论文总字数：16501字

摘 要

ABSTRACT………………………………………………………………………II

第一章 绪论………………………………………………………………………1

1.1 绪论…………………………………………………………………………1

1.2 课题背景……………………………………………………………………1

1.3 国内外研究现状……………………………………………………………3

1.3.1国外研究现状………………………………………………………3

1.3.2国内研究现状………………………………………………………3

1.4 研究思路和技术方法………………………………………………………4

第二章 企业现状………………………………………………………………5

2.1企业物流分析………………………………………………………………5

2.1.1企业概况……………………………………………………………5

2.1.2仓库分析……………………………………………………………5

2.1.3 TMS系统……………………………………………………………6

2.1.4货物调度及配送……………………………………………………6

2.2企业现存问题研究…………………………………………………………7

第三章 智能订单预测…………………………………………………………9

3.1预测分析…………………………………………………………………… 9

3.2损失函数矫正……………………………………………………………… 9

3.3线性回归模型………………………………………………………………11

3.4预测结果……………………………………………………………………12

第四章 路径优化程序…………………………………………………………14

4.1模拟建模……………………………………………………………………14

4.1.1问题假设……………………………………………………………14

4.1.2坐标及距离表建立…………………………………………………15

4.2优化方案选择及编程介绍…………………………………………………18

4.2.1节约里程法…………………………………………………………18

4.2.2 禁忌搜索法…………………………………………………………21

4.3测试结果分析………………………………………………………………25

第五章 总结………………………………………………………………………27参考文献…………………………………………………………………………28

附录A 程序代码………………………………………………………………29

致谢………………………………………………………………………………32

企业智能调度系统研究

摘 要

现代物流技术也在与时俱进，人工操作、计算的精度与效率正不断接受着考验。规模越大，累积越多，成本正随着发展规模同比增长，想要获得更多的利润就必须降低运行成本，而物流配送环节正是物流企业成本中占比大且优化可能性大的部分。作为物流企业的核心之一，物流环节能够有效地降低成本，提高满意度，使企业效益显著增加。

本文将以某物流企业为研究对象，通过调查分析智能调度系统的现状，结合新兴技术分析物流企业内部运作机制，运用IE原理，对整体流程进行优化。

通过机器学习中的线性回归对订单量进行初步预测，同时通过建模模拟配送客户、配送条件等对象，结合Python程序模块对物流路线进行快速规划，在节约调度成本的同时降低车辆消耗。

关键词: 线性回归 python 路径优化 运筹学 启发式算法

Research on intelligent scheduling system of enterprise logistics

Abstract

The accuracy and efficiency of manual operation and calculation are constantly being tested as the modern technology is refreshing itself. The larger the scale is, the more the accumulation is, and the cost is increasing year on year with the development scale. The operating cost has to be reduced as long as we want to earn more profit. The logistics distribution link is just the part of the logistics enterprise cost which accounts for a large proportion and has a great possibility of optimization.

This paper will take a logistics enterprise as the research object, through the investigation and analysis of the current situation of the intelligent scheduling system, combined with the emerging technology to analyze the internal operation mechanism of the logistics enterprise, and use the IE principle to optimize the overall process.

It uses linear regression in machine learning to predict the quantity of order. At the same time, through modeling and Simulation of distribution customers, distribution conditions and other objects, combined with Python program module for rapid planning of logistics routes, to save scheduling costs while reducing vehicle consumption.

Keywords:Linear regression;python;path optimization;operational research; heuristic algorithm

第一章.绪论

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