Abstract: State of the art vehicle route optimization approaches fail to handle growing customer needs. The disclosure herein generally relates to route planning, and, more particularly, to a method and system for vehicle route optimization. The system obtains information on (i) a network of customers, wherein the network of customers comprises nodes corresponding to a plurality of customer locations and a depot, and edges representing connections between the customer locations, (ii) distance between each pair of customer locations among the plurality of customer locations, (iii) one or more orders received from one or more of the customers, (iv) service start time, (v) service end time, and (vi) servicing time, as input data, and then by processing the input data, performs vehicle route optimization at different levels, till an optimized route plan is obtained.

Abstract: The disclosure herein generally relates to inventory management, and, more particularly, to a method and system for adaptive inventory replenishment. The system collects real-time information on product-inflow, product-outflow, and damaged goods, as input, and processes the inputs to learn a product-replenishment pattern. Further, a replenishment policy that matches the learnt product-replenishment pattern is selected, and based on the selected replenishment policy, at least one product-replenishment recommendation for the collected real-time information is generated.

Abstract: The disclosure herein generally relates to inventory management, and, more particularly, to a method and system for adaptive inventory replenishment. The system collects real-time information on product-inflow, product-outflow, and damaged goods, as input, and processes the inputs to learn a product-replenishment pattern. Further, a replenishment policy that matches the learnt product-replenishment pattern is selected, and based on the selected replenishment policy, at least one product-replenishment recommendation for the collected real-time information is generated.

Abstract: This disclosure relates generally to a system and method to predict flight delay. Moreover, the embodiments herein further provide the system and method to predict timings of an airline in real time by considering historical operations (arrival and departure) data, historical airport data (captured at the time of arrival and departure) including congestion information, and weather data of the airport. The flight delays involves prediction of arrival and departure times of flight. Herein, the method categorizes input data related to an airline history, airline network, airport data and various airline reference data. Further, the method analyses the cause of delay which may be due to maintenance issues with the aircraft, fueling, weather, congestion in air traffic, and security issues etc. The system and method computes the flight delay due to multiple airline operations and different input datasets using stochastic approximation approach.