| 丘陵山地环形单轨运输系统静态调度优化方法 |
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| 引用本文： | 杨方,周敏,江溢华,李善军.丘陵山地环形单轨运输系统静态调度优化方法[J].农业工程学报,2023,39(4):37-46. |
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| 作者姓名： | 杨方 周敏 江溢华 李善军 |
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| 作者单位： | 1. 华中农业大学工学院,武汉 430070;2. 农业农村部长江中下游农业装备重点实验室,武汉 430070;3. 农业农村部柑橘全程机械化科研基地,武汉 430070 |
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| 基金项目： | 中央高校基本科研业务费专项资金资助项目（2662022GXYJ002）;湖北省重点研发计划项目（2021BBA091）。 |
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| 摘 要： | 为了提高环形单轨运输系统完成静态任务的作业效率,该研究以丘陵山地为应用场景,针对任务点与运输车的匹配与调度需求问题,综合考虑最大程度满载的因素,建立了具有任务点“拼车”组合处理和运输车任务分配两个阶段的数学模型。提出基于相邻位置、任务量和随机序列不同优先级的3种启发式规则算法解决以运输车上货次数最少为目标的“拼车”组合问题,采用基于变邻域搜索的遗传算法解决以运输车堵塞次数最少为目标的运输车任务分配问题。通过模拟3种任务批量的运输情形,验证优化算法的性能,结果表明：基于任务量在3种启发式规则算法中求解速度最快,在80%的试验中获得的可行解最优,选择基于任务量和与之性能接近的基于随机序列的结果分别作为运输车任务分配问题的初始解,基于任务量更有利于第二阶段运输车任务分配问题的求解,得到的分配方案堵塞次数更少。基于变邻域搜索的遗传算法能显著提高标准遗传算法的求解质量,使堵塞次数降低了33.3%～100%,在大规模运输问题的求解中比变邻域搜索算法表现出更高的稳定性,最少堵塞次数出现的概率分别提高了10%和40%,有效性也优于整体匹配规则、随机重启爬山等其他类型的算法。该研究实现了对环形单轨运输系...
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| 关 键 词： | 农业机械 调度 算法 启发式规则 遗传算法 变邻域搜索 |
| 收稿时间： | 2022/9/26 0:00:00 |
| 修稿时间： | 2023/2/10 0:00:00 |
Static scheduling optimization method for the circular monorail transportation system in hilly and mountainous areas |
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| Yang Fang,Zhou Min,Jiang Yihu,Li Shanjun.Static scheduling optimization method for the circular monorail transportation system in hilly and mountainous areas[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(4):37-46. |
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| Authors: | Yang Fang Zhou Min Jiang Yihu Li Shanjun |
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| Institution: | 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China; 2. Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River, Ministry of Agriculture and Rural Affairs, Wuhan, 430070, China; 3. Citrus Mechanization Research Base, Ministry of Agriculture and Rural Affairs, Wuhan 430070, China |
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| Abstract: | Abstract: Circular monorail transportation system has been widely applied in hilly mountain areas. It is a high demand to improve the operation efficiency of static tasks in recent years. This study aims at the matching and scheduling requirements between mission points with the transport needs and transporters. The maximum full load was also considered comprehensively to determine the practice scenario of one-way goods transportation. The task was in the form of transporting cargo boxes from several loading points to a single unloading point. A mathematical model was established with the "carpooling" combined with the processing at the mission points and task assignment of transporters. The solving process of the transporter schedule was divided into two stages. In the first stage, the heuristic rule algorithms using different priorities were proposed to solve the "carpooling" combined processing with the goal of the least number of loadings of transporters. Combination algorithms included prioritization by adjacent position, task volume, and random sequence. In the second stage, a genetic algorithm based on variable neighborhood search (GA_VNS) was employed to solve the task assignment of transporters with the goal of the least number of transporter jams. GA_VNS was a hybrid intelligent algorithm with GA as a framework with the variable neighborhood search strategy, particularly with both global and local optimization. The performance of GA_VNS was compared with the standard GA, variable neighborhood search algorithm, overall matching rule, and random-restart hill-climbing algorithm. Transporting situations were simulated corresponding to three kinds of task batches. Five control experiments were carried out for each batch to improve the reliability of the experimental data. Experiments proved that the selected algorithms were superior. The designed combination algorithm with the task volume as the priority was solved fastest and was independent of the task batch. The operation time was generally between 0.3 and 0.4 s, with the lowest time cost. The feasible solution in 80% of the experiments was optimal among the three combination algorithms, and the solution advantage was concentrated in the large batch experiment, indicating more suitable for solving large-scale transportation in real time than the rest. The number of loadings in the remaining experiments was also only one unit more than the optimal solution. The solution of the assignment was also facilitated for the second stage scheduling in the validity. Therefore, the best performance was achieved in the combination algorithm with the task volume as a priority. The GA_VNS was employed to optimize the solution in multiple directions. GA_VNS significantly improved the quality of standard GA solution, and the number of transporter jams was reduced by 33.3%-100%. The higher stability was found in large-batch transporting situations, compared with the single variable neighborhood search algorithm. The probability of the minimum number of transporter jams appearing increased by 10% and 40%, but the operation time was about 10 times longer. The validity was better than other types of algorithms, such as overall matching and random-restart hill climbing. The finding can provide an approximately optimal solution to the static scheduling of the circular monorail transportation system in hilly mountain areas |
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| Keywords: | agricultural machinery scheduling algorithms heuristic rule genetic algorithm variable neighborhood search |
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