Volume 6, Issue 3 (5-2021)                   hrjbaq 2021, 6(3): 188-196 | Back to browse issues page

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Amiri Domari M, Firozi Jahantigh F. A Model for Control the Inventory of the Hospital Operating Room with Known Demand by Variable Neighborhood Search Method. hrjbaq. 2021; 6 (3) :188-196
URL: http://hrjbaq.ir/article-1-407-en.html
Department of Industrial Engineering, Shahid Nikbakht Engineering Faculty, Sistan and Baluchestan University, Zahedan, Iran , (Firouzi@eng.usb.ac.ir)
Abstract:   (1178 Views)

Introduction: Hospitals and medical centers are important in terms of quantity and quality of the services they provide. Therefore, inventory control is a process that ensures that the required inventory are available to the operational departments by considering the factors of time, place, quantity, quality and cost. The aim of this study was to reduce the cost of hospital with a control approach to planning available hospital inventories.
Materials and Methods: In this study, an improved optimized model with objective function was used, which includes inventory cost, costs and orders. Therefore, the inventory of ten most important items of operating room requirements was considered. To solve the model, the variable neighborhood search method was used with the approach of reducing hospital costs in MATLAB software.
Results: 10 types of most expensive goods used in the operating room were examined. Finally, according to the average demand of the studied goods in the year, a certain amount of inventory and the number of orders for each of them was obtained.
Conclusion: The variable neighborhood search algorithm answered in less than 5 minutes and by examining the quality of the answer, it shows that this method has an acceptable performance in solving this problem. Finally, the hospital did not face a shortage of goods during this period, despite heavy sanctions. Therefore, this method can be used to solve the problem of inventory control in the operating room of the hospital.

Author Contribution: All authors contributed equally in this work
Conflict of Interest/Funding/Supports: The authors declare that have no conflict of interest/funding/supports in this study.
Ethical Considerations: All ethical concerns respected in this study.
Applicable Remarks: To introduce new effective method for control of inventory in the operating room.   

Full-Text [PDF 1254 kb]   (294 Downloads)    
Type of Study: Research | Subject: General
Received: 2020/01/28 | Revised: 2021/09/4 | Accepted: 2021/05/18 | ePublished ahead of print: 2021/07/4 | Published: 2021/09/4

1. Qu X, Rardin RL, Williams JAS. A mean–variance model to optimize the fixed versus open appointment percentages in open access scheduling systems. Decision Support Systems. 2012;53(3):554-64. DOI: 10.1016/ j.dss.2012.04.003
2. Shanthikumar J, George Y, David D, Zijm W. Modeling and Optimization of Manufacturing Systems and Supply Chains. A State of the Art Handbook International Series in Operations Research and Management Science. 2003.
3. Keehan SP, Stone DA, Poisal JA, Cuckler GA, Sisko AM, Smith SD, et al. National Health Expenditure Projections, 2016-25: Price Increases, Aging Push Sector To 20 Percent Of Economy. Health Aff (Millwood). 2017;36(3):553-63. DOI: 10.1377/hlthaff.2016.1627 PMID: 28202501
4. Sarvandi S, Shahroodi K. Assessing the Patients' Hospitalization and Discharge Processes Based on Kaizen approach and Multiple-Criteria Decision Making (MCDM) in a Hospital. Journal of Hospital 2016.15(3):83-93.
5. Absi N, Kedad-Sidhoum S. The multi-item capacitated lot-sizing problem with safety stocks and demand shortage costs. Computers & Operations Research. 2009;36(11):2926-36. DOI: 10.1016/j.cor.2009.01.007
6. Muriana C. An EOQ model for perishable products with fixed shelf life under stochastic demand conditions. European Journal of Operational Research. 2016;255(2):388-96. DOI: 10.1016/j.ejor.2016.04.036
7. Dyas AR, Lovell KM, Balentine CJ, Wang TN, Porterfield JR, Jr., Chen H, et al. Reducing cost and improving operating room efficiency: examination of surgical instrument processing. J Surg Res. 2018;229:15-9. DOI: 10.1016/j.jss.2018.03.038 PMID: 29936982
8. Dexter F, Dexter EU, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg. 2010;110(4):1155-63. DOI: 10.1213/ANE.0b013e3181d3e79d PMID: 20357155
9. Abedini A, Li W, Ye H. An Optimization Model for Operating Room Scheduling to Reduce Blocking Across the Perioperative Process. Procedia Manufacturing. 2017;10:60-70. DOI: 10.1016/j.promfg.2017.07.022
10. Zullo MD, McCarroll ML, Mendise TM, Ferris EF, Roulette GD, Zolton J, et al. Safety culture in the gynecology robotics operating room. J Minim Invasive Gynecol. 2014;21(5):893-900. DOI: 10.1016/j.jmig. 2014.03.027 PMID: 24769449
11. Razmi J, Yousefi MS, Barati M. A stochastic model for operating room unique equipment planning under uncertainty. IFAC-PapersOnLine. 2015;48(3):1796-801. DOI: 10.1016/j.ifacol.2015.06.347
12. Vali Siar MM, Gholami S, Ramezanian R. Multi-period and multi-resource operating room scheduling and rescheduling using a rolling horizon approach: A case study. Journal of Industrial and Systems Engineering 2017;10:97-115.
13. Riet C, Demeulemeester E. Trade-offs in operating room planning for electives and emergencies. Operations Research for Health Care 2015.
14. Kroer LR, Foverskov K, Vilhelmsen C, Hansen AS, Larsen J. Planning and scheduling operating rooms for elective and emergency surgeries with uncertain duration. Operations research for health care. 2018;19:107-19.
15. Perdomo V, Augusto V, Xie X. Operating Theatre Scheduling Using Lagrangian Relaxation. In Proceedings of the International Conference on Service Systems and Service Management. 2006:1234-9. DOI: 10.1109/ icsssm.2006.320685
16. Neyshabouri S, Berg BP. Two-stage robust optimization approach to elective surgery and downstream capacity planning. European Journal of Operational Research. 2017;260(1):21-40. DOI: 10.1016/j.ejor.2016.11.043
17. Guido R, Conforti D. A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Computers & Operations Research. 2017;87:270-82. DOI: 10.1016/ j.cor.2016.11.009
18. Zhou QS, Olsen TL. Inventory rotation of medical supplies for emergency response. European Journal of Operational Research. 2017;257(3):810-21. DOI: 10.1016/j.ejor.2016.08.010
19. Darwish A, Mehta P, Mahmoud A, El-Sergany A, Culberson D. Improving operating room start times in a community teaching hospital. Journal of Hospital Administration. 2016;5(3):33. DOI: 10.5430/jha.v5n3p33
20. Vali-Siar MM, Gholami S, Ramezanian R. Multi-period and multi-resource operating room scheduling under uncertainty: A case study. Computers & Industrial Engineering. 2018;126:549-68. DOI: 10.1016/j.cie.2018. 10.014
21. Tagge EP, Thirumoorthi AS, Lenart J, Garberoglio C, Mitchell KW. Improving operating room efficiency in academic children's hospital using Lean Six Sigma methodology. J Pediatr Surg. 2017;52(6):1040-4. DOI: 10.1016/j.jpedsurg.2017.03.035 PMID: 28389078
22. Maestre JM, Fernández MI, Jurado I. An application of economic model predictive control to inventory management in hospitals. Control Engineering Practice. 2018;71:120-8. DOI: 10.1016/j.conengprac.2017.10.012
23. Brimberg J, Mladenovi'c N. Variable Neighbourhood Algorithm for Solving the Continuous Location-Allocation Problem. Studies in Locational Analysis 1996;10:1-10.
24. Cardoen B, Demeulemeester E, Beliën J. Operating room planning and scheduling: A literature review. European Journal of Operational Research. 2010;201(3):921-32. DOI: 10.1016/j.ejor.2009.04.011
25. Fattahi P, Hajipour V, Nobari A. A bi-objective continuous review inventory control model: Pareto-based meta-heuristic algorithms. Applied Soft Computing. 2015;32:211-23. DOI: 10.1016/j.asoc.2015.02.044

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