A Reinforcement Learning-Assisted Evolutionary Algorithm Balances Makespan and Time-of-Use Electricity Cost in Job Shop Scheduling: A Bi-Objective Model Tested on Brandimarte Benchmarks in Scientific Reports, Published November 13
Scientific Reports published a study of energy-efficient flexible job shop scheduling under time-of-use electricity pricing on November 13, 2026. The paper, by Guo, Li, Zhang and co-authors, carries digital object identifier 10.1038/s41598-026-73460-4, was received on May 28, 2026 and accepted on September 23, 2026. To address the difficulty of jointly optimizing production efficiency and electricity cost in flexible job shop scheduling under time-of-use electricity pricing, the study established a bi-objective scheduling model to minimize the makespan and the total electricity cost, and proposed an energy-aware hybrid reinforcement optimization algorithm called EAHROA. The algorithm adopts a two-segment encoding scheme to represent operation sequencing and machine selection. During decoding, a time-of-use electricity price-aware mechanism calculates processing and idle electricity costs, guiding operations to avoid peak-price periods when possible. Simulation experiments on Brandimarte benchmark instances showed that the algorithm provides competitive trade-offs between makespan and total electricity cost and achieved improved IGD, HV and SC results in the comparative studies.
(Source: Scientific)
Source: Scientific
Published by GSM · USA (original Chinese edition)
Photo: 图：工厂自动化生产线上的工业机器人。ell brown，CC BY-SA 2.0，经 Wikimedia Commons。 / Photo: ell brown, CC BY-SA 2.0, via Wikimedia Commons.
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