Agent-based epidemics simulation to compare and explain screening and vaccination prioritization strategies

Adam, Carole; Arduin, Hélène

doi:10.1177/00375497231178303

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…4 The principles of Open Science have also been applied specifically to the modeling and simulation field to promote reproducibility. 5 Publications in journals like SIMULATION can provide access to the simulator, which may run within a web browser 6 (e.g., via NetLogo) so that other modelers can fully examine the code and replicate published results. However, reproducibility within the field of modeling and simulation can become complicated.…”

Section: Introductionmentioning

confidence: 99%

Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations

Liang,

Phan,

Giabbanelli

2024

SIMULATION

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A stochastic network simulation is verified when its distribution of outputs is aligned with the ground truth, while tolerating deviations due to variability in real-world measurements and the randomness of a stochastic simulation. However, comparing distributions may yield false positives, as erroneous simulations may have the expected distribution yet present aberrations in low-level patterns. For instance, the number of sick individuals may present the right trend over time, but the wrong individuals were infected. We previously proposed an approach that transforms simulation traces into images verified by machine learning algorithms that account for low-level patterns. We demonstrated the viability of this approach when many simulation traces are compared with a large ground truth data set. However, ground truth data are often limited. For example, a publication may include few images of their simulation as illustrations; hence, teams that independently re-implement the model can only compare low-level patterns with few cases. In this paper, we examine whether our approach can be utilized with very small data sets (e.g., 5–10 images), as provided in publications. Depending on the network simulation model (e.g., rumor spread, cascading failure, and disease spread), we show that results obtained with little data can even surpass results obtained with moderate amounts of data at the cost of variability. Although a good accuracy is obtained in detecting several forms of errors, this paper is only a first step in the use of this technique for verification; hence, future works should assess the applicability of our approach to other types of network simulations.

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Section: Introductionmentioning

confidence: 99%

Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations

Liang,

Phan,

Giabbanelli

2024

SIMULATION

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Transitioning from AGVs to AIVs in integrated job shop scheduling with transportation tasks: a multi-agent simulator for comparative analysis

Sanogo,

Mekhalef Benhafssa,

Sahnoun

2024

SIMULATION

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Optimizing job shop scheduling in modern factories demands flexibility and adaptability to handle unexpected events and Unmanned Ground Vehicles (UGVs) limitations. This paper addresses these challenges by introducing a novel multi-agent simulator for the Job Shop Scheduling Problem (JSSP) with UGVs handling transportation tasks. The simulator, designed with Netlogo, incorporates real-world constraints, such as collision avoidance, UGV fleet size, and battery limitations, often overlooked in prior studies. By comparing the two categories of UGVs, namely, Autonomous Guided Vehicles (AGVs) and Autonomous Intelligent Vehicles (AIVs), under different scheduling methods (static vs dynamic), we evaluate their performance in constrained manufacturing environments. Our findings highlight the superior performance of AIVs in terms of overall makespan and resilience to additional constraints. Among other results, we found that schedules with a fleet of 2 AIVs produced similar makespans than schedules with a fleet of 4 AGVs. Consequently, the simulator and the conducted study provide valuable insights for optimizing JSSP within constrained environments and making informed decisions regarding AIVs adoption.

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Agent-based epidemics simulation to compare and explain screening and vaccination prioritization strategies

Cited by 2 publications

References 38 publications

Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations

Experimental evaluation of a machine learning approach to improve the reproducibility of network simulations

Transitioning from AGVs to AIVs in integrated job shop scheduling with transportation tasks: a multi-agent simulator for comparative analysis

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