云制造环境下供应链企业节点选择及组合优化

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3.0 侯斌 2024-11-19 4 4 1.43MB 59 页 15积分

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摘要

随着信息化技术的迅速发展，企业间对于市场份额地争夺越来越激烈，为了

提升企业实力，企业越来越倾向于联合上、下游企业构建供应链，企图通过企业

之间的协同合作，实现互利共赢的目标。云制造为供应链线上、线下业务的对接

提供了一个良好地发展平台，增加了资源种类和数量。目前，随着互联网的不断

发展，云制造概念地提出及云平台模式的逐渐完善，以云制造、物联网等为代表

的网络制造成为关注焦点，我们国家紧跟现代化制造步伐，考虑将云制造尽快应

用到实际中，本文将云制造与供应链结合起来进行研究具有重要意义。

云制造业务繁杂，从中高效地选择出符合要求的供应链伙伴至关重要。本文

建立了供应链节点搜索及组合优化体系，能够在庞大的云制造资源平台中，完成

对资源的分配、重组，改善资源的搜索速度，实现闲置资源的高效利用，体现绿

色生产的思想，并促进制造业信息化发展。本文主要针对供应链上下游企业包括

设计商、原料供应商、生产商、分销商和物流配送商等进行搜索、评价及组合，

通过构造由层次分析法和粗糙集相结合的综合评价模型，完成对单个供应链节点

的初步筛选，然后建立多目标组合优化模型，经改进蝙蝠算法模拟，实现供应链

整体最优的组合，验证了算法改进的有效性。本文的主要研究内容包括：

首先，论述了供应链调度及云制造相关理论。分析了云制造产生的背景、服

务模式及特点，为建立供应链协同调度模型奠定了基础，将云制造与一般供应链

进行比较，体现出云制造供应链的灵活多变性。鉴于云制造供应链任务的复杂性，

提出了对云制造任务进行分解的原则，为资源搜索奠定基础。

其次，阐明了云制造供应链节点选择的步骤，首先基于语义进行简单的预筛

选，然后，应用层次分析法和粗糙集理论相结合的综合评判法对供应链单个节点

进行初步选择，综合评判法结合了专家意见和客观数据，能够较准确地对庞大资

源库中的企业节点进行排序，为供应链节点进入最终评选提供依据，然后通过多

目标组合优化模型对供应链整体进行组合。

最后，鉴于云制造资源和任务的多样性，通过特定指标地选取构造了云制造

供应链组合多目标优化模型，从多角度、多指标考虑供应链组合优化问题，同时

将遗传算法引入到蝙蝠算法中，通过改进的蝙蝠算法对模型进行求解，结合实例

模拟，在云平台下挖掘出供应链在时间、质量、成本、可靠性、连接成本等方面

综合最优的改善方案，实现云平台下供应链企业节点选择及组合优化。

关键词：云制造粗糙集层次分析法供应链组合蝙蝠算法

ABSTRACT

Along with the quickly development of information technology, the competition

between enterprises in marketing share is increasingly fierce. In order to improve

enterprise strength, the optimization of supply chain is not confined to the

optimization of single enterprise, but more and more inclined to build joint enterprises.

Enterprises attempt to realize the goal of mutual benefit and win-win results through

collaboration between different businesses. Cloud manufacturing (CMfg) provides a

good development platform for the docking of supply chain online and offline

transactions. The emergence of CMfg expanded resource search channel and

increased resource type and quantity. With the continuous development of Internet,

the concept of CMfg is put forward and the platform is gradually perfect. Cloud

manufacturing and Internet of things, as the representative of advanced manufacturing

model, are becoming a research hot spot. CMfg will be the development trend of

China’s manufacturing in the future. To study the combination of cloud manufacturing

and supply chain is of great significance.

The environment of CMfg is so complex that how to efficiently select

appropriate supply chain partners is crucial. Supply chain nodes searching and

combinatorial optimization system is presented in this paper. In the huge CMfg

resources platform, it can realize the resource allocation and improve the efficiency of

resources search. The platform realizes the efficient utilization of idle resources,

embodies the thought of green manufacturing and promotes the development of

manufacturing network. In this paper, supply chain enterprises include designers, raw

material suppliers, manufacturers, distributors and logistics distribution business. The

comprehensive evaluation model based on rough set and analytic hierarchy process

(AHP) implemented the preliminary screening of supply chain single node, then the

multi-objective combinatorial optimization model is set up. Applying the improved

bat algorithm (BA) simulated the model and achieved optimal combination of supply

chain as a whole. The validity of the improved algorithm is proved. The main research

contents include:

Firstly, research cloud manufacturing and supply chain scheduling related theory.

In order to establish supply chain scheduling model, it analyzes CMfg background,

characteristics and service mode. Compared with general supply chain, the CMfg

supply chain is more flexible. Because of complexity of cloud manufacturing tasks,

the principle of task decomposition is put forward, laying the groundwork for the

resource search.

Secondly, it illustrates CMfg supply chain selection steps, that is, simple

preliminary screening supply chain based on semantic in the first place, then using the

comprehensive evaluation model based on rough set and AHP to select each supply

chain node, finally finish the combination of the CMfg supply chain through the

multi-objective optimization model. Comprehensive evaluation model is a

combination of expert opinion and objective data, and it can accurately make an order

of enterprise nodes in a various resources pool, which provides the basis for the

supply chain node into final selection.

Finally, in view of diversity of resources and tasks of cloud manufacturing, the

CMfg supply chain multi-objective optimization model is established based on

selected indicators. It considered the optimization problem from multiple perspectives.

At the same time, to improve BA the genetic algorithm (GA) is introduced into BA.

Through specific examples simulation, the model is solved by the improved BA about

the indicators of time, quality, cost, reliability and connecting cost under the cloud

platform. The validity of the improved BA is proved. Supply chain node selection and

combinatorial optimization is finished under cloud manufacturing platform.

Key Word: cloud manufacturing, rough set, analytic hierarchy

process, supply chain combination, Bat Algorithm

中文摘要

ABSTRACT

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

1.1 研究背景 ...................................................... 1

1.1.1 制造业的发展趋势 ........................................... 1

1.1.2 云制造的兴起 ............................................... 2

1.2 关键内容的研究现状 ............................................ 3

1.2.1 云制造的研究现状 ........................................... 3

1.2.2 粗糙集方法和层次分析法的研究现状 ........................... 4

1.2.3 供应链组合优化的研究现状 ................................... 5

1.3 研究目的及意义 ................................................ 5

1.4 论文研究内容及组织架构 ........................................ 7

1.4.1 研究的创新点 ............................................... 7

1.4.2 论文组织架构 ............................................... 7

第二章相关概念及理论研究 ........................................... 9

2.1 云计算到云制造的过度 .......................................... 9

2.1.1 云计算概述 ................................................. 9

2.1.2 云制造的概念 ............................................... 9

2.1.3 云制造与云计算的区别 ...................................... 10

2.2 云制造的服务模式及特点 ....................................... 10

2.3 云制造与供应链相结合 ......................................... 12

2.3.1 云制造供应链 .............................................. 12

2.3.2 云制造供应链与已有供应链的区别 ............................ 13

2.4 多目标组合优化的相关理论 ..................................... 14

2.5 多目标优化问题指标预处理 ..................................... 15

2.6 本章小结 ..................................................... 16

第三章云制造供应链选择步骤及单节点初选 ............................ 17

3.1 云制造供应链搜索平台的搭建及资源选择步骤 ..................... 17

3.1.1 云制造供应链实现框架 ...................................... 17

3.1.2 云制造供应链资源配置的步骤及指标选取 ...................... 19

3.2 基于关键字的云制造供应链预筛选 ............................... 21

3.2.1 任务分解 .................................................. 22

3.2.2 云制造供应链预筛选 ........................................ 22

3.3 基于粗糙集和层次分析法的供应链节点初步选择 ................... 23

3.3.1 层次分析法及使用步骤 ...................................... 23

3.3.2 粗糙集理论相关概念及权重确定 .............................. 24

3.3.3 综合评判法及其步骤 ........................................ 25

3.4 综合评价法应用实例分析 ....................................... 26

3.4.1 基于层次分析法主观评判矩阵的构造 .......................... 27

3.4.2 粗糙集理论构造客观评判矩阵 ................................ 28

3.4.3 综合判断矩阵的构建 ........................................ 28

3.4.4 计算制造商的评价值 ........................................ 29

3.5 本章小结 ..................................................... 29

第四章基于蝙蝠算法的多目标组合优化模型建立 ........................ 30

4.1 蝙蝠算法介绍 ................................................. 30

4.1.1 蝙蝠算法更新策略 .......................................... 31

4.1.2 蝙蝠算法流程 .............................................. 31

4.2 遗传算法介绍 ................................................. 34

4.2.1 遗传算法的产生 ............................................ 34

4.2.2 遗传算法编码方法 .......................................... 34

4.2.3 选择操作 .................................................. 34

4.2.4 交叉操作 .................................................. 35

4.2.5 变异操作 .................................................. 35

4.3 蝙蝠算法与遗传算法结合 ....................................... 36

4.4 供应链组合优化模型指标介绍 ................................... 37

4.5 云制造供应链多目标组合优化模型建立 ........................... 38

4.5.1 目标函数的确定 ............................................ 39

4.5.2 约束条件的设置 ............................................ 40

4.6 本章小结 ..................................................... 41

第五章云制造供应链组合优化调度实例模拟研究 ........................ 42

5.1 问题描述 ..................................................... 42

5.2 初始种群的产生及任务编码 ..................................... 43

5.3 供应链组合的

fitness

函数 ...................................... 44

5.4 算法参数设置 ................................................. 44

5.5 云制造供应链组合优化结果分析 ................................. 45

5.6 本章小结 ..................................................... 47

第六章总结与展望 .................................................. 48

6.1 全文总结 ..................................................... 48

6.2 研究展望 ..................................................... 48

参考文献 ........................................................... 50

在读期间公开发表的论文和承担科研项目及取得成果 ..................... 54

致谢 .............................................................. 55

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摘要：

摘要随着信息化技术的迅速发展，企业间对于市场份额地争夺越来越激烈，为了提升企业实力，企业越来越倾向于联合上、下游企业构建供应链，企图通过企业之间的协同合作，实现互利共赢的目标。云制造为供应链线上、线下业务的对接提供了一个良好地发展平台，增加了资源种类和数量。目前，随着互联网的不断发展，云制造概念地提出及云平台模式的逐渐完善，以云制造、物联网等为代表的网络制造成为关注焦点，我们国家紧跟现代化制造步伐，考虑将云制造尽快应用到实际中，本文将云制造与供应链结合起来进行研究具有重要意义。云制造业务繁杂，从中高效地选择出符合要求的供应链伙伴至关重要。本文建立了供应链节点搜索及组合优化体系，能够在庞大的云制...

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