Modeling and simulation: A comparative and systematic statistical review
Open accessAbstract
Modeling and simulation took a considerable part of both professional and educational fields during the past few years. With the emergence of new technologies, several modeling and simulation tools, languages and softwares were developed in different areas, and are even introduced in the educational program, which can only prove their importance, especially when it comes to decision-making process. Both concepts also took part of the industry 4.0 paradigm and can be used to test and validate the digital technologies to facilitate their implementation. This variety of modeling and simulation solutions lead us to the following challenging question: which solution to adopt for which case study? The purpose of this article is to set a list of the different modeling and simulation tools, languages and softwares through a statistical literature review, focusing on the industrial field, precisely when it comes to production flow optimization and waste elimination in accordance with lean manufacturing strategy, to determine the pros and cons of each modeling and simulation solution and therefore serve as a referential for companies to identify the best match to their need.
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El uso de la simulación con Flexsim permite predecir el comportamiento del sistema durante largos intervalos de tiempo, logrando identificar posibles cuellos de botella o puntos críticos del sistema.
“Añadiendo el uso de la simulación con Flexsim se puede predecir el comportamiento del sistema durante largos intervalos de tiempo, logrando identificar posibles cuellos de botella o puntos críticos del sistema[19],[20].”
From Integración del IoT a un sistema Kanban para el ensamble de elementos plásticos (2026) · cites this paper as [20]
Cites
Industry 4.0 Concept: Background and Overview
Rojko, A. (2017) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v11i5.7072 · Cited by 1,023 · see citing works
Business process modelling: Review and framework
Aguilar-Savén, R. S. (2003) · International Journal of Production Economics · doi:10.1016/s0925-5273(03)00102-6 · Cited by 926 · see citing works
Proceedings of the 2004 Winter Simulation Conference, 2004.
(2004) · doi:10.1109/wsc.2004 · Cited by 592 · see citing works
2014 Science and Information Conference
(2014) · doi:10.1109/sai33325.2014 · Cited by 501 · see citing works
Evaluation of mathematical models for flexible job-shop scheduling problems
Demi̇r, Y., & İşleyen, S. K. (2012) · Applied Mathematical Modelling · doi:10.1016/j.apm.2012.03.020 · Cited by 205 · see citing works
Jadhav, A. S., & Sonar, R. (2011) · Journal of Systems and Software · doi:10.1016/j.jss.2011.03.034 · Cited by 133 · see citing works
Bottleneck Identification And Process Improvement By Lean Six Sigma DMAIC Methodology
Nandakumar, N., Saleeshya, P. G., & Harikumar, P. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.04.436 · Cited by 99 · see citing works
Barriers to adopting simulation modelling in construction industry
Abdelmegid, M., González, V. A., Poshdar, M., O’Sullivan, M., Walker, C. G., & Ying, F. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.103046 · Cited by 90 · see citing works
Introduction to Modeling and Simulation
Banks, C. M. (2010) · Wiley eBooks · doi:10.1002/9780470590621.ch1 · Cited by 67 · see citing works
Lean and industry 4.0: A leading harmony
Naciri, L., Mouhib, Z., Gallab, M., Nali, M., Abbou, R., & Kebe, A. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.238 · Cited by 62 · see citing works
Self-citationModelling the engineering design system to improve performance
Girard, P., & Doumeingts, G. (2003) · Computers & Industrial Engineering · doi:10.1016/j.cie.2003.09.008 · Cited by 62 · see citing works
Banks, J. (1991) · NCSU Libraries Repository (North Carolina State University Libraries) · doi:10.5555/304238.304249 · Cited by 61 · see citing works
A hierarchical framework for evaluating simulation software
Nikoukaran, J., Hlupić, V., & Paul, R. J. (1999) · Simulation Practice and Theory · doi:10.1016/s0928-4869(98)00028-7 · Cited by 55 · see citing works
A quality framework for developing and evaluating original software components
Andreou, A. S., & Tziakouris, M. (2006) · Information and Software Technology · doi:10.1016/j.infsof.2006.03.007 · Cited by 55 · see citing works
A case study on process modelling — Three questions and three techniques
Glassey, O. (2007) · Decision Support Systems · doi:10.1016/j.dss.2007.10.004 · Cited by 53 · see citing works
Intelligent assembly modeling and simulation
Gupta, S. K., Paredis, C. J. J., Sinha, R., & Brown, P. F. (2001) · Assembly Automation · doi:10.1108/01445150110399151 · Cited by 45 · see citing works
A methodology and tool environment for process analysis and reengineering
Castano, S., Antonellis, V. D., & Melchiori, M. (1999) · Data & Knowledge Engineering · doi:10.1016/s0169-023x(99)00028-2 · Cited by 43 · see citing works
Methodological approach to manufacturing simulation software selection
Hlupić, V., & Paul, R. J. (1996) · Computer Integrated Manufacturing Systems · doi:10.1016/0951-5240(95)00037-2 · Cited by 36 · see citing works
Digitalized value stream mapping: review and outlook
Horsthofer-Rauch, J., Schumann, M., Milde, M., Vernim, S., & Reinhart, G. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.09.079 · Cited by 34 · see citing works
Modeling Supply Chain Performance
Dossou, P.-E., & Nachidi, M. L. (2017) · Procedia Manufacturing · doi:10.1016/j.promfg.2017.07.186 · Cited by 31 · see citing works
A Centralized Approach to Factory Simulation
Brown, S. L. (1997) · Cited by 30 · see citing works
Holder, K. (1990) · OR Insight · doi:10.1057/ori.1990.32 · Cited by 28 · see citing works
Analysis and design of co-operative work processes: a framework
Nurcan, S. (1998) · Information and Software Technology · doi:10.1016/s0950-5849(98)00048-2 · Cited by 25 · see citing works
Method for identification and integration of design automation tasks in industrial contexts
Rigger, E., Shea, K., & Stanković, T. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101558 · Cited by 20 · see citing works
Multimodal Approach to Modeling of Manufacturing Processes
Pawlewski, P. (2014) · Procedia CIRP · doi:10.1016/j.procir.2014.01.130 · Cited by 20 · see citing works
Reconfiguration of production systems using optimization and material flow simulation
Hoellthaler, G., Schreiber, M., Vernickel, K., Isa, J. B., Fischer, J., Weinert, N., Rosen, R., & Braunreuther, S. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.024 · Cited by 19 · see citing works
An approach for Business Process Improvement Based on Simulation Technique
Mehdouani, K., Missaoui, N., & Ghannouchi, S. A. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.12.176 · Cited by 16 · see citing works
Korayem, G. B., & Alboghdadly, A. M. (2020) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2020.06.004 · Cited by 15 · see citing works
A data-driven approach for Decision-Making support of factory simulation solutions
Yu, F., & Nielsen, C. P. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.129 · Cited by 10 · see citing works
Decision support for occupational risk overcome in maintenance activities
Gallab, M., Bouloiz, H., & Tkiouat, M. (2015) · Science and Information Conference · doi:10.1109/sai.2015.7237177 · Cited by 4 · see citing works
Self-citationOjsteršek, R., Palčić, I., & Buchmeister, B. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.201 · Cited by 2 · see citing works
“GANTT CHARTS FOR PRODUCTION FLOW” FRAMEWORK
Genua, C., Giuffrida, S., & Rinaudo, S. (2006) · IFAC Proceedings Volumes · doi:10.3182/20060517-3-fr-2903.00350 · Cited by 2 · see citing works
Associating Discrete Part Manufacturing Systems Semantics to SADT by Means of Petri Nets
Zaytoon, J., Lecolier, G. V., & Loor, P. D. (1995) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)46653-5 · Cited by 2 · see citing works
An efficient and user-friendly optimization framework for batch process scheduling
Tometzki, T., Neymann, T., Steimel, J., Subbiah, S., & Engell, S. (2010) · IFAC Proceedings Volumes · doi:10.3182/20100908-3-pt-3007.00007 · Cited by 1 · see citing works
From Enterprise Modelling to Decision Support System Definition for Virtual Enterprise
Zolghadri, M., Lecompte, T., & Bourrières, J.-P. (2001) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)33258-5 · Cited by 1 · see citing works
A GRAI Extended Approach with Information Aggregation Mechanisms
Aldanondo, M., & Breuil, D. (1994) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)46047-2 · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
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Simulation Model as an Element of Sustainable Autonomous Mobile Robot Fleet Management
Dobrzańska, M., & Dobrzański, P. (2025) · Energies · doi:10.3390/en18081894 · Cited by 5 · see citing works
Fuzzy Modeling and Tracking Control of Fuel Consumption in Cargo Ships
El‐Amrani, A., Barhrhouj, A., Ananou, B., & Ouladsine, M. (2025) · International Conference on Control and Fault-Tolerant Systems (SysTol) · doi:10.1109/systol66549.2025.11267343 · Cited by 1 · see citing works
Nielsen, M. K., Beliatis, M. J., & Tambo, T. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.01.159 · Cited by 1 · see citing works
Application of digital twin using discrete event simulation in intralogistics processes
Dobrzańska, M., & Dobrzański, P. (2025) · Acta Tecnología · doi:10.22306/atec.v11i2.279 · Cited by 1 · see citing works
Shang, F., Qin, X., Li, Z., Chen, H., Gao, T., Liu, Z., & Meng, X. (2026) · Proceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · doi:10.1177/14644207261473887 · Cited by 1 · see citing works
Lim, L. R., & Tan, A. W. K. (2024) · Green Manufacturing Open · doi:10.20517/gmo.2024.051401 · Cited by 1 · see citing works
Integración del IoT a un sistema Kanban para el ensamble de elementos plásticos
Ovalle Castiblanco, A. M., Vargas Sanchez, C. A., & Buriticá Rodriguez, J. E. (2026) · Ingeniería y Desarrollo · doi:10.14482/inde.44.01.632.655
Hybrid semi-parametric model for ship power prediction*
Chouikri, K., Graton, G., Noura, H. N., & Rapuc, S. (2025) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med64031.2025.11073208
Benchmarking Graphics Rendering Capabilities: Java Processing vs. P5.js
Firdaus, M. B., Darma, A. S., Arifin, Z., Anam, M. K., Halim, M. Y., & Yunianta, A. (2025) · Advance Sustainable Science Engineering and Technology · doi:10.26877/asset.v8i1.2036
VISP: Using Virtual Reality to Teach Production Process Optimization through Gamified Simulation
Niedermayr, D., Wolfartsberger, J., Bokor, B., Seiringer, W., & Altendorfer, K. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.02.356
A Systematic Review of Different Carbon Capture Technology Simulation Tools
Keshvarinia, M., MacKenzie, C. A., & Wright, M. M. (2026) · Energies · doi:10.3390/en19132988
Križanić, S., Hrustek, N. Ž., & Vrček, N. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591812
Enhancing Numerical Modeling Performance via Parallel TDMA Implementations
Zhulkovskyi, O., Zhulkovska, I., Nebaba, N. A., Gulesha, O., Kostenko, V., & Riabovolenko, V. (2026) · International Conference on Advanced Computer Information Technologies (ACIT) · doi:10.1109/acit69831.2026.11699986
- Authors
- Naciri, L., Gallab, M., Soulhi, A., Merzouk, S., & Nardo, M. D.
- Affiliations
- National School of Mineral Industry (MA)
- École Marocaine des Sciences de l'Ingénieur (MA)
- University of Naples Federico II (IT)
- Corresponding author
- Naciri, Lina
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2024
- Volume
- 232
- Pages
- 242-253
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W4392975297
- Cited by
- 11
- References
- 37
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 1
- FWCI
- 6.90
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- modeling and simulation, simulation software, simulation languages, production flow optimization, waste elimination, lean manufacturing, Industry 4.0, decision-making, tool selection
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