Advanced Available-to-Promise in Online Chemical Production Scheduling

In material requirements planning, available-to-promise (ATP) is a widely used approach for determining which customer orders can be fulfilled considering current inventory and planned production. While the production schedule for ATP is typically determined a priori based on forecasted demand, advanced ATP (AATP) provides additional flexibility by making production scheduling and order-promising decisions simultaneously as new order information arrives. In this work, we introduce an AATP framework for chemical manufacturing that at its core has a state–task network scheduling model that also incorporates order-promising decisions. Using a rolling-horizon approach that simulates the online AATP process, we evaluate the performance of AATP in comparison to ATP and more restricted variants of AATP under various demand forecast scenarios. The results from two computational case studies show that AATP generally is more robust against demand uncertainty, achieves higher order acceptance rates, and improves the overall profitability. We also demonstrate the impact of the batching interval, which determines the frequency of reoptimization, on AATP’s performance.