Abstract: A system may include a memory and a processor in communication therewith. The processor may be configured to perform operations including obtaining an egocentric network with a center node for an entity and receiving data about the entity. The operations may include predicting, with the data, a node array of the egocentric network including a first node to remain, a second node to disappear, and a third node to join the network. The operations may include prognosticating, with the data, a connection array of the egocentric network including a first link connecting the first node to the center node, a second link disappearing from the egocentric network, and a third link connecting the third node to the center node. The operations may include updating the egocentric network to reflect the node and connection arrays, generating an output based on the egocentric network, and displaying the output to a user.

Abstract: A method for planning under uncertainty is disclosed. The method includes steps of processing a stochastic programming formulation based on forecast values of at least one of product and service configurations, and determining a resource requirements plan for one or more planning periods in a non-deterministic bill of resources of at least two levels.

Abstract: Aspects of the disclosure provide for a system. In some examples, the system comprises a first sensor associated with first and second attributes and a second sensor associated with the second attribute and a third attribute. The system further comprises a processor communicatively coupled to the first and second sensors. The processor is configured to identify a number of activations of the first sensor within a time window, identify a number of activations of the second sensor within the time window, and construct a sensor vector identifying a number of first attribute activations based on the first number of activations of the first sensor, a number of second attribute activations based on the number of activations of the first sensor and the number of activations of the second sensor, and a number of third attribute activations based on the number of activations of the second sensor.

Abstract: A method for planning under uncertainty is disclosed. The method includes steps of processing a stochastic programming formulation based on forecast values of at least one of product and service configurations, and determining a resource requirements plan for one or more planning periods in a non-deterministic bill of resources of at least two levels.

Abstract: A computer method for providing optimization for design processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable design variable, f(x,b) subject to constraints on the dependent uncontrollable design variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its design variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its design variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent design variable y.

Abstract: A computer method for providing optimization for business processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable business variable, f(x,b) subject to constraints on the dependent uncontrollable business variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its business variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its business variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent business variable y.

Abstract: A computer method for providing optimization for manufacturing processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable manufacturing variable, f(x,b) subject to constraints on the dependent uncontrollable manufacturing variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its manufacturing variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its manufacturing variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent manufacturing variable y.

Abstract: A computer method for providing optimization for manufacturing processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1,. . . bm}, and y comprises a dependent uncontrollable manufacturing variable, f(x,b) subject to constraints on the dependent uncontrollable manufacturing variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its manufacturing variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its manufacturing variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent manufacturing variable y.

Abstract: A computer method for providing optimization for design processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable design variable, f(x,b) subject to constraints on the dependent uncontrollable design variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its design variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its design variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent design variable y.