Version 1
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 23 July 2024 (13:03:18 CEST)
How to cite:
Wallrath, R.; Franke, M. B. Integrating MILP, Discrete-Event Simulation, and Data-Driven Models for Distributed Flow Shop Scheduling using Benders Cuts. Preprints2024, 2024071794. https://doi.org/10.20944/preprints202407.1794.v1
Wallrath, R.; Franke, M. B. Integrating MILP, Discrete-Event Simulation, and Data-Driven Models for Distributed Flow Shop Scheduling using Benders Cuts. Preprints 2024, 2024071794. https://doi.org/10.20944/preprints202407.1794.v1
Wallrath, R.; Franke, M. B. Integrating MILP, Discrete-Event Simulation, and Data-Driven Models for Distributed Flow Shop Scheduling using Benders Cuts. Preprints2024, 2024071794. https://doi.org/10.20944/preprints202407.1794.v1
APA Style
Wallrath, R., & Franke, M. B. (2024). Integrating MILP, Discrete-Event Simulation, and Data-Driven Models for Distributed Flow Shop Scheduling using Benders Cuts. Preprints. https://doi.org/10.20944/preprints202407.1794.v1
Chicago/Turabian Style
Wallrath, R. and Meik B. Franke. 2024 "Integrating MILP, Discrete-Event Simulation, and Data-Driven Models for Distributed Flow Shop Scheduling using Benders Cuts" Preprints. https://doi.org/10.20944/preprints202407.1794.v1
Abstract
Digitalization plays a crucial role in improving the performance of chemical companies. In this context, different modeling, simulation, and optimization techniques like MILP, discrete-event simulation (DES), and data-driven (DD) models are being used. Due to their heterogeneity, these techniques must be executed individually, and holistic optimization is a manual and time-consuming process. We propose Benders decomposition to combine these techniques into one rigorous optimization procedure. The main idea is that heterogeneous models can simultaneously be optimized as Benders subproblems. We illustrate this concept with the distributed permutation flow shop scheduling problem (DPFSP) and assume that a MILP, DES, and DD model exist for three flow shops. Our approach can compute bounds and report gap information on the optimal makespan for five medium-sized literature instances. The approach is promising because it enables the optimization of heterogeneous models and makes it possible to build optimization capabilities on an existing model and tool landscape in chemical companies.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
