative facilities. Proceedings of IOP Conference Series: Materials Science and Engineering, 319(1): 012072. https://doi.org/10.1088/1757-899x/319/1/012072
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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