Abstract: A method and system for optimizing performance of Genetic Algorithm (GA) in solving scheduling problem is disclosed. The method includes receiving input constraints associated with supply and demand sides, for scheduling problem. The method include initializing set of schedules using initializer that sets initial set of solutions for GA to start optimization. The method may include generating parent population for GA. The method may include creating child population via evolution using current probabilistic parameters including crossover and mutation operators. The method may include utilizing a Multi-Level Hierarchical Grouping (MLHG) to de-duplicate child population. The method includes determining a new population from a total population including the parent population and the child population, using custom multi-objective sorting technique. The method may further include updating probabilistic parameters of the GA during runtime using runtime adapter, when pre-determined iterations unattained.

Abstract: The present invention discloses a system for scheduling jobs within a manufacturing environment, integrating a plurality of sensors to capture operational parameters. A processing unit, linked to the plurality of sensors, analyzes datasets to determine the operational parameters. A machine learning module coupled to the processing unit, enhances a scheduling algorithm using the operational parameters. This machine learning module includes a first predictor for estimating job processing times and forecasting operating conditions based on these parameters. A formulator adjusts the scheduling algorithm using the forecasted operating conditions for distinct time intervals. Additionally, a second predictor forecasts subsequent operating conditions based on the modified scheduling algorithm and initial forecasts. The system optimizes job scheduling, enhancing efficiency and productivity within the manufacturing environment.

Abstract: A method and system for optimal scheduling of nursing services in a hospital is provided herein. The method and system comprises forecasting demand for the nursing services for a time interval based on historical data and external data. The method and system further comprises extracting nurses' availability data for the time interval. The method and system comprises aggregating constraints applicable to the nursing services. The method and system also comprises computing a schedule of nurses based on the constraints using at least an optimization model.