Abstract: Generally, in complex systems such as business organizations and supply chain networks, decisions making and control are challenging due to dynamic environment thereof. The embodiments herein provide method and system that uses reinforcement learning (RL) for exploring policies and deciding control actions, and actor-based modelling and simulation for performing accurate long-term rollouts of the policies, in order to optimize operation of the complex systems. In an embodiment, a method for actor based simulation of complex system includes modeling, actors of the complex system using an actor abstraction, where actors includes RL agents embodied in each subsystem of the complex system, and executes micro-level interactions amongst the subsystems. An emergent macro-behavior of the complex system is simulated by the using the RL agents based on the micro-level interactions. Optimal decisions pertaining to actions of the RL agents are learnt based on the emergent macro-behavior of the complex subsystem.

Abstract: This disclosure provides a system and method for concurrent dynamic optimization of a replenishment decision in a networked node environment by receiving at least one current state attribute associated with the networked node environment at a predetermined time interval; obtaining an optimized entity actionable matrix by mapping the at least one of current state attribute and an associated actionable entity; computing a predictive replenishment matrix for an impending cycle of the networked node environment based on the optimized entity actionable matrix; identifying a reward function for the predictive replenishment matrix; and determining the replenishment suggestive actionable element based on the reward function. The reward function includes a computation of overall cost with regard to a realized revenue associated with aggregation of the plurality of nodes.

Abstract: This disclosure provides a system and method for concurrent dynamic optimization of a replenishment decision in a networked node environment by receiving at least one current state attribute associated with the networked node environment at a predetermined time interval; obtaining an optimized entity actionable matrix by mapping the at least one of current state attribute and an associated actionable entity; computing a predictive replenishment matrix for an impending cycle of the networked node environment based on the optimized entity actionable matrix; identifying a reward function for the predictive replenishment matrix; and determining the replenishment suggestive actionable element based on the reward function. The reward function includes a computation of overall cost with regard to a realized revenue associated with aggregation of the plurality of nodes.