TY - JOUR
T1 - Optimizing Locations and Scales of Distribution Centers under Uncertainty
AU - Zhen, Lu
AU - Wang, Weirong
AU - Zhuge, Dan
N1 - Funding Information:
Manuscript received September 14, 2015; revised November 28, 2015; accepted January 21, 2016. Date of publication March 8, 2016; date of current version October 16, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 71422007 and Grant 71101087, in part by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and in part by the Shanghai Pujiang Talent Program under Grant 11PJC072. This paper was recommended by Associate Editor S. Bandyopadhyay. (Corresponding author: Lu Zhen.) The authors are with the School of Management, Shanghai University, Shanghai 200444, China (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/11
Y1 - 2017/11
N2 - In supply chain networks of large companies, how to determine the location for establishing distribution centers is an important strategic-level decision problem. This paper proposes a stochastic programming model to determine distribution centers' locations as well as their scales. The objective of the model is to minimize the expected total transportation cost under uncertain demands of customers. An improved particle swarm optimization algorithm and a Lagrangean relaxation-based solution approach are proposed to solve the model. This paper also performs a case study on applying this model to the largest retailer in China. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods.
AB - In supply chain networks of large companies, how to determine the location for establishing distribution centers is an important strategic-level decision problem. This paper proposes a stochastic programming model to determine distribution centers' locations as well as their scales. The objective of the model is to minimize the expected total transportation cost under uncertain demands of customers. An improved particle swarm optimization algorithm and a Lagrangean relaxation-based solution approach are proposed to solve the model. This paper also performs a case study on applying this model to the largest retailer in China. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods.
KW - Distribution centers
KW - facility location
KW - Lagrangean relaxation
KW - particle swarm optimization (PSO)
KW - stochastic programming
UR - https://www.scopus.com/pages/publications/85035749631
U2 - 10.1109/TSMC.2016.2531696
DO - 10.1109/TSMC.2016.2531696
M3 - Journal article
AN - SCOPUS:85035749631
SN - 2168-2216
VL - 47
SP - 2908
EP - 2919
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 11
M1 - 7428960
ER -