Blue Yonder Group, Inc.

Vishal Shinde

Tushar Shekhar

Narayan Nandeda

Rashid Puthiyapurayil

Phani Bulusu

Vidhi Chugh

A system and method efficiently solve subsequent runs of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem. Embodiments include modeling a supply chain planning problem as a multi-objective hierarchal linear programming problem having first Run1 objectives and based, at least in part, on supply chain input data, receiving one or more changes to the supply chain input data, modeling a second supply chain planning problem based, at least in part, on the one or more changes to the supply chain input data, and modeled as a second multi-objective hierarchal linear programming problem having Run2 objectives, generating a superset matrix, and generating a supply chain plan comprising the one or more changes to the supply chain input data by converting a solution of the second supply chain planning problem.

Efficiently solving multi-objective hierarchical linear programming problems

A system and method for efficiently determining the fair-share bands of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem include a processor and memory and are configured to model a supply chain planning problem as a multi-objective hierarchal linear programming problem, assign weights at each band of a fixed number of at least two bands, determine a direction of improved band values from a value of a Key Process Indicator (KPI) calculated from an expected demand and short quantities, wherein the expected demand and short quantities are calculated from the multi-objective hierarchical linear programming problem using a sample generated by Gibbs sampling of a conditional Gaussian Bayesian Network, and generate a supply chain plan.

Fair share band optimization using Gaussian Bayesian network

A system and method of solving supply chain planning problems modeled as multi-objective hierarchical linear programming problems receive supply chain input data for a supply chain planning problem, solve a first multi-objective hierarchical linear programming problem, store a cumulative list of bound changes, receive changes to the supply chain input data, model a second supply chain planning problem as a second multi-objective hierarchal linear programming problem based, at least in part, on the one or more changes to the supply chain input data, derive an intermediate objective based, at least in part, on the cumulative list of bound change, and solve the second multi-objective hierarchical linear programming problem, using the basis of the solved intermediate objective.

Systems and methods for solving multi-objective hierarchical linear programming problems using previously-solved solution information

Fair share band optimization using Gaussian Bayesian Network

A system and method are disclosed for solving subsequent runs of a supply chain planning problem modeled as a multi-objective hierarchical linear programming problem. Embodiments further include receiving supply chain input data for a supply chain planning problem, modeling the supply chain planning problem as a multi-objective hierarchal linear programming problem having a first objective and at least one additional objective, solving a base run of the multi-objective hierarchical linear programming problem for the first objective and the at least one additional objective, generating a base plan by converting a solution of the base run of the multi-objective hierarchical linear programming problem, receiving one or more changes to the supply chain input data, identifying primal feasibility change of the one or more changes that affect only primal feasibility, and identifying dual feasibility changes of the one or more changes that affect only dual feasibility.

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Bengaluru, India

Vishal Shinde