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Aiming at the problems of high cost in manual scheduling and low vehicle utilization rate for trackless rubber-tyred vehicles in underground coal mines, an intelligent scheduling model was established to coordinately optimize transportation efficiency and environmental performance. Firstly, the MT-CVRPTW model integrating hard time windows, cargo taboos and mileage constraints was established. Secondly, a bi-objective optimization model with the objectives of minimizing the total cost and carbon emissions was constructed, and a Parallel Genetic Algorithm-Adaptive Large Neighborhood Search (PGA-ALNS) hybrid optimization framework based on PGA and ALNS was proposed. The results of the simulation experiments show that, through the two-layer collaborative mechanism of the global search of PGA and the dynamic neighborhood optimization of ALNS, combined with the time-sensitive weight strategy and elite retention mechanism, the algorithm keeps the carbon emission cost at the minimum. Compared with the Genetic Algorithm and Simulated Annealing Algorithm, its total transportation cost is reduced by 11.1% and 7.6% respectively, the total distance is reduced by 10.7% and 4.7% respectively, and the solution time is shortened by 40.2% and 36.1% respectively. For the scheduling scheme optimized by the PGA-ALNS algorithm, its average vehicle load rate reaches 94.16%. This method effectively improves the economic efficiency and environmental friendliness of underground transportation, and provides a technical path for the construction of intelligent mines.

, authors=Jun YAO¹, Bangmin WU^{1, 2}, Yaoyi HE², authorsList=Jun YAO, Bangmin WU, Yaoyi HE, authorCompany=null, correspAuthors=Bangmin WU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322559883493355986, articleId=1322559883103285712, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=煤矿井下无轨胶轮车多目标优化调度方法研究, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对煤矿井下无轨胶轮车人工调度成本高、车辆利用率低的问题，通过构建智能化调度模型协同优化运输效率与环保性能。首先，建立融合硬时间窗、货物禁忌和里程约束的MT-CVRPTW模型。其次，构建以总成本与碳排放最小化为目标的双目标优化模型，并提出基于并行遗传算法（PGA）和自适应大邻域搜索（ALNS）的PGA-ALNS混合优化框架。仿真试验结果表明，该算法通过PGA的全局搜索与ALNS动态邻域优化的双层协同机制，结合时间敏感权重策略和精英保留机制，使碳排放成本保持最低，相较于遗传算法和模拟退火算法，其运输总成本分别降低了11.1%和7.6%，总距离分别降低了10.7%和4.7%，求解时间分别缩短了40.2%和36.1%。采用PGA-ALNS算法优化后的调度方案，其车辆平均实载率达94.16%。该方法有效提升了井下运输的经济性与环保性，为智能矿山建设提供了技术路径。

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姚军（1972—），男，陕西西安人，硕士，副教授，主要从事宽带数据网络、网络安全及煤矿安全预警等研究工作。E-mail:yaoj@xust.edu.cn

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姚军（1972—），男，陕西西安人，硕士，副教授，主要从事宽带数据网络、网络安全及煤矿安全预警等研究工作。E-mail:yaoj@xust.edu.cn

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