Azadeh et al. (2011)Azadeh, A., Moghaddam, M., Asadzadeh, S. M., & Negahban, A. (2011). An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: the case of injection process with ambiguous data. European Journal of Operational Research, 214(3), 768-779. http://doi.org/10.1016/j.ejor.2011.05.015. http://doi.org/10.1016/j.ejor.2011.05.01...
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Manufacture of plastic artifacts - Evaluate the performance of different layouts in a new manufacturing plant arrangement |
Simulator: Visual SLAM Specifics: Simulator used to model the injection manufacturing process |
Model: Non-radial Fuzzy-DEA Orientation: Output Inputs: - Outputs: average machine utilization (desirable output), average wait time (undesirable output) and average system time (undesirable output) |
Weng et al. (2011)Weng, S. J., Tsai, B. S., Wang, L. M., Chang, C. Y., & Gotcher, D. (2011). Using simulation and Data Envelopment Analysis in optimal healthcare efficiency allocations. In Proceedings of the 2011 Winter Simulation Conference (WSC) (pp. 1295-1305), Phoenix, AZ, USA. New York: IEEE. http://doi.org/10.1109/WSC.2011.6147850. http://doi.org/10.1109/WSC.2011.6147850...
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Emergency Sector - Assess potential bottlenecks, maximize production flows, and identify solutions to reduce patient time in the emergency department (ED) while increasing patient satisfaction |
Simulator: Arena Specificities: The model simulated and followed patients in the emergency department throughout their stay in the ED, from presentation to discharge |
Model: DEA-VRS Orientation: Input Inputs: doctors, nurses, beds Outputs: number of patients seen (desired output) |
van den Bergh et al. (2013)van den Bergh, J. V., De Bruecker, P., Beliën, J., De Boeck, L., & Demeulemeester, E. (2013). A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA. Expert Systems with Applications, 40(7), 2659-2668. http://doi.org/10.1016/j.eswa.2012.11.009. http://doi.org/10.1016/j.eswa.2012.11.00...
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Aircraft Line Maintenance - The work focuses on the scheduling of allocated human resources to minimize cost and maximize maintenance service and attendance at an airport |
Simulator: Microsoft Visual Studio (C++) Specifics: Real-time flight arrival data for the winter season was used to identify the appropriate distributions |
Model No.: CRS Orientation: Input Inputs: Labor costs and allocated labor per shift Outputs: success rate (desired), average number of solutions served (desired) and average delay time (unwanted) |
Azadeh et al. (2014)Azadeh, A., Motevali Haghighi, S., & Asadzadeh, S. M. (2014). A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times. Journal of Manufacturing Systems, 33(2), 287-302. http://doi.org/10.1016/j.jmsy.2013.12.008. http://doi.org/10.1016/j.jmsy.2013.12.00...
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Injection Molding Production Line - Reduce bottlenecks in the production line by injection molding |
Simulator: Visual SLAM Specifics: The performance of different priority rules for task dispatch was evaluated, comparing them by queue performance indicators |
Model: Stochastic-DEA Orientation: Output Inputs:- Outputs: queue size (unwanted), average machine utilization (desired) and cycle time (unwanted) |
Dev et al. (2014)Dev, N. K., Shankar, R., & Debnath, R. M. (2014). Supply chain efficiency: a simulation cum DEA approach. International Journal of Advanced Manufacturing Technology, 72(9-12), 1537-1549. http://doi.org/10.1007/s00170-014-5779-6. http://doi.org/10.1007/s00170-014-5779-6...
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Supply Chain - Analyze the efficiency of the total supply chain in the context of average supply rate performance, considering the possibility of time delays due to changes in lead time and inventory review period |
Simulator: Arena Specifics: Uses to obtain supply time in a model that includes multiple levels and links of a hypothetical supply chain |
Model No.: VRS Orientation: Input Inputs: delay due to lead time and inventory review period Outputs: Average Supply Rate |
Marlin & Sohn (2016)Marlin, B., & Sohn, H. (2016). Using DEA in conjunction with designs of experiments: an approach to assess simulated futures in the Afghan educational system. Journal of Simulation, 10(4), 272-282. http://doi.org/10.1057/jos.2015.14. http://doi.org/10.1057/jos.2015.14...
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National education system - Determine the critical relationships, potential futures, and unforeseen consequences of potential policies regarding primary and secondary education in Afghanistan |
Simulator: NOLH Specifics: In the simulation model, entities such as students and teachers are created, which then transit through some part of the system and then are terminated, in which attributes of the entities contain decision rules (containing records about demographic and socioeconomic information), making them analogous to agents |
Model No.: CRS Orientation: Input Inputs: student problem-solving ability, teacher-solving ability, teacher training capacity, community-based education capacity; Student Demand and Required Funds Outputs: dropout, literate, graduates, parity between provinces and quality of education |
Hosseini et al. (2019)Hosseini, Z., Navazi, F., Siadat, A., Memari, P., & Tavakkoli-Moghaddam, R. (2019). A tailored fuzzy simulation integrated with a fuzzy DEA method for a resilient facility layout problem: a case study of a refrigerator injection process. IFAC-PapersOnLine, 52(13), 541-546. http://doi.org/10.1016/j.ifacol.2019.11.214. http://doi.org/10.1016/j.ifacol.2019.11....
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Refrigerator injection process of a home appliance company - Improve the resiliency performance of the production line |
Simulator: Visual SLAM Specifics: 24 different scenarios were generated to be evaluated in the simulation in terms of queue time and utilization rate |
Model No.: Fuzzy DEA Orientation: Output Inputs: - Outputs: Wait time (unwanted), average utilization of flexible devices (desired), average utilization of other devices (desired), and average fault tolerance time (unwanted) |
Keshtkar et al. (2020)Keshtkar, L., Rashwan, W., Abo-Hamad, W., & Arisha, A. (2020). A hybrid system dynamics, discrete event simulation and data envelopment analysis to investigate boarding patients in acute hospitals. Operations Research for Health Care, 26, 100266. http://doi.org/10.1016/j.orhc.2020.100266. http://doi.org/10.1016/j.orhc.2020.10026...
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Hospitalization of patients in emergencies - Investigate the impact of the inpatient problem on patients' ability to access other units in the hospital |
Simulator: AnyLogic Specificities: the integration between DES and DS makes it possible to monitor the patient flow at the micro and macro level |
Model No.: VRS Orientation: Output Inputs: number of porters, number of stretchers for transport and number of consultants Outputs: length of stay (unwanted) and patients seen (desired) |
Navazi et al. (2019) Navazi, F., Tavakkoli-Moghaddam, R., & Memari, P. (2019). Layout optimization of injection process by considering integrated resilience engineering: a fuzzy-DEA approach. International Journal of Modelling and Simulation, 41(1), 52–66. https://doi.org/10.1080/02286203.2019.1670325. https://doi.org/10.1080/02286203.2019.16...
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Refrigerator injection process of a home appliance company - Evaluate the layout of a high-volume production line of single-queue installations and multiple products that requires many downtimes per setup |
Simulator: Visual SLAM Specifics: To improve the facility layout of the injection modelling process, this study investigates the performance of each layout against the three factors of Resilience Engineering, which includes flexibility, teamwork, and fault tolerance |
Model: Fuzzy DEA-VRS Orientation: Output Inputs: - Outputs: Average Wait Time (Unwanted), Average Device Utilization (Desired), Average Flexible Device Utilization (Desired), and Average Fault Tolerance Time (Unwanted) |
Monazzam et al. (2022)Monazzam, N., Alinezhad, A., & Adibi, M. A. (2022). Simulation-based optimization using DEA and DOE in production systems. Scientia Iranica, 29(6), 3470-3488. http://doi.org/10.24200/sci.2021.55499.4253. http://doi.org/10.24200/sci.2021.55499.4...
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Manufacturing - Increase efficiency and determine methods to evaluate and optimize the performance of different parts of production systems |
Simulator: Enterprise Dynamics Specificities: It models different sectors of the production system and integrates them with results on productivity |
Model: Ratio Efficiency Dominant Orientation: - Inputs: production planning, non-mechanized inventories, and assembly shop cycle time Outputs: total production profit, production volume, average productivity of production halls, number of trucks transporting the body and number of semi-finished products during the process |
Tavakoli et al. (2022)Tavakoli, M., Tavakkoli-Moghaddam, R., Mesbahi, R., Ghanavati-Nejad, M., & Tajally, A. (2022). Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study. Medical & Biological Engineering & Computing, 60(4), 969-990. http://doi.org/10.1007/s11517-022-02525-z. PMid:35152366. http://doi.org/10.1007/s11517-022-02525-...
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Health (COVID-19) - Propose a decision support system for hospital managers, who are willing to be more efficient and who rely on data to support decision-making |
Simulator: Arena Specifics: Simulate the process of patient flow and then predict the future entry of patients into a hospital as a case study and suggest some policies based on different likely scenarios |
Model No.: CRS Orientation: Input Inputs: number of doctors on the COVID-19 emergency hotline, number of nurses on the COVID-19 emergency hotline, number of doctors in the ICU, number of nurses in the ICU, number of doctors in the ICU, number of nurses in the ICU, number of radiologists in a CT scan unit, Number of service providers in the laboratory, number of beds in the ICU, number of beds in the ICU and number of beds in the special COVID-19 emergency line Outputs: total time, average patient waiting time, rate of patients discharged, and cost of inputs |
Taleb et al. (2023)Taleb, M., Khalid, R., Ramli, R., & Nawawi, M. K. M. (2023). An integrated approach of discrete event simulation and a non-radial super efficiency data envelopment analysis for performance evaluation of an emergency department. Expert Systems with Applications, 220, 119653. http://doi.org/10.1016/j.eswa.2023.119653. http://doi.org/10.1016/j.eswa.2023.11965...
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Emergency Sector - Investigate the impact of the allocation of human resources (receptionists, nurses, and doctors) in terms of the queue performance indicators |
Simulator: Arena Specificities: Performs the simulation from arrival at the emergency room until discharge |
Model No.: Super Efficiency SBM-VRS Guideline: Not applicable Inputs: number of receptionists, number of nurses and number of doctors Outputs: average length of stay in the emergency room (unwanted) and average length of stay in queues (unwanted) |