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.
arXiv:2606. 07403v1 Announce Type: cross Abstract: Benders decomposition is a fundamental framework for solving large-scale mixed-integer optimization problems with complicating variables that, when fixed, yield significantly easier subproblems.
iScheduler is a reinforcement‑learning‑driven framework that tackles large‑scale Resource Investment Problems (RIP) by modeling them as a Markov decision process over decomposed subproblems and building schedules through sequential process selection. The approach speeds up optimization and allows efficient reconfiguration by reusing unchanged process schedules and only rescheduling affected processes. Using the new L‑RIPLIB benchmark, iScheduler achieves competitive resource costs while cutting time to feasibility by up to 43× compared to leading solver‑backed baselines.
arXiv:2607. 22550v1 Announce Type: cross Abstract: We propose a learning-augmented Benders decomposition framework to solve large-scale two-stage stochastic mixed-integer programs.
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.
arXiv:2607. 25484v1 Announce Type: new Abstract: In some real applications a plan may later become unfeasible due to newly imposed budget constraints, yet, at the same time, using only the original actions of the plan and their order is mandatory.
SCHEDBench is a natural‑language benchmark that evaluates whether large language models (LLMs) produce schedules that remain constraint‑feasible when the same scheduling problem is expressed in different natural‑language surface forms. The benchmark covers 1,132 instances from job‑shop scheduling, resource‑constrained project scheduling, nurse rostering, and curriculum timetabling, and uses domain‑specific templates and surface‑form variations to generate varied problem statements. Experiments with thirteen frontier and open‑weight LLMs show that models are not reliably invariant to semantically equivalent renderings, with surface‑form variation reducing feasibility and increasing hard‑constraint violations, especially when constraints are reordered.
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.
arXiv:2508.01751v3 Announce Type: replace Abstract: Modeling scheduling problems with conditional time intervals and cumulative functions has become a common approach when using modern commercial con...
arXiv:2606. 13407v1 Announce Type: new Abstract: Renewable energy is essential for meeting future energy demands; however, solar energy generation, which occurs only during daylight hours often does not align with household consumption patterns.
arXiv:2609.39559v1 Announce Type: new Abstract: In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produ...
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...
arXiv:2606. 10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints.