arXiv:2607. 11725v1 Announce Type: cross Abstract: Prefabricated prefinished volumetric construction moves most building work into module factories, whose production floor operates as a flexible job shop.
arXiv:2610.22433v1 Announce Type: new Abstract: Flexible job-shop scheduling becomes resource-coupled when a scarce vehicle fleet moves jobs between stations, because the schedule sets the transport...
arXiv:2606. 13682v1 Announce Type: new Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases.
The study evaluates six deep reinforcement learning (DRL) approaches for high‑performance computing (HPC) job scheduling, comparing Proximal Policy Optimization (PPO), Advantage Actor‑Critic (A2C), and Deep Q‑Network (DQN) with and without action masking. Across two real Slurm workload traces—one homogeneous and one heterogeneous—masked DRL treatments complete evaluation, while unmasked ones are truncated. On the heterogeneous trace, MaskablePPO outperforms baseline heuristics in all three metrics, achieving waiting times within 10% of the best heuristics and a 22% lower maximum bounded slowdown than Last‑Come, First‑Served (LCFS).
StoreBench is a live‑commerce environment where an autonomous agent runs a mid‑size online apparel store on a production‑grade backend, facing continuous customer orders, supplier price changes, and market shocks. The agent uses the same 29 merchant tools as a human operator, with a windowed operation budget that ties simulated time to actions, ensuring that model latency does not affect the simulation. Seven large language models were evaluated across 11 scenarios of 30–45 days and a full simulated year, but none matched the scripted smart‑triage policy, and human experts outperformed all models.
The paper introduces a method for learning search control policies that dramatically reduce the space required for heuristic plan search. By using an indexical policy with registers and a special choose rule, the approach ensures that every execution is bounded polynomially in the number of objects, allowing a depth‑first procedure to find plans in polynomial space. The learned policies, obtained via a language‑model guided counterexample loop, successfully solve 1,709 of 1,890 IPC 2023 Learning Track tasks and the Autoscale Agile suite, outperforming state‑of‑the‑art planners such as LAMA, BFWS, and Levitron.
arXiv:2604. 24117v2 Announce Type: replace Abstract: Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing.
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.
arXiv:2606. 18438v1 Announce Type: cross Abstract: In this paper, we study a sequential workforce management problem in a contingent labor setting with uncertainty in both worker production and labor supply.
arXiv:2606. 25362v1 Announce Type: cross Abstract: Sequential contextual stochastic programs model real-time decision systems in which each time epoch commits to an action under uncertainty whose consequences propagate into future decisions.
arXiv:2608.29690v1 Announce Type: cross Abstract: Advances in machine learning (ML) have created new opportunities to complement traditional operations research (OR) methods. In particular, transform...
arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.