Abstract: This disclosure relates generally to a method and system for optimal power allocation in large-scale electric vehicle (EV) charging network having cluster topology. Currently, methods based on mathematical and constraint programming provide solutions for optimal power allocation but are computationally slow on large problem sizes. Reinforcement learning (RL) based learning-to-optimize (L2O) are also used for solving, but the solutions these methods provide impact the solution quality. The present disclosure provides mathematical programming-based method for optimizing power allocation decisions. The power allocation problem for the EV charging network is modelled with constraints on energy demand, power supply capacity, available network capacity, customer types and their priority level. Further the optimization problem is formulated to maximize the power flow to prime customers in the EV charging network. The problem is formulated using a mixed-integer linear program.

Abstract: The embodiments of the present disclosure herein address unresolved problems of cyclic dependency between cost of power procurement and routing of one or more electric vehicles (EVs) of a logistic enterprise. Embodiments herein provide a method and system for an optimization framework to overcome a cyclic dependency between cost of power procurement and routing of electric vehicles (EVs) of a logistic enterprise. The optimization framework comprises two independent routines, coupled through an exchange of parameter values. First routine optimizes routing cost of the EVs to satisfy delivery constraints by assuming that the average EV charging cost is known. This is given as input to a second routine that optimizes electricity procurement cost from different energy sources by assuming that the EVs routes are fixed. The output from the second routine is then given back as input to first routine, and the procedure iterates till a stopping criteria is reached.

Abstract: A computerized method for evaluating agent-call-logging, in a contact center, is provided herein. The method includes operating an evaluating agent-call-logging module. The operating of an evaluating agent-call-logging module includes: (i) receiving a recorded interaction between an agent and a customer, stored in the database of recorded interactions and a corresponding agent-call-logging, having ‘n’ sentences, stored in the database of call loggings; (ii) operating a speech-to-text algorithm on the received recorded interaction to yield a summarized text thereof. The yielded summarized text is comprised of sentences expressed by the customer; and (iii) operating a semantic comparison unit, to compare each sentence in the yielded summarized text with a corresponding sentence in the agent-call-logging, to yield a Sentence Similarity Score (SSS) for each sentence of the agent-call-logging.

Abstract: A computerized method for evaluating agent-call-logging, in a contact center, is provided herein. The method includes operating an evaluating agent-call-logging module. The operating of an evaluating agent-call-logging module includes: (i) receiving a recorded interaction between an agent and a customer, stored in the database of recorded interactions and a corresponding agent-call-logging, having ‘n’ sentences, stored in the database of call loggings; (ii) operating a speech-to-text algorithm on the received recorded interaction to yield a summarized text thereof. The yielded summarized text is comprised of sentences expressed by the customer; and (iii) operating a semantic comparison unit, to compare each sentence in the yielded summarized text with a corresponding sentence in the agent-call-logging, to yield a Sentence Similarity Score (SSS) for each sentence of the agent-call-logging.

Abstract: The present systems and methods are configured to extract appliance instructions and settings from recipes to control connected appliances using the extracted instructions, wherein the system automates the appliance parameters from recipe instructions.