arXiv:2601. 06542v2 Announce Type: replace-cross Abstract: In this paper, we investigate the Resource-Constrained Project Scheduling Problem (RCPSP) with Time-of-Use (TOU) energy tariffs and machine states, a variant of RCPSP for production scheduling, where energy price is part of the criteria and one highly energy-demanding machine can be in one of the following three states: proc, idle, or off.
By Corentin Juvigny, Anton\'in Nov\'ak, Jan Mand\'ik, Zden\v{e}k Hanz\'alek
arXiv:2607. 05177v1 Announce Type: new Abstract: Workforce scheduling is an NP-hard combinatorial optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, employee preferences and operational objectives.
By Vipul Patel, Anirudh Deodhar, Dagnachew Birru
arXiv:2609.36578v1 Announce Type: cross
Abstract: Scheduling problems arise from repeatedly selecting one item from a set of candidates based on their states. These problems often reduce to assigning...
By Hong Je-Gal, Hyun-Suk Lee
arXiv:2602. 20427v2 Announce Type: replace Abstract: Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis.
By Yaohui Cai, Vesal Bakhtazad, Cunxi Yu, Zhiru Zhang
arXiv:2608.30419v1 Announce Type: new
Abstract: Healthcare workforce scheduling is an NP-hard optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, emp...
By Vipul Patel, Anirudh Deodhar, Dagnachew Birru
arXiv:2601. 20735v2 Announce Type: replace Abstract: We develop a computational approach to Metric Answer Set Programming (ASP) to allow for expressing quantitative temporal constraints, like durations and deadlines.
By Arvid Becker, Pedro Cabalar, Martin Di\'eguez, Susana Hahn, Javier Romero, Torsten Schaub
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
arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.
By Jin Wang, Xi Lin, Handing Wang
The paper introduces SDDL, a neuro‑symbolic framework that converts natural‑language combinatorial scheduling problems into compact, solver‑aligned representations, delegating low‑level modeling and search to a deterministic compiler and external solver. On a 300‑instance subset of scheduling tasks, SDDL achieves higher feasibility rates for resource‑constrained language models—up to 55.3% and 28.3%—compared to direct‑generation baselines (23.7% and 1.3%) and solver‑code baselines (21.7% and 7.0%), with a median optimality gap of 0.0% among feasible schedules.
By Shrenil Shaun Sharma, Avi Sharma
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
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 introduces a temporal planning framework for intelligent flood response, integrating priority-driven triage, route accessibility, resource allocation, and supply management. It supports mid-execution re-planning to adapt to unexpected environmental changes and is implemented in both ANML and PDDL 2.1 for broad planner compatibility. Experimental results demonstrate the framework’s feasibility and scalability for modeling and solving flood response scenarios.
By Fazlul Hasan Siddiqui, Md. Monjurul Islam, Sabah Binte Noor