Biomedical Machine Maintenance Scheduling
DOI:
https://doi.org/10.37266/ISER.2021v9i2.pp110-116Keywords:
Tissue Bank, Biobank, Machinery, Optimization, Preventive Scheduling, ReliabilityAbstract
Tissue banks procure approximately 45,000 tissue donations per year, providing nearly 9,000,000 individuals (about half the population of New York) with life-enhancing and life-saving medical procedures. Proper biobank machine maintenance is imperative to this process. Mandatory forms of maintenance are critical to avoid unexpected malfunctions, which can halt operations and render samples unusable. Each machine has a unique reliability rate within the system; although some can quickly be repaired or replaced, many processes rely on limited machinery where even planned downtime can significantly influence the tissue processing. AlloSource, one of the largest tissue manufacturers in the United States, too often schedules these preventive events unnecessarily or inconveniently, resulting in machines breaking down at inopportune times. In response to these inefficiencies we ask, “What is the best consolidated and standardized equipment maintenance schedule that maximizes monthly maintenance events to ensure increased equipment availability while meeting the demand of the biomedical manufacturing network?” We use an optimization model to consider equipment reliability, downtime, availability, and demand to develop a preventive maintenance schedule. Our model focuses on scheduling the maximum number of events the maintenance crew can conduct each month to ensure vital equipment to the allograft process is available, which provides more opportunities for tissue therapies. In doing so, the maintenance crew is also able to complete more events, driving up annual throughput while driving down equipment downtime.References
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Armada Medical Marketing. (2020, August 19). About AlloSource. Retrieved November 23, 2020, from https://allosource.org/about-allosource/
Boland, N., Kalinowski, T., Waterer, H., & Zheng, L. (2012). Mixed integer programming based maintenance scheduling for the Hunter Valley coal chain. Journal of Scheduling, 16(6), 649–659.
https://doi.org/10.1007/s10951-012-0284-y
Cassady, C., & Kutanoglu, E. (2005). Integrating Preventive Maintenance Planning and Production Scheduling for a Single Machine. IEEE Transactions on Reliability, 54(2), 304–309. https://doi.org/10.1109/tr.2005.845967
Christiansen, L. K. (2007, September). Tissue Banking Advancing Cancer Care. Retrieved November 23, 2020, from https://www.dana-farber.org/legacy/uploadedfiles/library/research/tissue-banking/tissue-banking-booklet.pdf
Khalaf, A., Djouani, K., Hamam, Y., & Alayli, Y. (2010). Evidence-based mathematical maintenance model for medical equipment. 2010 International Conference on Electronic Devices, Systems and Applications.
https://doi.org/10.1109/icedsa.2010.5503071
McDonald S. A. (2010). Principles of Research Tissue Banking and Specimen Evaluation from the Pathologist's Perspective. Biopreservation and Biobanking, 8(4), 197–201. https://doi.org/10.1089/bio.2010.0018
Narayan R. P. (2012). Development of tissue bank. Indian journal of plastic surgery: official publication of the Association of Plastic Surgeons of India, 45(2), 396–402. https://doi.org/10.4103/0970-0358.101326
Ruiz-Hernández, D., Pinar-Pérez, J. M., & Delgado-Gómez, D. (2020). Multi-machine preventive maintenance scheduling with imperfect interventions: A restless bandit approach. Computers & Operations Research, 119. https://doi.org/10.1016/j.cor.2020.104927
Schlünz, E., & Vuuren, J. V. (2013). An investigation into the effectiveness of simulated annealing as a solution approach for the generator maintenance scheduling problem. International Journal of Electrical Power & Energy Systems, 53, 166–174. https://doi.org/10.1016/j.ijepes.2013.04.010
Published
2022-01-15
How to Cite
Katz, D., Kim, S., King, A., Palm, E., Dulin, J., Hill, J., & Steeger, G. (2022). Biomedical Machine Maintenance Scheduling. Industrial and Systems Engineering Review, 9(2), 110-116. https://doi.org/10.37266/ISER.2021v9i2.pp110-116
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