Abstract: A computer includes a processor and a memory device. The computer is configured to a) receive decision-making criteria from at least one of at least a portion of a plurality of agents associated with a plurality of agent devices, the memory device, and a user, b) generate valid decision combinations using at least a portion of received decision-making criteria, c) transmit, to the plurality of agents, valid decision combinations, d) receive, from a deciding agent, a decision, and e) constrain, using the received decision, valid decision combinations. The computer is configured to f) return to c) until determining that no more decisions can be received. The computer is configured to g) transmit a final decision set to the plurality of agents upon determining that no more decisions can be received. The final decision set represents a complete combination of decisions including at least a portion of received decisions.

Abstract: A computer-implemented method for estimating maintenance task durations is provided. The method is implemented by at least one computing system including at least one processor and at least one memory device coupled to the processor. The method includes receiving a query including a first query maintenance task and a plurality of query factors, and identifying an initial baseline task duration based at least in part on the first query maintenance task. The initial baseline task duration includes a plurality of baseline task factors. The method further includes identifying at least one adjustment factor based at least in part on the differences between the plurality of query factors and the plurality of baseline task factors, calculating a maintenance task duration estimate based at least in part on the initial baseline task duration and the adjustment factors, and planning execution of the first query maintenance task based on maintenance task duration estimate.

Abstract: A method and system for performing model-based multi-objective asset optimization and decision-making is provided. The method includes building at least two predictive models for an asset. The building includes categorizing operational historical data via at least one of: controllable variables, uncontrollable variables, output objectives, and constraints. The building also includes selecting at least two output objectives or constraints, and identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints. The method also includes validating each predictive model and performing multi-objective optimization using the predictive models. The multi-objective optimization includes specifying search constraints and applying a multi-objective optimization algorithm. The method further includes generating a Pareto Frontier, and selecting a Pareto optimal input-output vector.