Abstract
With the development of the economy, the demands for food quality have gradually increased. Cold chain as a temperature-controlled supply chain has emerged to ensure the fresh goods. In recent years, the establishment and improvement of the cold chain logistics, as complex system engineering, is very important. While the cold chain logistics system has been vigorously developing, the common problems we face include: selection of suitable location for cold chain distribution center as well as optimization of distribution route. In this paper, we mainly focus on how to optimize the cold chain logistics distribution for a fresh processing factory M. We propose the methods to optimize the cold chain logistics distribution for M based on linear programming model. First, we adopt location set covering problem (LSCP) to model the location problem of cold chain logistics distribution center for Factory M, and use zero-one programming to solve the distribution center location. Then, we use transshipment problem, which is one of the transportation problem, to model and solve the distribution route for M. Experiments are conducted on IBM ILOG CPLEX Optimization platform, and the suitable plan of logistics distribution for Factory M is figured out at the end. The experiment result shows that the optimal solution using our optimization method can save about 15% of transportation cost compared to the result without any planning.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018 |
| Editors | Bing Xu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 593-597 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538645086 |
| DOIs | |
| Publication status | Published - 2018 Dec 14 |
| Event | 3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018 - Chongqing, China Duration: 2018 Oct 12 → 2018 Oct 14 |
Publication series
| Name | Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018 |
|---|
Other
| Other | 3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018 |
|---|---|
| Country | China |
| City | Chongqing |
| Period | 18-10-12 → 18-10-14 |
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All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Control and Optimization
- Instrumentation
Cite this
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Cold Chain Logistics Distribution Optimization for Fresh Processing Factory Based on Linear Programming Model. / Sun, Xue; Wu, Chao-Chin; Chen, Liang-Rui.
Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2018. p. 593-597 8577759 (Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Cold Chain Logistics Distribution Optimization for Fresh Processing Factory Based on Linear Programming Model
AU - Sun, Xue
AU - Wu, Chao-Chin
AU - Chen, Liang-Rui
PY - 2018/12/14
Y1 - 2018/12/14
N2 - With the development of the economy, the demands for food quality have gradually increased. Cold chain as a temperature-controlled supply chain has emerged to ensure the fresh goods. In recent years, the establishment and improvement of the cold chain logistics, as complex system engineering, is very important. While the cold chain logistics system has been vigorously developing, the common problems we face include: selection of suitable location for cold chain distribution center as well as optimization of distribution route. In this paper, we mainly focus on how to optimize the cold chain logistics distribution for a fresh processing factory M. We propose the methods to optimize the cold chain logistics distribution for M based on linear programming model. First, we adopt location set covering problem (LSCP) to model the location problem of cold chain logistics distribution center for Factory M, and use zero-one programming to solve the distribution center location. Then, we use transshipment problem, which is one of the transportation problem, to model and solve the distribution route for M. Experiments are conducted on IBM ILOG CPLEX Optimization platform, and the suitable plan of logistics distribution for Factory M is figured out at the end. The experiment result shows that the optimal solution using our optimization method can save about 15% of transportation cost compared to the result without any planning.
AB - With the development of the economy, the demands for food quality have gradually increased. Cold chain as a temperature-controlled supply chain has emerged to ensure the fresh goods. In recent years, the establishment and improvement of the cold chain logistics, as complex system engineering, is very important. While the cold chain logistics system has been vigorously developing, the common problems we face include: selection of suitable location for cold chain distribution center as well as optimization of distribution route. In this paper, we mainly focus on how to optimize the cold chain logistics distribution for a fresh processing factory M. We propose the methods to optimize the cold chain logistics distribution for M based on linear programming model. First, we adopt location set covering problem (LSCP) to model the location problem of cold chain logistics distribution center for Factory M, and use zero-one programming to solve the distribution center location. Then, we use transshipment problem, which is one of the transportation problem, to model and solve the distribution route for M. Experiments are conducted on IBM ILOG CPLEX Optimization platform, and the suitable plan of logistics distribution for Factory M is figured out at the end. The experiment result shows that the optimal solution using our optimization method can save about 15% of transportation cost compared to the result without any planning.
UR - http://www.scopus.com/inward/record.url?scp=85060395842&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060395842&partnerID=8YFLogxK
U2 - 10.1109/IAEAC.2018.8577759
DO - 10.1109/IAEAC.2018.8577759
M3 - Conference contribution
AN - SCOPUS:85060395842
T3 - Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018
SP - 593
EP - 597
BT - Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2018
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
ER -