Abstract:
The disclosure relates generally to methods and systems for predicting water quality of a river having a varying river ecosystem. Due to multiple and diverse factors, understanding and estimating the water quality of the river stream (river itself) is extremely and technically challenging. The present disclosure discloses a development of a river digital twin model utilizing a multi-modeling approach to comprehensively model the river and its varying ecosystems. The agents encompass entities that directly or indirectly introduce effluents or withdraw water from the river. Agents and their interactions are defined using a combination of behavior rules, correlations, and physics principles, creating the digital twin model that closely mimics the real river system. Physics-based equations are also employed in the present disclosure to capture the dynamics of the river, while relationships between different agents are established.
Abstract:
Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of pandemic related uncertainties which exists along multiple dimensions effected by authorities. This uncertainty plays out in a non-uniform manner thus leading to highly heterogeneous evolution of pandemic. Present disclosure provides digital twin based systems and methods for business continuity plan and safe return to workplace wherein a simulation-based data-driven evidence-backed approach is implemented that captures details pertaining to virus, individualistic characteristics of employees and their dependents, offices, locations of the employees and offices, and various pandemic control measures that are in effect and need to be explored using a hybrid modelling and simulation approach that combines fine-grained actor/agent model and coarse-grained stock-and-flow model. The present disclosure further leverages past macro-level data pertaining to pandemic evolution of relevant cities, states, and countries to make available information amenable for collective analysis and infection prediction.
Abstract:
System and method for modelling and simulating a decision making process of an enterprise is disclosed. Data corresponding to a plurality of units in the enterprise is received. For each unit, a unit configuration may be determined. The unit configuration comprises goals of the unit, a set of internal properties of the unit, a set of functions of the unit, events to be handled by the unit, and a composition structure of one or more units to interact with other units of the plurality of units participating to perform a task. Further, a plurality of tuples is specified for the unit configuration. The plurality of tuples of the unit configuration is translated into an executable programming language. Subsequently, the unit configuration is simulated using the executable programming language to analyze decision making of the enterprise corresponding to the plurality of units for the unit configuration.
Abstract:
This disclosure relates generally to the method and system for dynamically optimizing the operations of logistics management system. The existing methods for optimizing the operational processes in the sorting terminals are not dynamic as the sorting terminal operations are largely manual and experience driven. The proposed method and system describe an actor-based representation of simulatable digital twin of the sorting terminal that enables in-silico quantitative exploration of design space to help human experts arrive at the right decisions related to the logistics management system. The proposed method and system initially construct a high-fidelity simulatable digital twin of the sorting terminal, validate it, set it up with real data, and simulate various adaptation and design alternatives to understand their impacts on the key performance indicator values.