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Published Apr 1, 2020
Mohammad Dehghanimohammadabadi Nihan Kabadayi

Abstract

Quality of care is crucial for patients' satisfaction and safety in healthcare centers. The majority of hospitals attempt to implement facility-wide improvements to ensure high-quality care delivery. This study aims to propose a combined Simulation-Optimization and MCDM approach to accurately assess the impact of quality improvement initiatives on different facets of healthcare systems. In this framework, first, the importance (weights) of the different healthcare criteria is determined by health providers’ using an AHP approach. Then, the weights provided by AHP are applied in a simulation-optimization environment to determine the most efficient action with the most desirable quality of care. Simulation provides a platform to examine the effectiveness of different improvement efforts and calculate their impact on the system performance measures.

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Keywords

Analytic Hierarchy Process, Multi-criteria Decision Making, Simulation-Optimization, Healthcare Operations

References
Al-Dhaheri, N., Jebali, A. and Diabat, A. (2016). A simulation-based Genetic Algorithm approach for the quay crane scheduling under uncertainty. Simulation Modelling Practice and Theory, 66, 122–138. doi: https://doi.org/10.1016/j.simpat.2016.01.009

Alshaebi, A., Dauod, H., Weiss, J. and Yoon, S.W. (2017). Evaluation of different forklift battery systems using statistical analysis and discrete event simulation. IIE Annual Conference. Proceedings, 1637–1642.

Alt, J.K. and Lieberman, S. (2010). Representing dynamic social networks in discrete event social simulation. Proceedings of the 2010 Winter Simulation Conference, 1478–1489. doi: https://doi.org/10.1109/wsc.2010.5679046

Azadivar, F. and Wang, J. (2000). Facility layout optimization using simulation and genetic algorithms. International Journal of Production Research, 38(7), 4369–4383.doi: https://doi.org/10.1080/00207540050205154

Baccouche, A. ,Goren, S., Huyet, A.L. and Pierreval, H. (2011). An approach based on simulation optimization and AHP to support collaborative design: With an application to supply chains. 2011 IEEE Workshop On Computational Intelligence In Production And Logistics Systems (CIPLS), 1–7. doi: https://doi.org/10.1109/cipls.2011.5953360

Balezentis, T. and Streimikiene, S. (2017). Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation. Applied Energy, 185, 862–871. doi: https://doi.org/10.1109/cipls.2011.5953360

Bamakan, S.M.H. and Dehghanimohammadabadi, M. (2015). A weighted Monte Carlo simulation approach to risk assessment of information security management system. International Journal of Enterprise Information Systems (IJEIS), 11(4), 63–78. doi: https://doi.org/10.4018/ijeis.2015100103

Belevich, I., Joensuu, M., Kumar, D.,Vihinen, H. and Jokitalo, E. (2016). Microscopy image browser: a platform for segmentation and analysis of multidimensional datasets. PLoS Biology, 14(1), e1002340. doi: https://doi.org/10.1371/journal.pbio.1002340

Carson Y. and Maria, A. (1997). Simulation optimization: methods and applications. Proceedings of the 29th conference on Winter simulation, 118–126.

Chambers, L.D. (2019). The practical handbook of genetic algorithms: New Frontiers, Volume II.

Chen, C.F. Applying the analytical hierarchy process (AHP) approach to convention site selection. Journal of Travel Research, 45(2), 167–174. doi: https://doi.org/10.1177/0047287506291593

Dehghanimohammadabadi, M. (2016). Iterative optimization-based simulation (IOS) with predictable and unpredictable trigger events in simulated time, Western New England University. doi: https://doi.org/10.1109/wsc.2015.7408423

Dehghanimohammadabadi, M., Rezaeiahari, M. and Keyser, T.K. (2017). Simheuristic of patient scheduling using a table-experiment approach - Simio and MATLAB integration application. Proceedings of the 2017 Winter Simulation Conference.

Dehghanimohammadabadi, M. and Keyser, T.K. (2017). Intelligent simulation: Integration of Simio and MATLAB to deploy decision support systems to simulation environment. Simulation Modelling Practice and Theory, 71, 45–60. doi: https://doi.org/10.1109/wsc.2017.8248015

Dehghanimohammadabadi, M. and Keyser, T.K. (2015). Smart Simulation: Integration of Simio and Matlab | Simio. Proceedings of the 2015 Winter Simulation Conference. doi: https://doi.org/10.1109/wsc.2017.8248015

Eskandari, H., Riyahifard, M., Khosravi, S. and Geiger, C.D. (2011). Improving the emergency department performance using simulation and MCDM methods. Proceedings of the 2011 winter simulation conference (WSC), 1211–1222. doi: https://doi.org/10.1109/wsc.2011.6147843

Gocken, M., Dosdogru, A.T., Boru, A. and Geyik, F. (2017). Characterizing continuous (s, S) policy with supplier selection using Simulation Optimization. SimulTIon, 0037549716687044. doi: https://doi.org/10.1177/0037549716687044

Gocken, M., Dosdogru, A.T., Boru, A. and Geyik, F. (2015). (R,s,S) Inventory control policy and supplier selection in a two-echelon supply chain: An optimization via simulation approach. Proceedings of the Winter SImulation Conference, California, USA. doi: https://doi.org/10.1109/wsc.2015.7408320

Gul, M., Celik, E., Gumus, A.T. and Guneri, A.F. (2016). Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European Journal of Industrial Engineering, 10(2), 196–223. doi: https://doi.org/10.1504/ejie.2016.075846

Hsu, T.H. and Pan, F.F. (2009). Application of Monte Carlo AHP in ranking dental quality attributes. Expert Systems with Applications, 36(2), 2310–2316. doi: https://doi.org/10.1016/j.eswa.2007.12.023

Juan, A.A., Faulin, J., Grasman, S.E., Rabe, M. and Figueira, G. (2015). A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems. Operations Research Perspectives, 2, 62–72. doi: https://doi.org/10.1016/j.orp.2015.03.001

Karczmarek, P., Pedrycz, W., Kiersztyn, A., and Rutka, P. (2017). A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft Computing, 21(24), 7503–7517. doi: https://doi.org/10.1007/s00500-016-2305-9

Kokol, P., Pohorec, S., Stiglic, G. and Podgorelec, V. (2012). Evolutionary design of decision trees for medical application. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(3), 237–254. doi: https://doi.org/10.1002/widm.1056

Man, K.F. Tang, K.S. and Kwong, S. (1999). Genetic algorithms: Concept and design. London: Springer Verlag.

Marseguerra, M., Zio, E., and Podofillini, L. (2002). Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliability Engineering & System Safety, 77(2), 151–165. doi: https://doi.org/10.1016/s0951-8320(02)00043-1

Meng, C. (2015). Simulation-based decision support for agricultural supply chain performance improvement. The University of Arizona.

Moon, Y.B. and Phatak, D. (2005). Enhancing ERP system’s functionality with discrete event simulation. Industrial Management & Data Systems, 105(9), 1206–1224. doi: https://doi.org/10.1108/02635570510633266

Ozgur, C., Colliau, T., Rogers, G., Hughes, Z. and Myer-Tyson, B. (2017). MatLab vs. Python vs. R. Journal of Data Science, 15(3), 355–372.

Pun, K.P., Tsang, Y.P., Choy, K.L., Tang, V. and Lam, H.Y. (2017). A fuzzy-AHP-based decision support system for maintenance strategy selection in facility management. 2017 Portland International Conference on Management of Engineering and Technology (PICMET), 1–7. doi: https://doi.org/10.23919/picmet.2017.8125300

Saaty, T.L. (1980) The Analytic Hierarchy Process. New York: McGraw-Hill.

Talbi, E.G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons.

Vieira, A.A.C., Dias, L.S., Pereira, G., Oliveira, J.A., Carvalho, M.S. and Martins (2016). Automatic simulation models generation of warehouses with milk runs and pickers. 28th European Modeling and Simulation Symposium, EMSS 2016, 231–241.

Yin, P.Y., Wu, T.H. and Hsu, P.Y. (2017). Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization.” Renewable Energy, 107, 508–521. doi: https://doi.org/10.1016/j.renene.2017.02.036
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