钢铁企业物流库存的车辆调度优化

Optimization of Vehicle Scheduling for Logistics Enterprises in Iron and Steel Enterprises
LEI Zhao-ming, ZHAO Fan, LIAO Wen-zhe, WANG Peng-cheng
(College of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China)
1引言
对于生产制造企业来说，在生产与销售成本已经较少有 压缩空间可言的情况下,物流成本的优化获得各大企业的广 泛关注⑴。钢铁企业作为较大的生产制造企业，其生产成本 和原燃料价格的大幅度增长导致越来越多的企业将关注的
基金项目：河北省科技支撑计划项目(13210307D) 收稿日期：2017-10-16修回日期：2017-11-04
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重点转移到厂内外物流成本的优化上来。而燃料入库调度 作为钢厂厂内物流的重要组成部分，在厂内物流成本优化中 发挥着关键的作用，因此良好的车辆调度⑵及资源入库调度 对于钢铁企业来说意义重大。
第36卷第1期 文章编号:1006-9348 (2019)01 -0366-07
计算机仿真
钢铁企业物流库存的车辆调度优化
2019年1月
雷兆明，赵凡，廖文詰，王鹏程
(河北工业大学控制科学与工程学院，天津300130)
摘要:作为整个钢铁企业生产过程的准备阶段，车辆人库环节关系着下游生产线的资源配置和库存的运转效率。传统的调 度策略没有从车辆人库顺序和时间角度对库存调度的物流成本和效率进行分析 ，导致调度效率低,成本高，而选择合适的车 辆入厂入库顺序和时间并考虑多车共同作业可以有效的提高库存效率且节省成本 。为此首先建立了最小化货车出厂时间 和传送带最小运转成本的整数规划模型，然后提出了改进萤火虫优化算法对复杂模型进行求解，通过自适应策略改进算法 使其增强个体搜索能力，同时在萤火虫移动的过程中引入了 Levy变异，避免算法陷人局部最优而无法寻到最优解,最后由 测试函数和仿真实例进行测试。结果表明，改进算法可以迅速找岀最优调度方案，从而指导企业合理安排资源人库，优化了 物流库存管理。 关键词:人库优化;车辆调度;萤火虫优化算法 中图分类号:TP391.9 文献标识码：B
ABSTRACT: As the preparation phase of the whole iron and steel enterprise production process, the vehicle storage link relates to the resource allocation of the downstream production line and the operating efficiency of the inventory. The traditional scheduling strategy does not analyze the logistics cost and efficiency of inventory scheduling from the aspects of vehicle storage order and time, resulting in low scheduling efficiency and high cost. Choosing the right or­ der and time of arrival of the vehicle into the factory and considering the multi-vehicle co-operatioห้องสมุดไป่ตู้ can effectively improve the efficiency of inventory and save cost. For this reason. In the paper, we firstly established an integer pro­ gramming model that minimizes the factory time and the minimum operating cost of the conveyor, and then proposed an improvement of the Glowworm Swarm Optimization to solve the complex model. The adaptive strategy was used to improve the algorithm to enhance the individual's searching ability. At the same time, Levy mutation was introduced in the process of firefly movement to avoid the algorithm from falling into local optimals. Finally, tests were carried out with test functions and simulation examples. The results show that the improved algorithm can quickly find out the optimal scheduling scheme, so as to guide the enterprises to arrange the resource storage and optimize the logistics in­ ventory management. KEYWORDS: Warehousing optimization ； Vehicle scheduling ； Glowworm swarm optimization