The Boeing Company

Rockwell Electronic Commerce Technologies, LLC

Sudhakar Y Reddy

Kenneth W Fertig

Ole J Mengshoel

Ken Fertig

Philip L Stubblefield

Bruce E Riggins

Bruce Riggins

Presented are methods for determining computational flow for a data-dependent constraint network that are especially useful for modeling and trading off alternative configurations in a single computational environment, during design analysis and optimization of an engineering system. These methods leverage a preexisting constraint management system that uses a bipartite graph of variables and constraints to model an engineering system and employ logic formulae based world sets to determine the applicability of the constraints to different system configurations. These methods ensure that a data-dependent constraint network is in a consistent state, and are the essential foundation for other techniques that rely on a consistent constraint network to produce computational plans for propagating values and uncertainties through the constraint network during tradeoff analyses.

Maintaining computational flow in a data-dependent constraint network

Presented are rapid and robust techniques for estimating the uncertainty in product attributes (performance, cost, etc.) during the multi-disciplinary design and analysis phase of the product life-cycle. The techniques leverage the capabilities of a preexisting constraint management system that may be used to calculate performance and cost metrics of an engineering system as a function of the design structure and operational scenarios. The techniques are particularly useful when the constraint management system is used to automate the reverse computation required when the analyst specifies cost, schedule, or performance targets using approaches such as cost as independent variable. Disclosed techniques may also be applied to constraint management systems that include compound-valued variables.

System and method for rapid and robust uncertainty management during multidisciplinary analysis

A method of determining a conditional computational plan for a data dependent constraint network represented by a bipartite graph containing input variable nodes, output variable nodes, and relation nodes, may include specifying, using a variable node specifier, at least one output variable node for which a plan is desired. The method may further include determining, using a plan determiner, a plan from the input variable nodes to the output variable node using a backward chaining search of the bipartite graph.

System and method for computational planning in a data-dependent constraint management system

A method for simplifying a complex well-formed formula (WFF) may include receiving, in a formula processor, an input WFF and converting the input WFF into an initial bit array. The method may further include simplifying the initial bit array into a simplified bit array by removing predicates from the initial bit array not necessary to represent the input WFF. The method may include converting the simplified bit array into a return WFF in either a conjunctive normal form or a disjunctive normal form.

System and method for rapid management of logic formulas

In one embodiment, a method for troubleshooting a fault to determine a root cause of the fault. A Bayesian network model is created based on information obtained from a Fault Isolation Manual (FIM), where the FIM provides tests to be performed in troubleshooting the fault to determine a root cause of the fault. Heuristics are used to determine a structure and conditional probabilities for the Bayesian network. A plurality of test costs inherent in the FIM are imputed by first generating a plurality of constraints between the cost of each test and fault probabilities that hold for all fault scenarios. A linear programming algorithm is used to solve the plurality of constraints, and to construct a tuned Bayesian network model. The tuned Bayesian network model is used to iteratively rank likely faults according to their probabilities given accumulating test evidence, and to rank pending tests according to their value.

Method and system using linear programming for estimating test costs for bayesian diagnostic models

Multi-Objective Optimization (MOO) is integrated with a Constraint Management System (CMS) so as to rapidly and flexibly search large design spaces and focus on 'interesting' designs as determined by user-specified criteria. A method embeds a trade space and its trade-off envelope within the same CMS network used to calculate the values of the points in the trade space itself. The method supports automatic variation of the resulting trade-off analyses in response to variations in upstream parameters, assumptions, and/or requirements. When feasible, the CMS can back-solve for values of upstream parameters so that selected attributes of the trade-off envelope achieve user-specified values. The coupled use of a CMS with multi-objective optimization avoids having to generate a large set of permutations of inputs and factors and then restricting the analysis to those having desired outputs or of having to manually 'reformulate' the equation set so that it solves in the desired direction.

Multi-objective optimization within a constraint management system

A method and apparatus are provided for dynamically reassigning agents among call types in a call distribution system having a plurality of agents assigned to a plurality of call types. The method includes the steps of detecting a deficiency in agent responsibility assigned to a call type of the plurality of call types based upon a measured service parameter and corresponding target service parameter and determining an agent allocation among the plurality of call types best suited to correct the deficiency.

Contact center autopilot algorithms

An apparatus is provided for reallocating agent responsibility for servicing a plurality of call types received by an automatic call distributor. The apparatus includes an autopilot adapted to form a plurality of proposed alternative fractional call assignments among the plurality of call types for an agent of the automatic call distributor, a performance predictor adapted to calculate a service parameter for each of the plurality of call types for each of the formed alternate agent call assignments and a comparator adapted to automatically select an agent assignment of the plurality of formed alternative agent assignments based upon the calculated service parameter.

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Santa Clara, CA, United States of America

Sudhakar Y Reddy