Abstract: In one example, a device to calculate a relative set of weighted indices for a set of objectives includes an input device that receives a prioritized list of the set of objectives. A user interface module creates a square matrix of the set of objectives and their subjective relative intensity of importance includes a module to query for subjective intensity of importance between respective objectives in the prioritized list of objectives. The user interface module only presents as options select subjective intensity of importance which preserve a transitivity property of the prioritized list of objectives. A compute module calculates a principle eigenvector of the square matrix to thereby create the relative set of weighted indices.

Abstract: Examples disclosed herein relate to worker task assignment based on correlation and capacity information. For example, a processor may create task assignments for workers based on a comparison of task attributes to worker attributes. The number of workers assigned to a task may be based on the availability of workers assigned. The processor may output information related to the task assignments.

Abstract: Portfolio generation based on a dynamic allocation of resources is disclosed. One example is a system including a data processor, a resource allocator, and a portfolio planner. The data processor accesses resource allocation data including a plurality of projects, a portfolio shaping preference, a plurality of constraints and a plurality of objectives, and activates, based on the portfolio shaping preference, a sub-plurality of the plurality of constraints and a sub-plurality of the plurality of objectives. The resource allocator generates at least one project portfolio based on the sub-plurality of constraints and the sub-plurality of objectives, wherein the at least one project portfolio includes a sub-plurality of the plurality of projects, and is based on a dynamic allocation of the resource allocation data. The portfolio planner schedules the sub-plurality of the plurality of projects, and provides the at least one project portfolio to a computing device via a graphical user interface.

Abstract: A method includes receiving flight leg data and dispatcher position data for a planning horizon and identifying cycle flight legs and extraordinary flight legs based on the flight leg data. The method includes allocating each cycle flight leg to at least one dispatcher position while minimizing workload deviations between the dispatcher positions. The method includes allocating, after allocating each cycle flight leg, each extraordinary flight leg to at least one dispatcher position while minimizing workload deviations between the dispatcher positions.

Abstract: A method includes receiving flight leg data and dispatcher position data for a planning horizon and identifying cycle flight legs and extraordinary flight legs based on the flight leg data. The method includes allocating each cycle flight leg to at least one dispatcher position while minimizing workload deviations between the dispatcher positions. The method includes allocating, after allocating each cycle flight leg, each extraordinary flight leg to at least one dispatcher position while minimizing workload deviations between the dispatcher positions.

Abstract: A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.

Abstract: A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.