【original】optimization of industrial distribution based on small world networks

October 2011

Yibo YangBeijing University of Technology

Optimization of Industrial Distribution Based on Small-world Networks

—A Case Study of Chaoyang District, Beijing, China

2 Beijing University of Technology

Outline

Introduction

○ Networks is everywhere

Small-world Network and Application

○ Evolving of Small-world Networks

○ Application in Industrial Distribution

○ Proof of Feasibility

A Case Study of Chaoyang District, Beijing, China

○ Introduction to Chaoyang District

○ Industrial Networks Structure Networks

○ Critical Chain Analysis

○ Distribution Networks Buffer

○ Brief AON Chart


Introduction

Social Networks Copyright: www.pixmule.com

Food Web Copyright: www.tutorvista.com

World Wide WebCopyright: http://marlonacosta.com/

• Networks is everywhereRelated to statistical physics, applied mathematics, information

engineering, sociology, biology, management science, etc.


Small-world Network Model and Application

• Small-world Network Model○ Systems can be highly clustered, like regular lattices.

○ Systems have small characteristic path lengths, like random graphs.

The structure of Small-world networkCopyright: www.urbagram.net


• Evolving of Small-world Networks

○ Growth: the network initially has m0 nodes and e0 edges, each time

adding 1 new nodes and m edges.

○ Preferential attachment: selected M nodes (M ≥ m) as a new small

world from the existing network randomly. Newly joined nodes according to the priority of connection probability to select connect

to the local world's m nodes.

○ Ki: vertex degree of the i node

○ SW: Small-world Networks composed of new selected M nodes


• Application in Industrial Distribution ○ Growth: after years of development, the city has a relatively stable

industrial system which formed by a relatively independent industry.

○ Preferential attachment: when a new industry appears, it will give priority to the related industries of the city; then, In the following time, Industrial systems will follow a similar law of evolution.

A Case of Industry Evolution


• Proof of Feasibility:

• Characteristic of industries linked

adaptability

transitivity

imbalance

path dependence

reactivity jump

exogeneity


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• System Dynamics Equations

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Evolution of the industrial system of urban area has a characteristic of scale-free


• Chaoyang District is a district of northeastern Beijing, China, occupying 475 square kilometers urban area. It is the largest district

• A total population of

3,642,000 in 2005

Introduction to Chaoyang District

• Commercial center

○ over 3,000 foreign companies

○ 112 Global 500 Companies

○ 47 headquarters of multinational companies Chaoyang-World

Copyright: http://www.bing.com/mapsChaoyang-beijing

Copyright: WWW. wikipedia.org/wki/Chaoyang_District,_Beijing`


Industrial Networks Structure Overall

High-tech manufacturing industry Public software services industry


• Overall structure shows the feature of “small world networks”

• Obvious characteristics of scale-free networks

○ The distribution of each node (degree) is uneven

• Degree of majority of nodes is small

• Degree of small classes of nodes is large

○ The characteristics of the local concentration• High-tech manufacturing industry

• Public software services industry


Small Sub-world Networks 1

• High-tech manufacturing industry

Bb2 : food manufacturing industry Bb4 : tobacco industryBb5 : textile industry Bb9 : furniture manufacturing industryBb13: petroleum ,nuclear fuel industry Bb18: plastic products industry Bb19 : non-metallic mineral industry Bb21 :non-ferrous metal refining industryBb22: metal products industry Bb25 :automotive industryBb27 :high-tech manufacturing and software services industry Bb29: handicrafts and other manufacturing Ca6 :Loading, Unloading and Other Transport Services


Node ID Clustering Coefficients Nrm Degree

Bb2 —— 50.746

Bb4 —— 44.776

Bb5 1.000 46.269

Bb9 0.500 52.239

Bb13 1.000 40.299

Bb18 0.667 49.254

Bb19 0.667 46.269

Bb21 0.800 46.269

Bb22 0.667 49.254

Bb25 1.000 44.776

Bb27 0.429 47.761

Bb29 0.600 46.269

Ca6 0.242 58.209


Small Sub-world Networks 2

Cb2： computer services industryCb3： software industryBc1： electricity, heat production and supply industryCa1： transport industryCe1 ：BankingCe3 ：insurance

• Public software services industry


Node ID Clustering Coefficients Nrm Degree

Bc1 1.000 82.090

Ca1 1.000 79.104

Cb2 1.000 82.090

Cb3 1.000 83.582Ce1 1.000 98.507

Ce3 1.000 98.507

Both high-tech manufacturing industry and public software services industry are all in their own “small world”


Critical Industrial Chain Analysis


Industrial Distribution Networks Buffer


Brief AON Chart

13

8

7

15 5 6

17

4

1 12 14

2 11

16

3 10

9

18


References

• Aldrich, H. E., & Kim, P. H. (2007). Small worlds, infinite possibilities? How social networks affect entrepreneurial team formation and search. Strategic Entrepreneurship Journal, 1(1-2), 147-165.

• ChinaTouristMaps. (2011). Map of Chaoyang District Beijing. from http://www.chinatouristmaps.com/provinces/beijing/chaoyang-district.html

• Engel, J. S., & Del-Palacio, I. (2009). Global networks of clusters of innovation: Accelerating the innovation process. Business Horizons, 52(5), 493-503.

• Frommer, I., & Pundoor, G. (2003). Small-worlds: A review of recent books. Networks, 41(3), 174-180.

• Jerome S. Engel, Itxaso del-palacio. Global Networks of Clusters of Innovation: Accelerating the Innovation Process[J].Business Horizons,2009,25(5):493-503

• Kristian Moller, Senja Svahn How to Influence the Birth of New Business Fields [J].Industrial Marketing Management,2009(38):450-458

• Möller, K., & Svahn, S. (2009). How to influence the birth of new business fields —Network perspective. Industrial Marketing Management, 38(4), 450-458.

• Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of `small-world' networks. nature, 393(6684), 440-442.


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