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.

Abstract: This disclosure relates to optimizing an operation of an equipment by a neural network based optimizer is provided. The method include receiving, information associated with at least one equipment instance (j) as an input at a predefined sequence of timestamps; training, a plurality of simulation models for each equipment instance to obtain a function (fj); processing, the external input parameters (et) to obtain a fixed-dimensional vector and passed as an input to obtain an vector (it); generating, a modified (it) from the output vector (it) based on a domain constraint value; computing, a reward (rt) based on (i) the function (fj), (ii) the modified (it), (iii) the external input parameters (et), and (iv) a reward function (Rj); and iteratively performing the steps of processing, generating, and computing reward (rt) for a series of subsequent equipment instances after expiry of the predefined sequence of timestamps associated with a first equipment instance.