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.
By Changkun Guan, El Mehdi Er Raqabi, Mathieu Tanneau, Pascal Van Hentenryck
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.
By Yi-Xiang Hu, Yuke Wang, Feng Wu, Zirui Huang, Shuli Zeng, Xiang-Yang Li
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.
By Seung Jin Choi, Kimiya Jozani, Josh Cooper, Esra Buyuktahtakin Toy
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: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.
By Martha Del Toro, Raquel Fuentetaja, Angel Garc\'ia-Olaya
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.
By Shrenil Shaun Sharma, Avi Sharma