Improved augmented Lagrangian coordination for optimizing supply chain configuration with multiple sharing elements in industrial cluster

Date13 May 2019
DOIhttps://doi.org/10.1108/IMDS-06-2018-0253
Pages743-773
Published date13 May 2019
AuthorDuxian Nie,Ting Qu,Yang Liu,Congdong Li,G.Q. Huang
Subject MatterInformation & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems
Improved augmented Lagrangian
coordination for optimizing
supply chain configuration with
multiple sharing elements in
industrial cluster
Duxian Nie
College of Mathematics and Informatics,
South China Agricultural University, Guangzhou, China
Ting Qu
School of Intelligent Systems Science and Engineering,
Jinan University (Zhuhai Campus), Zhuhai, China and
Institute of Physical Internet, Jinan University (Zhuhai Campus), Zhuhai, China
Yang Liu
School of Intelligent Systems Science and Engineering,
Jinan University (Zhuhai Campus), Zhuhai, China;
Institute of Physical Internet,
Jinan University (Zhuhai Campus), Zhuhai, China and
Department of Management and Engineering,
Linköping University, Linköping, Sweden
Congdong Li
Institute of Physical Internet,
Jinan University (Zhuhai Campus), Zhuhai, China, and
G.Q. Huang
Institute of Physical Internet,
Jinan University (Zhuhai Campus), Zhuhai, China and
Department of Industrial and Manufacturing Systems Engineering,
University of Hong Kong, Pokfulam, Hong Kong
Abstract
Purpose The purpose of this paper is to study various combination forms of the three basic sharing
elements (i.e. orders sharing, manufacturers capacity sharing and suppliers capacity sharing) in the cluster
supply chain (CSC), formulate a distributed model to protect enterprisesdecision privacy and seek to develop
an effective method for solving the distributed complex model.
Design/methodology/approach A distributed assembly cluster supply chain configuration
(ACSCC) model is formulated. An improved augmented Lagrangian coordination (ALC) is proposed and
used to solve the ACSCC model. A series of experiments are conducted to validate the improved ALC and
the model.
Industrial Management & Data
Systems
Vol. 119 No. 4, 2019
pp. 743-773
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-06-2018-0253
Received 22 June 2018
Revised 25 October 2018
Accepted 21 January 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
This work was supported by the National Natural Science Foundation of China (51875251, 61473093),
China Scholarship Council (201807630004), Guangdong Natural Science Foundation (China)
(2016A030311041, 2017A030313401), Blue Fire Project (Huizhou) Industry-University-Research Joint
Innovation Fund of Ministry of Education (China) (CXZJHZ201722) and the Fundamental Research
Funds for the Central Universities (China) (11618401).
743
Improved
augmented
Lagrangian
coordination
Findings Two major findings are obtained. First, the market orders quantity change and the sales price of
the product have a great impact on both the optimal results of the ACSCC and the cooperative strategy,
especially, when the market order increases sharply, enterprises have to adopt multiple cooperative strategies
to complete the order; meanwhile, the lower sales price of the product helps independent suppliers to get more
orders. Second, the efficiency and computational accuracy of the improved ALC method are validated as
compared to the centralized ALC and Lingo11.
Research limitations/implications This paper formulated the singl e-period ACSCC model under
certain assumptions , yet a multi-period ACSC C model is to be developed , a more comprehensive
investigation of the relationships among combination forms is t o be extended further and a rigid proof of
the improved ALC is necess ary.
Practical implications Enterprises in the industrial cluster should adopt different cooperative strategies
in terms of the market orders quantity change and the sales price of the product.
Social implications The proposed various combination forms of sharing elements and the formulated
ACSCC model provide guidance to managers in the industrial cluster to choose the proper policy.
Originality/value This research studies various combination forms of the three basic sharing elements in
the CSC. A distributed ACSCC model has been established considering simultaneously multiple sharing
elements. An improved ALC is presented and applied to the ACSCC problem.
Keywords Supplier selection, Industrial cluster, Improved augmented Lagrangian coordination,
Supply chain configuration
Paper type Research paper
1. Introduction
With the global economic slowdown, falling commodity prices, the implementation of energy-
saving emission reduction standards, early excessive credit growth and the negative effect of
geopolitical unrest and other factors, the global market competition intensifies day by day (Luo,
2007). To achieve profitability, enterprises must not only improve product quality, reduce
production cost and energy consumption, but also face up with the ever-changing market
demand. However, since most small and medium enterprisesproduction technology is
backward, facilities are in small scale, varieties of products are few, funds are in shortage and
ability to acquire information is limited, it is difficult to rely on their own ability to accomplish
these tasks and adapt to dramatic changes in market environment. Industrial cluster, which is
adopted by many countries as a new type of economic development model (Porter, 2000),
provides a natural platform for small and medium enterprises to cooperate with each other
(Roelandt and Den Hertog, 1999). Cluster supply chain (CSC) is a complex network system
formed by the coupling of supply chain and industrial cluster, which has the characteristics of
regional proximity, industrial correlation and cooperation (Li et al., 2012). CSC can help small- and
medium-sized enterprises to reduce their production energy consumption, improve enterprises
ability to resist risks and contribute to achieving profitability through resource sharing (such as
order sharing), etc. For example, enterprises of the assembly supply chain in an industrial cluster
coordinate each other more easily and conveniently than those of the traditional assembly supply
chain because they have clustering characteristics (e.g. geographical proximity and mutual
cooperation) (Qu et al., 2017). Therefore, the research on the operation of an industrial cluster,
especially the CSC operation, is of great significance to the development of regional economy.
Supply chain configuration (SCC) proposed by Graves and Willems (2001) is a complex
overall decision-makingprocess oriented to the wholesupply chain operation.The aim of SCC
is to optimize certain performance indicators (such as time, cost and quality) of the supply
chain by configuring each supply chain node (Huang and Qu, 2008). According to the
definitionof SCC, cluster supply chainconfiguration (CSCC)was proposed in the literature(Qu
et al., 2015). Customer demand diversity and hig h-frequency change make the production of
cluster system based on orders sharing possess the characteristics of natural dynamic and
short life cycle, which is an important characteristic of CSC. Thus,oriented to the order, it is a
crucialstage of the CSC operation to promptlyand effectively configureand reconfigure a CSC.
The CSCC decision process is highly complicated because of the dynamic cooperation
among single supply chains and the characteristic of the short life cycle of CSC. Therefore,
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