强化学习辅助进化算法兼顾车间工期与分时电价成本：《科学报告》以Brandimarte基准算例验证双目标模型，11月13日刊出
《科学报告》于2026年11月13日刊出分时电价下节能柔性作业车间调度研究。论文由Guo、Li、Zhang等人发表，数字对象标识符为10.1038/s41598-026-73460-4，2026年5月28日收稿、9月23日接受。
研究针对柔性作业车间在分时电价下难以同时优化生产效率与用电成本的问题，建立了以最小化最大完工时间与总电费为目标的双目标调度模型，并提出一种名为EAHROA的能源感知混合强化优化算法。
该算法采用两段式编码方案，分别表示工序排序与机器选择。解码时引入分时电价感知机制，计算加工与待机用电成本，引导工序尽可能避开电价高峰时段。
在Brandimarte基准算例上的仿真实验显示，该算法在最大完工时间与总电费之间给出了有竞争力的权衡，并在对比研究中取得了更好的IGD、HV与SC指标结果。
（来源：《科学报告》）
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)
来源：《科学报告》
发布：GSM·美国（发布链接）
图片来源：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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