[Submitted on 15 Oct 2026]
Title:BOLT: Multi-Bottleneck Bound-Guided Learning for Flexible Job-Shop Scheduling with Time Lags and Resource-Coupled Transport
View a PDF of the paper titled BOLT: Multi-Bottleneck Bound-Guided Learning for Flexible Job-Shop Scheduling with Time Lags and Resource-Coupled Transport, by Ziheng Zhang and 5 other authors
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Abstract:Flexible job-shop scheduling becomes resource-coupled when a scarce vehicle fleet moves jobs between stations, because the schedule sets the transport waits. BOLT (bound-oriented learning for transport-coupled scheduling) builds a lower bound from chain, machine-capacity, and fleet-capacity relaxations. The bound equals the makespan at termination, so its per-step changes form a reward that sums to a constant minus the makespan. Because it never exceeds the attainable makespan, each candidate's bound increase is a proven rise of the makespan floor, which the policy reads as a price. The root bound certifies a completed schedule's suboptimality gap without a solver. On industry-motivated 10-module prefabricated-construction instances, explicit transport coupling outperforms a transport-penalty policy in every tested cell where loaded moves occupy at least 66% of fleet time and in none at or below 56%. Across five zero-shot 50- and 80-module production batches, BOLT outperforms the best dispatching-rule pair on four and the penalty policy on the three with long moves. In the three-vehicle 80-module batch, its mean makespan is 13.0% below the best pair and 4.2% below an hour-budget constraint-programming solver whose proven gap averages 34%. BOLT decides in 2.3-5.9 ms per event on one processor core and re-plans a disruption in seconds.
Comments: 12 pages main text plus 18 pages of supplementary material appended after the references (30 pages total). Code, data, and trained models: this https URL. Submitted to IEEE Transactions on Automation Science and Engineering
Subjects:
Machine Learning (cs.LG); Optimization and Control (math.OC)
Cite as: arXiv:2610.22433 [cs.LG]
(or arXiv:2610.22433v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.22433
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ziheng Zhang [view email] [v1] Thu, 15 Oct 2026 08:54:27 UTC (590 KB)
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