CHN Energy Yulin Energy Co., Ltd.
CHN Energy Information Technology Co., Ltd.
State Key Lab of Intelligent Transportation System, Beihang University

井工煤矿无轨胶轮车数量多,运输易受搬家倒面、突发事件等影响,传统的人工调度方法效率低,且易造成车辆闲置、空载、里程浪费等问题,而现有的辅助运输车辆调度方法大多面向固定任务使用离散事件优化的方案,将全局模型拆解为局部模型,缺乏对井工煤矿整体情况的分析。针对上述问题,提出了一种基于百度工业求解器的井工煤矿无轨胶轮车全局调度模型,介绍了该模型中信息收集模块、数据建模模块和工业求解器模块设计方案,以及无轨胶轮车全局调度流程。该模型采用基于“分批求解、迭代优化”的无轨胶轮车全局调度算法,由百度工业求解器基于动作调整启发式算法对车辆调度问题进行优化求解,解决了传统调度模型求解时间长、易陷入局部最优解等问题。实验结果表明,基于百度工业求解器的井工煤矿无轨胶轮车全局调度模型较人工调度方法大幅降低了使用车次,提高了车辆运转效率,调度优化的求解时间低于基于Gurobi求解器的局部调度模型,更适用于井下辅助运输场景下大规模复杂调度任务。

There are a large number of trackless rubber-tyred vehicles in underground coal mines. Thetransportation is easily affected by moving surfaces, emergencies, and other factors. Traditional manualscheduling methods are inefficient and prone to problems such as idle, empty, and wasted vehicles. However,existing auxiliary transportation vehicle scheduling methods mostly focus on fixed tasks using discrete eventoptimization schemes. It breaks down the global model into local models, and lacks analysis of the overallsituation of underground coal mines. In order to solve the above problems, a global scheduling model for tracklessrubber-tyred vehicle in underground coal mines based on Baidu industrial solver is proposed. The design schemeof the information collection module, data modeling module, and industrial solver module in this model areintroduced, as well as the global scheduling process for trackless rubber-tyred vehicles. This model adopts aglobal scheduling algorithm for trackless rubber-tyred vehicles based on "batch solving and iterative optimization". The vehicle scheduling problem is optimized and solved by Baidu industrial solver based on actionadjust heuristic algorithm. It solves the problems of long solving time and easy getting stuck in local optimalsolutions in traditional scheduling models. The experimental results show that the global scheduling model fortrackless rubber-tyred vehicles based on Baidu industrial solver significantly reduces the number of vehicles usedand improves vehicle operation efficiency compared to manual scheduling methods. The solution time forscheduling optimization is lower than that of the local scheduling model based on Gurobi solver. It is moresuitable for large-scale complex scheduling tasks in underground auxiliary transportation scenarios.

国家自然科学基金项目(52102448);国家重点研发计划项目(2022YFB4703702)

陈湘源,潘涛,周彬. 井工煤矿无轨胶轮车全局调度模型[J]. 工矿自动化,2023,49(12):63-69.

CHEN Xiangyuan, PAN Tao, ZHOU Bin. Global scheduling model for trackless rubber-tyred vehicle in underground coalmines[J]. Journal of Mine Automation,2023,49(12):63-69.