A system for incremental solution of the shift design and assignment problem comprises an interface configured to receive an incremental change and an existing schedule. The system comprises a processor to determine whether labor demand has changed; in response to labor demand having been changed, generate an updated set of shift candidates; determine a new cost function; restart a solver using the updated set of shift candidates, the existing schedule, the incremental change, and the new cost function, wherein the solver comprises a mixed integer programming (MIP) solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates.

A method for execution by a storage network begins by issuing a decode threshold number of read requests for a set of encoded data slices to a plurality of storage units of a set of storage units and continues by determining whether less than a decode threshold number of read requests has been received in a time window. The method continues by identifying one or more encoded data slices encoded data slices associated with read requests of the decode threshold number of read requests that have not been received and for an encoded data slice of the one or more encoded data slices, issuing a priority read request to a storage unit storing a copy of the encoded data slice. The method then continues by receiving a response from the storage unit storing the copy of the encoded data, where the storage unit storing the copy of the encoded data slice is adapted to delay one or more maintenance tasks in response to the priority read request.

A system and method for determining optimal pathways to a predetermined goal, including generating a unique code for an event within a plurality of data records, where the same unique code will be generated for similar events; constructing at least one pathway using the unique codes based on the events, wherein the at least one pathway includes at least one segment; determining a goal, wherein the goal is the end of the at least one pathway; comparing the at least one pathway to other pathways sharing the determined goal; and optimizing the pathway for achievement of the determined goal.

Systems and methods are provided for solving workforce management scheduling optimization decomposing and iteratively. Execution of a master problem can occur to select a best schedule among generated schedules for each employee of a group of employees while enforcing one or more global constraints. The generated schedule can be determined by execution of one or more sub-problems, each of the one or more sub-problems can be enforced work rules for the respective employee through the use of reduced cost for the employee. A flexible objective function can be executed to account for workforce management schedule wide metrics, including fitness of schedule to demand, fairness among the group of employees, schedule preferences and others.

A device may receive a request for a schedule and scheduling constraints to utilize when generating the schedule, and may process, based on the request, a first portion of the scheduling constraints and first optimization variables, with a first optimization solver model, to generate capacity data for the schedule. The device may process the capacity data, a second portion of the scheduling constraints, and second optimization variables, with a second optimization solver model, to generate shift assignment data for the schedule, and may process the shift assignment data, a third portion of the scheduling constraints, and third optimization variables, with a third optimization solver model, to generate skill and task assignment data for the schedule. The device may generate the schedule based on the capacity data, the shift assignment data, and the skill and task assignment data, and may perform one or more actions based on the schedule.

According to some embodiments, data may be received indicative of a plurality of insurance claims along with an indication of an appropriate claim segment classification for each insurance claim. A first claim handler may then be automatically selected for a first insurance claim based at least in part on: (i) a first segment classification associated with the first insurance claim, (ii) numbers of other insurance claims currently assigned to claim handlers, and (iii) load factors associated with claim handlers. An indication of the selected first claim handler may then be transmitted.

Systems and methods are disclosed for scheduling a workforce. In one embodiment, the method comprises receiving a shift activity template; receiving an association between the shift activity template and at least one worker; and scheduling a plurality of schedulable objects. The scheduling is performed in accordance with a workload forecast and schedule constraints. Each of the schedulable objects is based on the shift activity template. The shift activity template describes a worker activity performed during a shift. The template has range of start times and a variable length for the activity. The activity is associated with a queue.

A crew planning system includes a demand forecasting module and an optimization module. The system forecasts anticipated reserve demand levels, and determines suitable reserve staffing approaches to meet anticipated reserve demand. The forecasting is based upon probabilistic distribution models which take into account variability associated with reserve demand for a particular day. Via use of the crew planning system, reserve staffing expenses may be reduced and/or reserve demand may be met with a higher degree of probability.

An approach for optimizing the selection of team members on a project team. The approach retrieves data associated with prospective members of a project team. The approach generates team member personality scores based on a machine learning model. The approach generates a team compatibility score based on a portion of the prospective team member personality scores. The approach calculates a predicted project success score based on the team compatibility score. The approach assigns prospective team members to the team based on maximizing the predicted project success score.

An approach for predicting team dynamics based on changing circumstances. The approach retrieves profiles associated with a group of first team members of a project team. The approach selects a virtual reality (VR) scenario to interact with the group of first team members. The approach executes the VR scenario based on the profiles. The approach receives VR input from an interacting team member interacting with the VR scenario. The approach predicts responses of the group of first team members to the VR input. The approach analyzes the responses and provides the interacting team member a ranked list of scenario outcomes.