Understanding and managing product line complexity: Applying sensitivity analysis to a large-scale MILP model to price and schedule new customer orders

Abstract

This article analyzes a complex scheduling problem at a company that uses a continuous chemical production process. A detailed mixed-integer linear programming model is developed for scheduling the expansive product line, which can save the company an average of 1.5% of production capacity per production run. Furthermore, through sensitivity analysis of the model, key independent variables are identified, and regression equations are created that can estimate both the capacity usage and material waste generated by the product line complexity of a particular production run. These regression models can be used to estimate the complexity costs imposed on the system by any particular product or customer order. Such cost estimates can be used to properly price new customer orders and to most economically assign them to the production runs with the best fit. The proposed approach may be adapted for other long-production-run manufacturing companies that face uncertain demand and short customer lead times.