Relaxation and Decomposition Methods for Mixed Integer Nonlinear Programming (International Series of Numerical Mathematics)
| Hi-index | 0.00 |
Ford Motor Company used operations research methodology to aid in determining the best sourcing footprint for its $1.5 billion Automotive Components Holdings, LLC (ACH) Interiors business, saving approximately $40 million in upfront investment over the previously preferred alternative. This extensive undertaking required a complete reengineering of the supply footprint of 42 high-volume product lines over 26 major manufacturing processes and more than 50 potential supplier sites. Under extreme time constraints (two months), we developed a decision-support tool and a novel approach to solve the underlying large-scale mixed-integer nonlinear program. We reformulated a complex real-life problem into a manageable model that provided practical insights in a timely fashion. The proposed algorithms scale well and account for nonlinearities arising from supplier facility cost structures. The new tool provided a state-of-the-art, data-driven, quantitative basis for sourcing decisions in an area of strategic importance to the company and enabled Ford to make faster and better decisions on how to restructure its ACH Interiors business.