Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in thevineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, andincreasing in grape losses rates. The objective of this research is to investigate how the variation inworkers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.
Source: MrrTrack model
Estimated active users × assumed paying share × estimated monthly revenue per customer. We distinguish B2B, B2C and creator tools, and use comparable revenue when available. These are model assumptions, not measured sales.
Indicative range $1.7k–$27.1k
Share of monthly visits
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Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in thevineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, andincreasing in grape losses rates. The objective of this research is to investigate how the variation inworkers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.
Updated: 09/01/2026
Share calculated from Meta's reported reach breakdown. Spend by country is not published.
Traffic, revenue and acquisition: the data behind the product.
Catalog updated on
Hiring workers under seasonal recruiting contracts causes significant variation of workers skills in thevineyards. This leads to inconsistent workers performance, reduction in harvesting efficiency, andincreasing in grape losses rates. The objective of this research is to investigate how the variation inworkers experience could impact vineyard harvesting productivity and operational cost. The complexity of the problem means that it is difficult to analyze the system parameters and their relationships using individual analytical model. Hence, a hybrid model integrating discrete event simulation (DES) and agent based modeling (ABM) is developed and applied on a vineyard to achieve research objective. DES models harvesting operation and simulates process performance, while ABM addresses the seasonal workers heterogeneous characteristics, particularly experience variations and disparity of working days in the vineyard. The model is used to evaluate two seasonal recruiting policies against vineyard productivity, grape losses quantities, and total operational cost.
Official website anylogic.comAnyLogic receives an estimated 119,010 visits in September 2026.
This represents a change of +6.1% compared with August 2026 (112,209 visits).
These are estimated visits, not unique users or paying customers. Traffic growth does not establish sales growth.
| Month | Visits |
|---|---|
| 135,684 | |
| 112,209 | |
| 119,010 |
AnyLogic has an estimated monthly revenue of $6,780. This is an estimate, not company-reported revenue or payment-verified MRR.The model uses available product, traffic and comparable SaaS signals; its assumptions remain uncertain.
The leading countries for visits to AnyLogic are: United States (27.9%), Russia (11.8%), India (6.6%). These shares describe website traffic, not the location of paying customers.
The recorded channels include: Direct (45.2%), Search (38.8%), Referrals (7.9%).Coverage may be partial: a missing value does not establish that a channel is unused. These visits do not measure conversions or advertising returns.
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Explore acquisition dataThe selection of SaaS to compare includes Atipica.Similarity is assessed from available descriptions, categories and use cases. It does not guarantee equivalent features or pricing.
Answers based on the data available in this profile.
AnyLogic receives an estimated 119,010 visits in September 2026. These are estimated visits, not unique users or paying customers. Traffic growth does not establish sales growth.
AnyLogic has an estimated monthly revenue of $6,780. This is an estimate, not company-reported revenue or payment-verified MRR.The model uses available product, traffic and comparable SaaS signals; its assumptions remain uncertain.
This represents a change of +6.1% compared with August 2026 (112,209 visits). These are estimated visits, not unique users or paying customers. Traffic growth does not establish sales growth.
The leading countries for visits to AnyLogic are: United States (27.9%), Russia (11.8%), India (6.6%). These shares describe website traffic, not the location of paying customers.
The selection of SaaS to compare includes Atipica.Similarity is assessed from available descriptions, categories and use cases. It does not guarantee equivalent features or pricing.
The catalog data was last updated on October 18, 2026.The latest available traffic month is September 2026.Traffic periods, collection dates and revenue source dates can differ.