Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty - Beykal, Burcu; Avraamidou, Styliani; Pistikopoulos, Efstratios N. (Computers & chemical engineering, 2022)
•We present a data-driven algorithm for the solution of integrated planning and scheduling problems under uncertainty.•The proposed algorithm is based upon the recently developed DOMINO framework for the solution of single-follower bi-level problems, which is here extended for the solution of bi-level multi-follower stochastic optimization problems.•Computational studies to show the applicability of the proposed approach are presented through the solution of three planning and scheduling case studies. The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. In this paper, we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.