应急物流系统的定位-路径问题（LRP）研究

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摘要

进入 21 世纪以来，人们不但要疲于应付社会活动所造成的各种危机，而且亦

被频繁爆发的各种自然灾害所困扰。在现代社会高速发展的同时，人口、资源、

环境、公共卫生等方面的问题与压力与日俱增，这也直接导致了各类突发事件爆

发的频率加快，影响范围和危害程度急剧上升，因此研究应急物流系统和应急物

资调度问题具有非常重要的现实意义和理论意义。我国领土幅员辽阔，有些地区

又处在自然灾害频发地带，几乎每年都会发生各种人为的或自然的灾害。比如 1976

年的唐山大地震、1998 年的长江大洪水、2003 年的“SARS ”、2005 年的松花江

重大污染事件、2008 年初的南方雪灾以及随后发生的震惊世界的汶川大地震、甲

型HIN1 流感以及 2010 年青海玉树发生的 7.1 级地震等，这些突发事件造成的巨

大损失，给人们留下了惨痛的记忆和教训。据不完全统计，由应急物流造成的损

失占灾害总损失的 15%-20%。当灾害发生后，如何科学有效的优化应急救援物资

配送体系，降低物流总成本，提高物流服务效率是灾后应急系统可靠性、稳定性

和时效性的关键。

应急物流中心定位-分配问题(Location-Allocation Problem , LAP)和救援车辆路

径安排问题(Vehicle Routing Problem ,VRP)是应急物流系统优化中的两个关键问

题，传统意义上都是分别解决的。但事实上，这两者之间存在相互依赖、相互影

响的关系，从系统整体优化的角度看有必要对进行集成研究，即研究应急物流

系统中的定位-路径问题(Location-Routing Problem ,LRP)。但现有对LRP的研究基本

针对一般物流系统，而面向应急物流系统中LRP的研究还比较少。

突发事件的应急救援中对于应急物资的需求往往是不确定的，而且各种自然

灾害可能对运输道路造成损毁，导致运输网络的容量的不确定性以及运输车辆在

道路上行驶的不确定性。考虑到 LRP 本身是 NP 难问题，研究者们在求解时多采

取两阶段的方式，将其分解为定位-配给问题和车辆路线安排问题，将 LAP 的输出

作为 VRP 的输入求得。

本文将 LRP 看作是一个整体，在考虑灾后应急物资需求不确定，运输网络拥

堵情况存在的条件下，建立一个以应急救援时间（应急物资运送总时间）为最优

目标，以系统总成本为次优目标的多目标模糊需求静态 LRP 模型，在充分比较了

遗传算法和免疫算法的区别以及相关性后，提出了一种改进型的遗传算法对模型

进行求解，并通过算例计算验证了该模型和算法的有效性。

关键词：应急物流应急物资配送定位-路径问题改进型遗传算法

ABSTRACT

In the 21st century, people are not only struggling with the crisis caused by social

activities, but also in the trouble of frequent natural disasters. At the same time, with

rapidly development in modern society, some issues and pressures in population,

resources, environment, public health increasing, which led to the outbreak of the

frequency of all types of emergencies faster directly, reach and sharp increase in degree

of harm, the study emergency logistics systems and emergency supplies scheduling

problem has a very important practical and theoretical significance. China has vast

territory, in some areas of which natural disasters occurred frequently, occur a variety of

man-made or natural disasters almost every year. Such as the Tangshan earthquake in

1976, the Yangtze River flood in 1998, "SARS" in 2003, a major pollution incident in

the Songhua River of 2005, the South snowstorm in early 2008 and the following

earthquake that shocked the world in Wenchuan, HIN1 influenza and Yushu earthquake

in Qinghai Province in 2010 of magnitude 7.1, these incidents caused great losses to

people and leaving the painful memories and lessons to learned. According to

incomplete statistics, the emergency logistics loss accounted for 15% -20% of the total

loss of disaster. When a disaster occurs, how to optimize the emergency relief supplies

and distribution system scientific and effectively and reduce total logistics costs and

improve efficiency of logistics services are the key of reliability, stability and timeliness

to the disaster response system.

Emergency logistics center location - allocation problem (LAP) and the vehicle

routing problem (VRP) are the two key issues of the emergency logistics system

optimization and solved respectively in the traditional sense. But in fact, there is

interdependence between the two, the relationship of mutual influence. It is necessary to

integrate these research from the perspective of system overall optimization, that is to

study the positioning of Location- Routing Problem (LRP ) in emergency logistics

system. However, the existing research are based on the general LRP logistics system,

logistics system for Emergency Research LRP is still relatively small.

As the needs for emergency supplies in emergency rescue often uncertain, and

various natural disasters may cause damage to road transport, resulting in the capacity

of transport networks and transport vehicles, the uncertainty in the uncertainty on the

roads . Taking into account the LRP itself as a NP hard problem, the researchers take

two-stage approach in solving the problem normally, divide it to location - allocation

problem and the vehicle routing problem, taking the output of the VRP as the LAP input

to solve it.

Considering the uncertain needs of emergency supplies after disaster and

congestion conditions of transportation network existence in the circumstances, this

article put LRP as a whole and establish a multi-objective fuzzy demand LRP model

which taken emergency response time (total time of delivery of emergency supplies) for

the optimal objective, the total cost of the system as sub-optimal target. After the full

comparison of genetic algorithm and immune algorithm and the correlation between

them, I propose an improved genetic algorithm to solve the model and demonstrate

the model and the algorithm through numerical examples.

Key Words: Emergency Logistics, distribution positioning emergency

Supplies, Location-Routing Problem, Improved Genetic

Algorithm

摘要

ABSTRACT

第一章绪论...................................................... 1

§1.1 研究背景及意义.................................................1

§1.2 国内外研究现状分析.............................................4

§1.2.1 国外研究现状................................................4

§1.2.2 国内研究现状................................................5

§1.2.3 研究现状分析及存在的问题....................................7

§1.3 本文的研究内容概述.............................................7

第二章应急物流概述..............................................9

§2.1 应急物流的概念及研究内容.......................................9

§2.2 应急物流与一般物流的差异.......................................10

§2.2.1 一般差异..................................................10

§2.2.2 定位-路径问题(LRP)的相关差异...............................10

§2.3 LAP、VRP 以及 LRP 简述.......................................11

§2.3.1 定位分配问题（LAP）概述...................................11

§2.3.2 路径安排问题（VRP）概述...................................11

§2.3.3 定位-路径问题(LRP)概述.....................................12

§2.3.4 解决 LRP 的算法研究........................................13

§2.4 本章小结.......................................................16

第三章一种改进的遗传算法（免疫遗传算法）..........................17

§3.1 算法介绍.......................................................17

§3.2 算法比较.......................................................18

§3.2.1 两种算法的不同特点.........................................18

§3.2.2 两种算法的优缺点...........................................18

§3.3 一种改进的遗传算法（免疫遗传算法）.............................20

§3.4 本章小结.......................................................22

第四章应急物资配送的多目标模糊静态 LRP 研究.......................23

§4.1 问题描述.......................................................23

§4.2 模型研究.......................................................23

§4.2.1 相关假设..................................................23

§4.2.2 符号说明..................................................24

§4.2.3 模型建立..................................................25

§4.2.4 模型的转化................................................26

§4.3 免疫遗传算法...................................................28

§4.3.1 抗体表示..................................................28

§4.3.2 初始化抗体群..............................................29

§4.3.3 适应度函数................................................29

§4.3.4 亲和力函数................................................30

§4.3.5 排斥力函数................................................30

§4.3.6 免疫算子..................................................30

§4.3.7 抗体的促进和排斥策略......................................31

§4.3.8 最优抗体记忆策略..........................................32

§4.3.9 选择策略..................................................32

§4.4 改进后的遗传算法步骤..........................................32

§4.5 实证分析......................................................33

§4.6 本章小结......................................................39

第五章结论与展望..................................................41

§5.1 研究工作总结...................................................41

§5.2 研究展望.......................................................41

参考文献............................................................43

在读期间公开发表的论文和承担科研项目及取得成果......................47

致谢................................................................49

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摘要：

摘要进入21世纪以来，人们不但要疲于应付社会活动所造成的各种危机，而且亦被频繁爆发的各种自然灾害所困扰。在现代社会高速发展的同时，人口、资源、环境、公共卫生等方面的问题与压力与日俱增，这也直接导致了各类突发事件爆发的频率加快，影响范围和危害程度急剧上升，因此研究应急物流系统和应急物资调度问题具有非常重要的现实意义和理论意义。我国领土幅员辽阔，有些地区又处在自然灾害频发地带，几乎每年都会发生各种人为的或自然的灾害。比如1976年的唐山大地震、1998年的长江大洪水、2003年的“SARS”、2005年的松花江重大污染事件、2008年初的南方雪灾以及随后发生的震惊世界的汶川大地震、甲型HIN1流感...

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