Abstract:
Disclosed is method and system for continuously re-generating reactive on-line train schedules for trains running in a large railway network. Railway network partitioned based on user configuration, into first type comprising trunk line and feeder line sub-networks, and second type comprising supervisory dispatch control territories. Sense and respond cycle is continuously executed on multi-processor computing environment, senses dynamic data from field about train movements, and other changes from users. For each first type sub-network, degree of deviation is computed from incumbent plans and congestion in sub-networks. Using degree of deviation and congestion, trains are rerouted and suitable scheduling methods are chosen for each sub-network and executed in parallel and first level train schedules are sent to second level train schedulers working on second type sub-networks which in parallel identify and resolve conflicts among first level train schedules. Second level train schedules are collated to generate reactive on-line network train schedule.
Abstract:
Disclosed is a system and method for parallelizing grid search technique facilitating determination of PK-PD parameters. The method may comprise determining number of grids. The method may further comprise creating grid points based upon the number of grids (N) and a number of parameters (p). The method may further comprise distributing the grid points amongst number of threads. The method may further comprise evaluating an objective function value corresponding to each grid point in order to compute an objective function value associated with each of the grid points. Further, the method may comprise identifying a grid point having minimum objective function value. The grid point having the least objective function value may indicate the estimated initial PK-PD parameters.
Abstract:
Systems and methods of scheduling tasks and managing computing resource allocation in a closed loop control system is provided. The system uses historical run-time statistics that includes expected run-time (μ) and standard-deviation (σ) in run-times, of the tasks. The run-time statistics are used by the system to first predictively allocate and then to order the execution of the tasks in order to minimize the make-span. The schedule predicted is a queue of tasks to be executed on each computing resource ordered by a function of the expected run-time (μ) and standard-deviation (σ). Reactive scheduling involves periodically probing the progress and reacting to imbalances in progress across computing resources by switching tasks between lagging and leading computing resources.