arXiv:2608. 14122v1 Announce Type: new Abstract: Production scheduling in complex manufacturing environments is challenging when sequence-dependent setup times, stochastic disturbances, and due-date constraints must be addressed simultaneously.
By Arne Kr\"oger, Ralf Buscherm\"ohle, Wilhelm Hasselbring, Henrik Wilbers
The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data‑driven decisions made during execution. It proposes framing the problem as a chance‑constrained optimization and introduces two formulations: a decomposed two‑stage approach (planning to minimize superfluous activities under a feasibility constraint, followed by scheduling to minimize makespan) and an integrated single‑stage approach. Experiments on two real‑world and one synthetic dataset show that the integrated method achieves better makespans but struggles with scalability, whereas the decomposed method scales to larger settings.
By Michel Kunkler, Stefanie Rinderle-Ma
The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data-driven decisions made during execution. It proposes framing the problem as a chance-constrained optimization and introduces two formulations: a decomposed two-stage approach that first minimizes expected superfluous activities under a feasibility constraint and then schedules to minimize makespan, and an integrated approach that combines planning and scheduling into a single model. Experiments on two real-world and one synthetic dataset show that the integrated approach achieves better makespans but struggles with scalability, whereas the decomposed approach scales to larger settings.
By Michel Kunkler, Stefanie Rinderle-Ma
arXiv:2511.20413v2 Announce Type: replace-cross
Abstract: \emph{Decision-focused learning} (DFL) trains predictive models to optimize downstream decisions rather than prediction accuracy alone. While...
By Zhuojun Xie, Adam Abdin, Yiping Fang
arXiv:2606. 07846v1 Announce Type: cross Abstract: LLM-agent workflows chain model calls and tool invocations, and spend most of their wall-clock time waiting on upstream operations before downstream ones can start.
By Faisal Fareed
arXiv:2607. 02624v1 Announce Type: cross Abstract: Modern automotive software architectures comprise large sets of mixed-criticality functions executing on shared multi-core platforms with strict real-time and end-to-end timing requirements.
By Silviu S. Craciunas, Christian Hakert, Jian-Jia Chen, Zden\v{e}k Hanz\'alek, Paul Pop