Cash level optimization with autocalibration

A system for optimization comprising:

• at least one cashpoint node that is in communication with at least one server node through a computer network;
• wherein each or the at least one server node is operated by or on behalf of a banking institution;
• at least one computing device node in communication with the at least one server node through the computer network;
• wherein the at least one computing device node executes a forecasting engine configured to create a forecast dataset of a set of expected transactions with each of the at least one cashpoint nodes in communication with the at least one server node;
• wherein the forecast engine utilizes historical and current data from the cashpoint node to generate the forecast dataset;
• wherein, as part of a feedback loop, the at least one computing device node executes a calibration engine that, in part based on the forecast dataset, is configured to create a number of candidate calibration datasets;
• wherein the at least one computing device node executes an evaluator engine configured to evaluate the quality of calibration datasets and provide an evaluation dataset;
• wherein, as a finalization of a feedback loop, the at least one computing device node executes a calibration engine to create an optimized calibration dataset selected from the candidate calibration datasets based on the evaluation dataset of the candidate calibration datasets;
• wherein the at least one computing device node executes an optimizer engine configured to use the optimized calibration dataset and generates a plan dataset for the at least one cashpoint node;
• and
• wherein the at least one server node or the at least one cashpoint node can send a service order based on the plan dataset, and the order triggers a servicing of the at least one cashpoint node.

A method of optimization of a calibration for a cash level optimizer device comprising:

• receiving a dataset of cash level history, and current cash levels for a cash device with a computing device connected to the cash device via a computer network;
• preparing a forecast dataset comprising set of expected transactions, based on the cash level history and current cash levels;
• optimizing a calibration for the optimizer engine consisting of a set of variable parameters based on a prepared forecast dataset, and a set of historical transactions with the computing device;
• generating a plan based on the optimized calibration, the current cash levels of the cash device and the forecast dataset, and providing the plan to a banking institution or cash-in-transit company to allow for optimal servicing of the cash device;
• wherein the calibration optimization is performed using a domain specific metaheuristic search algorithm;
• wherein the metaheuristic search algorithm iteratively creates datasets consisting of candidate calibrations whose parameters are restricted by one or more constraints received from a constraint system device;
• wherein each dataset consisting of the candidate calibrations is evaluated using an evaluation engine which produces an evaluation dataset according to a pre-defined evaluation measure that determines the quality of each candidate calibration;
• wherein the optimized calibration is determined as the calibration candidate with the best quality upon meeting a termination criterium, optionally storing one or more values of the parameters that are part of the calibration datasets in a neural network, preferably a feedforward neural network;
• wherein the metaheuristic search algorithm takes into account plurality of parameters that are part of a calibration containing one or more of the following parameters, a stock level threshold of the cash device, a buffer factor of the cash device, a minimum balance for each of the denominations of the cash device, and cost parameters to service the cash device, and wherein the set of services costs are retrieved from a third party service by the computing device.