Abstract
Response surface methodology (RSM) is used for optimality analysis of the cost coefficients 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 coefficients have the greatest impact on the optimal total cost surface over the specified coefficient ranges. The mixed integer linear programming problems of interest are the large‐scale facility location and allocation problems in supply chain optimization. A system that automates this process for supply chain optimization at PFS Logistics Consulting is discussed and an example is presented.
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