Simulation Everywhere: An Evolutionary Expansion of Discrete-Event Modeling and Simulation research and practice

Godwin E. Etti; Ikpe Justice Akpan

REVIEW 3 major objections 5 minor 72 references

Simulation Everywhere: An Evolutionary Expansion of Discrete-Event Modeling and Simulation research and practice

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Discrete-event simulation has not died; a 15-year bibliometric analysis of 2,077 papers concludes it is alive, well, and still expanding as an operations research tool.

desk verdict A useful updated bibliometric snapshot of DES that confirms the field's continued vitality, but the undocumented screening step from 186,797 to 2,077 papers makes the quantitative expansion claims shaky until the methodology is fully disclosed. read the letter →

arxiv 2506.05698 v1 pith:4QZG4BIM submitted 2025-06-06 cs.OH

Ikpe Justice Akpan , Godwin E. Etti This is my paper

classification cs.OH

keywords discrete-eventsimulationbibliometricanalysisoperationsresearcheverywheredigitaltransformationIndustry4.0healthcareCOVID-19modeling

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to settle a long-running dispute in operations research: whether discrete-event simulation (DES) has outlived its usefulness. Using bibliographic metadata from 2,077 peer-reviewed publications between 2010 and 2024, the authors argue that DES is 'alive, well, and kicking,' with a positive (if uneven) annual growth rate of about 2%, a widening spread of application areas, and a central role in modeling COVID-19, digital twins, and Industry 4.0. The claim matters because it reverses the narrative, prominent around 2010, that DES had been superseded by agent-based simulation. The evidence is bibliometric, so the conclusion depends on whether the filtered sample fairly represents the whole field.

What carries the argument

The load-bearing instrument is a scientometric pipeline: a bibliographic database query yields 186,797 candidate records, which are filtered down to 2,077 documents; bibliometric software then generates publication counts, citation counts, co-authorship networks, and keyword co-occurrence maps. The analytical heart is a stratification of author keywords into prominent, emerging, and least-frequent themes, plus a linear regression of annual publication counts. This machinery converts raw publication metadata into the study's evidence of growth, thematic breadth, and international collaboration (19.31% international co-authorship across 89 countries).

What would settle it

Re-run the annual publication counts on the complete 186,797-record query output, or on a differently specified query that does not require 'discret*' in the title or keywords, and check whether the positive 2.03% growth trend and the application-domain spread still appear; if the trend flattens or reverses under reasonable alternative filters, the central claim of continuous expansion would collapse.

Watch

Extended reading notes

Core claim

The central claim is that discrete-event simulation has continuously expanded as an operations research technique from 2010 to 2024, both in volume and in the breadth of problems it tackles. The study reports 2,077 screened publications across 947 sources, with an average annual growth rate of 2.03%, a regression trend of $y = 2.232x + 120.61$ ($R^2 = 0.40$), and adoption across at least a dozen domains, led by computer science (26%) and engineering (21%). Thematic analysis of author keywords shows that DES remains anchored in classic OR topics—optimization, healthcare, logistics, scheduling—while absorbing newer ones such as Industry 4.0, digital twin, COVID-19, machine learning, and Internet of Things. The authors conclude that the 'simulation everywhere' label is now empirically warranted and that the next step is integrating generative AI into DES practice.

Load-bearing premise

The 2,077 selected papers are assumed to represent all discrete-event simulation research, even though the rules that excluded roughly 184,000 other records from the initial query are not fully disclosed.

Editorial extensions

If this is right

DES remains an active OR backbone for optimizing healthcare, logistics, manufacturing, and service operations, not a legacy technique.
The method is absorbing the digital-transformation toolkit—digital twins, virtual reality, IoT, machine learning—suggesting continued demand for simulation skills in industry.
DES is now a standard emergency-response tool, as shown by the 2020 peak in publications driven by COVID-19 modeling.
Simulation education and practice should keep DES at the core of OR curricula alongside agent-based and system-dynamics approaches.
The next expansion frontier is generative AI integration into model building and experimentation, which the authors flag as an open research direction.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editor's inference: the reported growth may be inflated by the COVID-19 publication spike; the raw yearly counts drop from 163 in 2020 to 147 in 2023, so the 'continuous expansion' claim would be better tested by comparing 2010-2019 and 2021-2024 separately.
Editor's inference: the paper's breadth result could reflect keyword relabeling (adding trendy terms like 'digital twin' to old simulation studies) rather than genuinely new application areas; a full-text topic model could check that.
Editor's inference: the unspecified filter that removed 184,666 records is the main uncontrolled variable; re-running the counts on the full query results would show whether the growth trend survives.
Editor's inference: a direct head-to-head bibliometric comparison of DES versus agent-based simulation over the same years would reveal whether DES's survival also meant it retained market share, or just that both grew.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You asked about arXiv:2506.05698. Short version: it's a legitimate but limited bibliometric update, and the central claim—DES is alive, well, and kicking—is almost certainly true, but it was already true in the papers they cite. The value here is the 2010–2024 window and the descriptive breakdowns of domains, themes, and citation impact. If you need a current snapshot of where DES shows up, this is a decent place to look.

What the paper does well: the data collection is broad, the thematic clustering and keyword analysis give a reasonable feel for the field's diversity, and the authors are transparent about their tools (VOSviewer, Bibliometrix). The modeling-process section is clear, and the limitations of simulation are acknowledged. The regression is weak (R² = 0.40), but they report it without overclaiming; the growth trend is visible in the raw counts anyway.

Where it gets soft: the screening step is the load-bearing wall. Table 1 says the Scopus query produced 187,727 documents, then later 187,797; after type exclusions it's 186,797; then they subtract 184,666 as "Non Discrete-Event Simulation in OR" and get 2,131. Then they say "Screened out 9 Irrelevant Documents" but compute 2,131 − 54 = 2,077. These numbers do not reconcile, and the criterion "Non Discrete-Event Simulation in OR" is never defined. No title-abstract protocol, no inter-rater check, no explanation of how 184,666 documents were judged irrelevant. That matters because the yearly publication curve and domain percentages are built on this sample. If the filter was applied unevenly across years or fields, the expansion evidence could be an artifact. The data are proprietary, so outsiders cannot check.

Also, there are avoidable editorial slips: two tables numbered 5, inconsistent author counts (5,627 vs 5,629), and a stray reference [65] that seems unrelated to simulation. These are minor but suggest the manuscript needs another pass.

For a reader, this is a useful survey of DES research output over the last 15 years, but I would not rely on its specific numbers until the sample construction is documented. The central qualitative message is robust and consistent with Brailsford's "alive and kicking" and the authors' own 2024 study. It deserves a serious referee: the topic is relevant to the OR community, and the flaws are fixable. I'd send it to review with major revision, asking for a step-by-step screening protocol, a corrected table of numbers, and a clear statement of what exactly the 184,666 excluded documents were.

Referee Report

3 major / 5 minor

Summary. The manuscript reports a bibliometric study of discrete-event simulation (DES) research and practice from 2010 to 2024, based on 2,077 Scopus-indexed documents. It claims that DES has undergone continuous expansion despite earlier predictions of its demise, identifies application domains and thematic structures, and analyzes citation and collaboration patterns. The paper's central conclusion is that 'DES is alive, well, and kicking,' with evidence of broadened adoption, sustained relevance, and a role in Industry 4.0 and digital transformation.

Significance. If the empirical basis were solid, the study would provide a useful quantitative complement to the qualitative debate about DES's vitality and would update the community's understanding of DES's application landscape. The manuscript applies standard bibliometric tools (VOSviewer, Bibliometrix) and offers detailed descriptive statistics, which are valuable for replication. However, the central claim rests on a sample whose construction is not reproducible from the information provided, and the growth analysis is statistically weak. The paper's contribution is therefore conditional on fixing the sample-construction and trend-analysis issues.

major comments (3)

[§3.1, Table 1] The sample-construction steps are internally inconsistent and not reproducible. The search is stated to generate 187,727 documents, but Table 1 then lists 187,797 in the subtraction; the subtraction '187,797 - 184,666 = 2131' contains a typo (the initial value should be 186,797, if the prior filtering is retained) and yields 2,131, while the text simultaneously reports 'Screened out 9 Irrelevant Documents' and computes '2131 - 54 = 2077.' More fundamentally, the decisive filter that removes 184,666 documents as 'Non Discrete-Event Simulation in OR' is never defined: the manuscript does not state whether irrelevance was judged by title/abstract keywords, manual reading, subject-area exclusion, or some algorithmic rule, nor does it report who applied the judgment or how reliable it was. Because the annual publication trend and the domain percentages in Figure 3 are computed from the final 2,077 documents, the undocumented filter is load-bearing for the central claim.
[§4.2, Figure 2b] The linear regression used to support the growth claim is not convincing as presented. The reported equation y = 2.232x + 120.61 has R² = 0.40, meaning time explains only 40% of the annual variation in publication counts; with only 15 data points, the high 2020 value (driven by COVID-19-related publications, as the text itself notes) could dominate the fitted slope. The text states 'The model is appropriate for this dataset because the trend of the SLP over time exhibits growth,' which is circular rather than a statistical justification. Additionally, the reported annual growth rate of 2.03% (Table 2) is not reconciled with the regression slope of 2.232 publications per year on a base of roughly 120-140, which would be closer to 1.6-1.9% per year; the manuscript offers no measure of uncertainty. These issues undermine the quantitative support for RO1.
[§4.6.1 and §4.1] The author and country counts are inconsistent across the manuscript, which casts doubt on the descriptive statistics. Section 4.1 reports 5,627 researchers, while Table 2 reports Authors 5,629; Section 4.6.1 then reports '1,968 unique authors from 89 countries' with '5,429 total frequencies.' These numbers cannot all be correct descriptions of the same sample. Similarly, the definitions of SCP and MCP in Table 6 are unconventional (SCP is usually single-country publications and MCP multi-country publications, whereas the table defines SCP as non-corresponding-author collaboration and MCP as corresponding-author contribution), and the country-assignment rule (all authors vs. corresponding author) is not specified. Since RO4 concerns social structures, these discrepancies matter for the paper's fourth objective.

minor comments (5)

[§4.5.2] The text has two tables numbered Table 5: 'Ten most cited documents' and 'The top 20 sources with most eminent sources and citation impacts.' Please renumber the second table as Table 6 and adjust subsequent table numbers.
[§4.4.3] The text states 'Figure 4 shows the complete themes under this category' when describing the least-frequent themes, but the relevant visualization is Figure 5. Please correct the cross-reference.
[Throughout] The manuscript contains several typos that should be corrected: 'coronavirus disease 2029' in the Introduction, 'InformaƟon' in the author affiliation line, 'the gift of live' in the Acknowledgments, and 'generative adversarial networks' in Section 4.4.3. These are presentation issues but should be fixed.
[References] Reference 65 (Zhu et al., AGU Fall Meeting, on sulfate simulation) appears irrelevant to the statement in Section 4.4.3 about machine learning and generative adversarial networks; please verify the intended citation.
[§2.2] The sentence 'The first step in the simulation and modeling process involves problem definition or formulation is the first step in the simulation and modeling process' is grammatically garbled and should be rewritten.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the bibliometric trends are descriptive, the linear fit is not reused as a prediction, and the self-citations are not load-bearing.

full rationale

The paper is a bibliometric survey: it counts Scopus-indexed publications matching a query, filters to what the authors judge to be DES-in-OR documents, and reports descriptive statistics such as annual counts, domain shares, citation counts, and collaboration indices. The only fitted equation is the linear trend y = 2.232x + 120.61 with R² = 0.40 (Fig. 2b); the slope and intercept are fitted to the observed annual publication counts and are never reused to derive a prediction or to assert a result that was not already present in the data. No parameter is calibrated on a subset and then presented as a prediction of a closely related quantity. The conclusion that 'DES is alive, well, and kicking' is a summary of the 2,077-document sample, not a consequence of a self-citation or an imported ansatz. Although the paper cites several earlier works by its own authors (e.g., refs 6, 7, 12, 25, 31, 37, 41, 47, 56, 68, 71), none of those citations supplies a load-bearing premise for the central claim; the central evidence is the external Scopus metadata. The undocumented 'Non Discrete-Event Simulation in OR' filter (Table 1) is a legitimate reproducibility/validity concern, but it is not a circular dependency: the selection criterion and the reported descriptive outputs are not equated by construction, and no derived quantity reduces to the selection rule. Accordingly, no circular step can be exhibited under the required standard, and the circularity score is 0.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claims rest on bibliometric assumptions: Scopus represents the field, keywords represent themes, and publication volume represents relevance. The trend line and theme cutoffs are fitted or hand-chosen. No new entities are postulated.

free parameters (3)

Linear trend slope and intercept = y = 2.232x + 120.61, R² = 0.40
Fitted to the annual publication counts in Figure 2b; used to claim steady growth.
Prominent theme cutoff = f >= 10
Hand-chosen threshold dividing 'prominent' from 'emerging' themes in Section 4.4.
Emerging theme cutoff = 4 <= f < 10
Hand-chosen threshold for 'emerging' themes in Section 4.4.

assumptions (3)

domain assumption Scopus coverage is representative of DES research
The paper uses only Scopus and asserts it has wide coverage (Section 3.1).
domain assumption Author keywords reflect research themes
Used to define thematic structure in Section 4.4.
domain assumption Publication count and citations measure relevance
The paper equates growth in publications with sustained relevance of DES.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Simulation Everywhere: An Evolutionary Expansion of Discrete-Event Modeling and Simulation research and practice." pith.science (2026). https://pith.science/paper/4QZG4BIM

@misc{pith2026250605698,
  author       = {Pith},
  title        = {Pith review of: Simulation Everywhere: An Evolutionary Expansion of Discrete-Event Modeling and Simulation research and practice},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4QZG4BIM}},
  note         = {Machine review of arXiv:2506.05698}
}

read the original abstract

Simulation was launched in the 1950s, nicknamed a tool of "last resort." Over the years, this Operations Research (OR) method has made significant progress, and utilizing the accelerated advances in computer science (hardware and software, processing speed, and advanced information visualization capabilities) to improve simulation usability in research and practice. After overcoming the initial obstacles and the scare of outliving its usefulness in the 2000s, computer simulation has remained a popular OR tool applied in diverse industries and sectors, earning its popularity leading to the term "simulation everywhere." This study uses bibliographic data from research and practice literature to evaluate the evolutionary expansion in simulation, focusing on discrete-event simulation (DES). The results show asymmetrical but positive yearly literature out-put, broadened DES adoption in diverse fields, and sustained relevance as a scientific method for tackling old, new, and emerging issues. Also, DES is an essential tool in Industry 4.0 and plays a central role in digital transformation that has swept the industrial space, from manufacturing to healthcare and other sectors. With the emergence, ongoing adoption, and deployment of generative artificial intelligence (GenAI), future studies seek ways to integrate GenAI in DES to remain relevant and improve the modeling and simulation processes.

Figures

Figures reproduced from arXiv: 2506.05698 by the authors.

**Figure 3.** Discrete-event simulaƟon applicaƟon domains 4.4 Thematic Structure of DES Research and Evolution This Section analyzes the thematic structure and evolutionary trends through a science mapping of DES research and practice. The preliminary results of the text analytics using the R-Bibliometrix identifies 4121 unique but unstemmed author keywords with total word frequency of 7,501 extracted from 2,077 SLPs. The basis f… view at source ↗

**Figure 4.** VisualizaƟon of emerging research themes and co-occurrence of author keywords [PITH_FULL_IMAGE:figures/full_fig_p016_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: VisualizaƟon of the themaƟc structure involving the least frequent author keywords of four or less occurrences (f < 4). The period segments 2022-2024, and post-2024 identify themes such as "mass vaccination," "capacity analysis," and "ambulance deployment," pointing to…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

72 extracted references · 71 canonical work pages

[65]

Ambient Sulfate Simulation over the Global South: Insights from GEOS-Chem and the SPARTAN Measurement Network

Zhu H, Martin RV, Oxford CR, Liu W, Hou W. Ambient Sulfate Simulation over the Global South: Insights from GEOS-Chem and the SPARTAN Measurement Network. In AGU Fall Meeting Abstracts 2024, Dec (Vol. 2024, pp. GC22C-02)

work page 2024
[1]

Simulation techniques in operations research - a review

Harling, J. Simulation techniques in operations research - a review. Operations Research 1958, 6(3), 307-319

work page 1958
[2]

and Lucas, T.W

Powers, M.J., Sanchez, S.M. and Lucas, T.W. The exponential expansion of simulation in research. In Proceedings of the 2012 Winter Simulation Conference (WSC) 2012, 1-12. IEEE

work page 2012
[3]

Computer simulation in management science, 5e, 2004

Pidd, M. Computer simulation in management science, 5e, 2004. Chichester. ISBN 0-470-09230-0, Wiley

work page 2004
[4]

Optimization via simulation: A review

Fu, M.C. Optimization via simulation: A review. Annals of Operations Research 1994, 53, 199-247

work page 1994
[5]

J., Robinson, S

Brooks, R. J., Robinson, S. L. Simulation and Inventory Control: Texts in Operational Research. 2001. Basingstoke, Palgrave Macmillan

work page 2001
[6]

J.; Shanker, M.; Razavi, R

Akpan, I. J.; Shanker, M.; Razavi, R. Improving the success of simulation projects using 3D visualization and virtual reality. Journal of the Operational Research Society 2020, 71(12), 1900-1926

work page 2020
[7]

J., Brooks, R

Akpan, I. J., Brooks, R. J. Users’ perceptions of the relative costs and benefits of 2D and 3D visual displays in discrete-event simulation. Simulation 2012, 88(4), 464-480. doi:10.1177/0037549711423734

work page doi:10.1177/0037549711423734 2012

Show all 72 references

[8]

Key performance indicators for successful simulation projects

Jahangirian, M, Taylor, S.J., Young, T., Robinson, S. Key performance indicators for successful simulation projects. Journal of the Operational Research Society 2017, 68(7), 747-65

2017
[9]

Why continuous simulation? The role of antecedent moisture in design flood estimation

Pathiraja, S., Westra, S., Sharma, A. Why continuous simulation? The role of antecedent moisture in design flood estimation. Water Resources Research 2012, 48(6), 1-15. doi:10.1029/2011WR010997

2012 doi
[10]

Discrete vs

Özgün O, Barlas Y. Discrete vs. continuous simulation: When does it matter. In Proceedings of the 27th international conference of the system dynamics society 2009 (Jul 26), 6, 1-22

2009
[11]

Discrete-Event Simulation

Goldsman, D., Goldsman, P. Discrete-Event Simulation. In: Loper, M. (eds) Modeling and Simulation in the Systems Engineering Life Cycle. Simulation Foundations, Methods and Applications, 2015. Springer, London. https://doi.org/10.1007/978-1-4471-5634-5_10

2015 doi
[12]

J., Brooks, R

Akpan, I. J., Brooks, R. J. Experimental evaluation of user performance on two-dimensional and three- dimensional perspective displays in discrete event simulation. Decision Support Systems 2014, 64, 14–30. doi:10.1016/j.dss.2014.04.002

2014 doi
[13]

The use of search experimentation in discrete-event simulation practice

Hoad, K, Monks, T, O'brien, F. The use of search experimentation in discrete-event simulation practice. Journal of the Operational Research Society 2015 66(7), 1155-68

2015
[14]

Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department

Baril, C, Gascon, V, Vadeboncoeur, D. Discrete-event simulation and design of experiments to study ambulatory patient waiting time in an emergency department. Journal of the Operational Research Society 2019, 70(12), 2019-38

2019
[15]

Simulation modelling is 50! Do we need a reality check? Journal of the Operational Research Society 2009, S69-S82

Taylor, S.J., Eldabi, T., Riley, G., Paul, R.J., Pidd, M. Simulation modelling is 50! Do we need a reality check? Journal of the Operational Research Society 2009, S69-S82

2009
[16]

So where to next? A survey of the future for discrete-event simulation

Taylor, Taylor, S.J., Robinson, S. So where to next? A survey of the future for discrete-event simulation. Journal of Simulation 1(1), 2006, 1-6

2006
[17]

O., Macal, C

Siebers, P. O., Macal, C. M., Garnett, J., Buxton, D., Pidd, M. Discrete-event simulation is dead, long live agent-based simulation! Journal of Simulation 2010, 4(3), 204-210

2010
[18]

Cross-paradigm simulation modeling: challenges and successes

Heath SK, Brailsford SC, Buss A and Macal CM 2011. Cross-paradigm simulation modeling: challenges and successes. In: Jain S, Creasey RR, Himmelspach J, White KP and Fu M eds. Proceedings of the 2011 Winter Simulation Conference 2011, 2783-2797. IEEE

2011
[19]

Discrete-event simulation is alive and kicking! Journal of Simulation 2014, 8(1), 1-8, DOI: 10.1057/jos.2013.13

Brailsford, S. Discrete-event simulation is alive and kicking! Journal of Simulation 2014, 8(1), 1-8, DOI: 10.1057/jos.2013.13

2014 doi
[20]

Fakhimi, M., Anagnostou, A., Stergioulas, L., Taylor, S. J. A hybrid agent-based and discrete-event simulation approach for sustainable strategic planning and simulation analytics. In Proceedings of the Winter Simulation Conference, 2014, 1573-1584. IEEE

2014
[21]

Integrating agent-based modeling into a discrete event simulation

Dubiel B, Tsimhoni O. Integrating agent-based modeling into a discrete event simulation. In Proceedings of the Winter Simulation Conference, 2005. 9. IEEE

2005
[22]

and Viana, J

Brailsford, S.C., Desai, S.M. and Viana, J. Towards the holy grail: combining system dynamics and discrete-event simulation in healthcare. In Proceedings of the 2010 winter simulation conference 2010, 2293-2303. IEEE. DOI: 10.1109/WSC.2010.5678927

2010
[23]

and Harper, P.R., 2014

Viana, J., Brailsford, S.C., Harindra, V. and Harper, P.R., 2014. Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection. European Journal of Operational Research 2014, 237(1), 196-206. DOI:10.1016/j.ejor.2014.02.052

2014 doi
[24]

Page, E. H. WSC turns 50: simulation everywhere! In Proceedings of the Winter Simulation Conference 2017, Dec. 3-6, Red Rock Resort, Las Vegas, USA. https://doi.org/10.1287/LYTX.2017.05.12

2017 doi
[25]

Discrete-event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID-19 global health pandemic

Akpan, I.J., Shanker, M., Offodile, O., F. Discrete-event simulation is still alive and strong: evidence from bibliometric performance evaluation of research during COVID-19 global health pandemic. International Transactions in Operational Research 2024, 31(4), 2069-92

2024
[26]

S., Fowler, J

Currie, C. S., Fowler, J. W., Kotiadis, K., Monks, T., Onggo, B. S., Robertson, D. A., Tako, A. A. How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation 2020. 14(2), 83- 97

2020
[27]

Discrete-event simulation study of a COVID-19 mass vaccination centre

Sala F, D'Urso G, Giardini C. Discrete-event simulation study of a COVID-19 mass vaccination centre. International Journal of Medical Informatics 2023, 170:104940

2023
[28]

Discrete-event simulation: an inquiry into user practice

Hollocks, B.W. Discrete-event simulation: an inquiry into user practice. Simulation Practice and Theory. 2001, 8(6-7), 451-71

2001
[29]

A discrete event simulation model to evaluate operational performance of a colonoscopy suite

Berg, B., Denton, B., Nelson, H., Balasubramanian, H., Rahman, A., Bailey, A., Lindor, K. A discrete event simulation model to evaluate operational performance of a colonoscopy suite. Medical Decision Making 2010, 30(3), 380-387

2010
[30]

J., Wang, W

Brooks, R. J., Wang, W. Conceptual Modelling and the Project Process in Real Simulation Projects: A Survey of Simulation Modellers. Journal of the Operational Research Society 2015, 66(10), 1669-1685

2015
[31]

J.; Shanker, M

Akpan, I. J.; Shanker, M. A comparative evaluation of the effectiveness of virtual reality, 3D visualization and 2D visual interactive simulation: an exploratory meta-analysis. Simulation 2019, 95(2), 145-170

2019
[32]

Discrete-event simulation

Goldsman, D., Goldsman, P. Discrete-event simulation. Modeling and Simulation in the Systems Engineering Life Cycle: Core Concepts and Accompanying Lectures 2015, 103-109. London: Springer

2015
[33]

F., Montevechi, J

Pereira, T. F., Montevechi, J. A. B., Miranda, R. D. C., Friend, J. D. Integrating soft systems methodology to aid simulation conceptual modeling. International Transactions in Operational Research 2015, 22(2), 265-285

2015
[34]

Conceptual Modeling for Discrete-Event Simulation

Robinson, S., Brooks, R., Kotiadis, K., Van Der Zee, D.J. Conceptual Modeling for Discrete-Event Simulation. Interfaces 2011, 41(6), 601-603

2011
[35]

P.; Edwards, J

Robinson, S.; Lee, E. P.; Edwards, J. S. Simulation based knowledge elicitation: effect of visual representation and model parameters. Expert Systems with Application. 2012, 39(9), 8479–8489

2012
[36]

P.; Ladbrook, J

Waller, A. P.; Ladbrook, J. Virtual worlds: experiencing virtual factories of the future. In Proceedings of the 34th Winter simulation conference: exploring new frontiers, San Diego, CA, Dec 8, 2002, 513–

2002
[37]

I.; Brooks, R

Akpan, J. I.; Brooks, R. J. Practitioners’ perception of the impacts of virtual reality on discrete-event simulation. In Proceedings of the 2005 Winter simulation conference, Orlando, FL, Dec 4, 2005, 9. Piscataway, NJ: IEEE

2005
[38]

Validation, verification, and testing techniques throughout the life cycle of a simulation study

Balci, O. Validation, verification, and testing techniques throughout the life cycle of a simulation study. Annals of Operations Research 1994 53(1), 121-173

1994
[39]

Validating complex construction simulation models using 3D visualization

Kamat VR, Martinez JC. Validating complex construction simulation models using 3D visualization. Systems Analysis Modelling Simulation 2003, 43(4), 455-67

2003
[40]

Methodology for conducting discrete-event simulation studies in construction engineering and management

Martinez, J.C. Methodology for conducting discrete-event simulation studies in construction engineering and management. Journal of Construction Engineering and Management 2010, 136(1), 3-16

2010
[41]

Experimental investigation of the impacts of virtual reality on discrete-event simulation

Akpan, J.I., Brooks, R.J. Experimental investigation of the impacts of virtual reality on discrete-event simulation. In Proceedings of the Winter Simulation Conference, 2005, Dec 4, 8. IEEE

2005
[42]

Effectiveness of visual interactive modeling in the context of multiple-criteria Group decisions

Jain, H.K., Ramamurthy, K., Sundaram, S. Effectiveness of visual interactive modeling in the context of multiple-criteria Group decisions. Systems, Man and Cybernetics Part A 2006, 36(2), 298-318

2006
[43]

E., Nelson, B

Wan, H., Ankenman, B. E., Nelson, B. L. Controlled Sequential Bifurcation: A New Factor-Screening Method for Discrete-Event Simulation. Operations Research 2006, 54(4), 743-755

2006
[44]

Seymour, N. E. VR to OR: a review of the evidence that virtual reality simulation improves operating room performance. World Journal of Surgery. 2018, 32, 182-188

2018
[45]

Jacso, P. 2005. As we may search—comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases. Current Science, 89, 1537–1547

2005
[46]

Herrero-Solana, V

de Moya-Anegón, F., Chinchilla-Rodríguez, Z., Vargas-Quesada, B., Corera-Álvarez, E., José Muñoz- Fernández, F., González-Molina, A. Herrero-Solana, V. Coverage analysis of Scopus: A journal metric approach. Scientometrics 2007, 73, 1, 53-78

2007
[47]

M., Akpan, J

Kobara, Y. M., Akpan, J. I., Nam, A. D., AlMukhthar, F. H., Peter, M. Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images. Journal of Imaging Informatics in Medicine 2025, 1-21. https://doi.org/10.1007/s10278-025-01538-y

2025 doi
[48]

J.; Waltman, L

Van Eck, N. J.; Waltman, L. Software Survey: VOSviewer, a Computer Program for Bibliometric Mapping. Scientometrics. 2010, 84(2), 523–538

2010
[49]

A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS

Van Eck N.J., Waltman L., Dekker R., van den Berg J. A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. Journal of the American Society for Information Science and Technology 2010, 61(12), 2405-2416

2010
[50]

Bibliometrix: An R-tool for comprehensive science mapping analysis

Aria, M.; Cuccurullo, C. Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of. Informetrics. 2017, 11, 959–975

2017
[51]

How to conduct a bibliometric analysis: An overview and guidelines

Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res., 2021, 133, 285–296

2021
[52]

Descriptive versus evaluative bibliometrics

Van Leeuwen, T. Descriptive versus evaluative bibliometrics. In Handbook of Quantitative Science and Technology Research 2004, 373-388. Springer, Dordrecht

2004
[53]

J., López-Herrera, A

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., Herrera, F. Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology 2011, 62(7), 1382-1402

2011
[54]

Information Sciences 1968–2016: A retrospective analysis with text mining and bibliometric

Yu, D., Xu, Z., Pedrycz, W., Wang, W. Information Sciences 1968–2016: A retrospective analysis with text mining and bibliometric. Information Sciences, 2017, 418, 619-634

1968
[55]

Yan, E.; Ding, Y. Scholarly network similarities: how bibliographic coupling networks, citation networks, co-citation networks, topical networks, co-authorship networks, and co-word networks relate to each other. Journal of the American Society for Information Science and Tech...

2012
[56]

Bibliometric performance and future relevance of virtual manufacturing technology in the fourth industrial revolution

Kobara, Y.M., Akpan, I.J. Bibliometric performance and future relevance of virtual manufacturing technology in the fourth industrial revolution. Systems 2023, 11(10), 524

2023
[57]

A systematic review of text stemming techniques

Singh, J., Gupta, V. A systematic review of text stemming techniques. Artificial Intelligence Review. 2017 48, 157-217

2017
[58]

A, Robinson, S

Tako, A. A, Robinson, S. The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems. 2012, 52(4), 802-815

2012
[59]

Applications of simulation within the healthcare context

Katsaliaki, K., Mustafee, N. Applications of simulation within the healthcare context. Journal of The Operational Research Society. 2011, 62(8), 1431-1451

2011
[60]

and Möller, J

Karnon, J., Stahl, J., Brennan, A., Caro, J.J., Mar, J. and Möller, J. Modeling Using Discrete Event Simulation: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force–4. Medical Decision Making 2012, 32(5), 701-711. DOI:10.1016/j.jval.2012.04.013

2012 doi
[61]

SimLean: Utilising simulation in the implementation of lean in healthcare

Robinson, S., Radnor, Z.J., Burgess, N., Worthington, C. SimLean: Utilising simulation in the implementation of lean in healthcare. European Journal of Operational Research. 2012, 219(1):188- 197

2012
[62]

Application of discrete event simulation in health care: a systematic review

Zhang, X. Application of discrete event simulation in health care: a systematic review. BMC Health Services Research, 2018, 687(2018), 1-11. DOI:10.1186/s12913-018-3456-4

2018 doi
[63]

J.; Hutabarat, W.; Oyekan, J.; Tiwari, A

Turner, C. J.; Hutabarat, W.; Oyekan, J.; Tiwari, A. Discrete event simulation and virtual reality use in industry: new opportunities and future trends. IEEE Transactions on Human-Machine Systems. 2016, 46(6), 882-894

2016
[64]

Macal, C.M

Chan, W.K.V., Son, Y.J. Macal, C.M. Agent-based simulation tutorial-simulation of emergent behavior and differences between agent-based simulation and discrete-event simulation. In Proceedings of the 2010 Winter Simulation Conference, 2010, 135-150. IEEE. DOI: 10.1109/WSC.2010.5679168

2010
[66]

Au, G., Paul, R. J. Visual interactive modelling: A pictorial simulation specification system, European Journal of Operational Research 1996, 9(11), 14–26

1996
[67]

Symbiotic simulation system (S3) for industry 4.0

Onggo, B.S. Symbiotic simulation system (S3) for industry 4.0. In Simulation for Industry 4.0: Past, Present, and Future 2019, 153-165. Cham: Springer International Publishing

2019
[68]

and Offodile, O.F

Akpan, I.J. and Offodile, O.F. The role of virtual reality simulation in manufacturing in industry 4.0. Systems 2024, 12(1), p.26

2024
[69]

Simulation in industry 4.0: A state-of- the-art review

Gunal, de Paula Ferreira W, Armellini F, De Santa-Eulalia LA. Simulation in industry 4.0: A state-of- the-art review. Computers & Industrial Engineering. 2020, 149, 106868. https://doi.org/10.1016/j.cie.2020.106868

2020
[70]

Simulation optimization in the era of Industrial 4.0 and the Industrial Internet

Xu, J., Huang, E., Hsieh, L., Lee, L.H., Jia, Q.S., Chen, C.H. Simulation optimization in the era of Industrial 4.0 and the Industrial Internet. Journal of Simulation. 2016, 10(4), 310-20

2016
[71]

I., Razavi, R., Akpan, A

Akpan, J. I., Razavi, R., Akpan, A. A. Evolutionary trends in decision sciences education research from simulation and games to big data analytics and generative artificial intelligence. Big Data 2025, 1–23. doi:10.1089/big.2024.0128

2025
[72]

and Jalali, M.S., 2024

Akhavan, A. and Jalali, M.S., 2024. Generative AI and simulation modeling: how should you (not) use large language models like ChatGPT. System Dynamics Review 2024, 40(3), p.e1773. https://doi.org/10.1002/sdr.1773

2024 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [65]

Ambient Sulfate Simulation over the Global South: Insights from GEOS-Chem and the SPARTAN Measurement Network

Zhu H, Martin RV, Oxford CR, Liu W, Hou W. Ambient Sulfate Simulation over the Global South: Insights from GEOS-Chem and the SPARTAN Measurement Network. In AGU Fall Meeting Abstracts 2024, Dec (Vol. 2024, pp. GC22C-02)

work page 2024

[2] [1]

Simulation techniques in operations research - a review

Harling, J. Simulation techniques in operations research - a review. Operations Research 1958, 6(3), 307-319

work page 1958

[3] [2]

and Lucas, T.W

Powers, M.J., Sanchez, S.M. and Lucas, T.W. The exponential expansion of simulation in research. In Proceedings of the 2012 Winter Simulation Conference (WSC) 2012, 1-12. IEEE

work page 2012

[4] [3]

Computer simulation in management science, 5e, 2004

Pidd, M. Computer simulation in management science, 5e, 2004. Chichester. ISBN 0-470-09230-0, Wiley

work page 2004

[5] [4]

Optimization via simulation: A review

Fu, M.C. Optimization via simulation: A review. Annals of Operations Research 1994, 53, 199-247

work page 1994

[6] [5]

J., Robinson, S

Brooks, R. J., Robinson, S. L. Simulation and Inventory Control: Texts in Operational Research. 2001. Basingstoke, Palgrave Macmillan

work page 2001

[7] [6]

J.; Shanker, M.; Razavi, R

Akpan, I. J.; Shanker, M.; Razavi, R. Improving the success of simulation projects using 3D visualization and virtual reality. Journal of the Operational Research Society 2020, 71(12), 1900-1926

work page 2020

[8] [7]

J., Brooks, R

Akpan, I. J., Brooks, R. J. Users’ perceptions of the relative costs and benefits of 2D and 3D visual displays in discrete-event simulation. Simulation 2012, 88(4), 464-480. doi:10.1177/0037549711423734

work page doi:10.1177/0037549711423734 2012