Response surface methodology (RSM) is used for optimality analysis of the cost parameters in mixed integer linear programming. This optimality analysis goes beyond traditional sensitivity and parametric analysis in allowing investigation of the optimal objective function value response over pre-specified ranges on multiple problem parameters. Design of experiments and least squares regression are used to indicate which cost parameters have the greatest impact on the optimal objective function value total cost-and to approximate the optimal total cost surface over the specified ranges on the parameters. The mixed integer linear programming problems of interest are the large-scale problems in supply chain optimization also known as facility location and allocation problems. Furthermore, this optimality analysis technique applies to optimality analysis of costs or right-hand-side elements in continuous linear programs and optimality analysis of costs in mixed of pure integer linear programs. A system which automates this process for supply chain optimization at PFS Logistics Consulting is also detailed, along with description of its application and impact in their daily operations
