基于改进樽海鞘群算法的卸车调度优化

doi:10.3969/j.issn.1674-0696.2021.11.05

重庆交通大学学报（自然科学版） ›› 2021, Vol. 40 ›› Issue (11): 32-39.DOI: 10.3969/j.issn.1674-0696.2021.11.05

• 交通+大数据人工智能 • 上一篇

基于改进樽海鞘群算法的卸车调度优化

李长安1,3,4，赵德隆2，王国勇2，吴忠强2，张立杰1,3

(1. 燕山大学先进锻压成形技术与科学教育部重点实验室，河北秦皇岛 066004; 2 .燕山大学电气工程学院，河北秦皇岛 066004； 3.燕山大学河北省重型机械流体动力传输与控制重点实验室，河北秦皇岛 066004； 4. 神华天津煤炭码头有限责任公司，天津300457)

收稿日期:2020-09-05 修回日期:2020-12-10 发布日期:2021-11-24
作者简介:李长安（1982—），男，山东沂南人，博士研究生，主要研究方向为港口设备智能化控制和调度优化。E-mail：8246390@qq.com 通信作者：张立杰(1969—)，男，吉林辽源人，教授，主要研究方向为液压控制及可靠性、机器人控制。E-mail：ljzhang@ysu.edu.cn
基金资助:
国家自然科学基金项目(51875499)

Optimization of Discharging Scheduling Based on Improved Salp Swarm Algorithm

LI Changan1,3,4, ZHAO Delong2, WANG Guoyong2, WU Zhongqiang2, ZHANG Lijie1,3

(1. Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education, Yanshan University, Qinhuangdao 066004, Hebei, China; 2. College of Electric Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China; 3. Hebei Key Laboratory of Hydrodynamic Transmission and Control of Heavy Machinery, Yanshan University, Qinhuangdao 066004, Hebei, China; 4. Shenhua Tianjin Coal Terminal Co., Ltd., Tianjin 300457, China)

Received:2020-09-05 Revised:2020-12-10 Published:2021-11-24

摘要/Abstract

摘要： 针对港口载煤列车的卸车调度流程主要依靠工人经验进行调度作业，存在决策时间长、作业冲突和列车在港时间过长等问题。以列车在港时间最少为总优化目标，在已知列车到港时间及堆垛与煤种对应关系的前提下，考虑工作机械可用性、作业流程可达性及其相互约束关系等因素，构建了卸车调度数学模型。提出了一种基于改进樽海鞘优化算法的优化调度方法。引入自适应惯性权重，可有效地提高算法收敛速度；引入随机柯西变异策略，可有效地提高算法寻优能力。5个测试函数的测试结果表明：相比于樽海鞘优化算法、自适应樽海鞘优化算法、粒子群算法与鲸鱼优化算法，改进樽海鞘优化算法收敛速度更快，精度更高。港口堆场作业实际数据的仿真实验表明：改进樽海鞘优化算法可优化出满意的卸车调度任务，减少了火车总在港时间，提高了港口总体的工作效率。

关键词: 交通运输工程；铁路运输；卸车调度；改进樽海鞘算法；自适应惯性权重；柯西变异策略

Abstract: In the past, port coal discharge process mainly relied on the experience of workers for discharging operations, and there were problems such as long decision-making time, frequent operation conflicts, and excessive stagnation of train in the port. Taking the minimum time for trains in port as the overall optimization objective, on the premise of knowing the train arrival time and the corresponding relationship between stacking and coal types, a mathematical model of discharge scheduling was constructed, which considered the availability of working machinery, accessibility of operation process and their mutual constraints. Finally, the optimal scheduling method based on the improved salp swarm optimization algorithm was proposed. The adaptive inertia weight was introduced to effectively improve the convergence speed of the algorithm. Random Cauchy mutation strategy was introduced to effectively improve the optimization ability of the algorithm. The test results of the five test functions show that compared with the salp optimization algorithm, the adaptive salp optimization algorithm, particle swarm optimization algorithm and whale optimization algorithm, the improved salp optimization algorithm has faster convergence speed and higher accuracy. The simulation experiment of the actual data of port yard operation shows that the improved salp optimization algorithm can optimize and obtain the satisfactory discharge scheduling task, reduce the total time of the train in port and improve the overall work efficiency of the port.

Key words: traffic and transportation engineering; railway transportation; discharge schedule; improved salp swarm algorithm; adaptive inertia weights; Cauchy mutation strategy

中图分类号:

U292.2

李长安1,3,4，赵德隆2，王国勇2，吴忠强2，张立杰1,3. 基于改进樽海鞘群算法的卸车调度优化[J]. 重庆交通大学学报（自然科学版）, 2021, 40(11): 32-39.

LI Changan1,3,4, ZHAO Delong2, WANG Guoyong2, WU Zhongqiang2, ZHANG Lijie1,3. Optimization of Discharging Scheduling Based on Improved Salp Swarm Algorithm[J]. Journal of Chongqing Jiaotong University(Natural Science), 2021, 40(11): 32-39.

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基于改进樽海鞘群算法的卸车调度优化

Optimization of Discharging Scheduling Based on Improved Salp Swarm Algorithm

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