TY - GEN
T1 - Distributed Optimization for Supply Chain Planning for Multiple Companies Using Subgradient Method and Consensus Control
AU - Debuchi, Naoto
AU - Nishi, Tatsushi
AU - Liu, Ziang
N1 - Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022
Y1 - 2022
N2 - With recent liberalization and enlarging of trade among companies, it is necessary to generate an optimal supply chain planning by cooperation and coordination of supply chain planning for multiple companies without sharing sensitive information such as costs and profit among competitive companies. A distributed optimization can solve the optimization problems with limited information. A distributed optimization method using subgradient and consensus control methods has been proposed to solve continuous optimization problems. However, conventional distributed optimization methods using subgradient and consensus control methods cannot be applied to the supply chain planning for multiple companies including 0–1 decision variables. In this paper, we propose a new distributed optimization method for solving the supply chain planning problem for multiple companies by subgradient method and consensus control. By branching the cases 0–1 variables, an optimal solution can be obtained by the enumeration. A method to reduce the computational effort has been developed in the proposed method. From numerical experiments, it is confirmed that we can obtain an optimal solution by the reduction of the computation.
AB - With recent liberalization and enlarging of trade among companies, it is necessary to generate an optimal supply chain planning by cooperation and coordination of supply chain planning for multiple companies without sharing sensitive information such as costs and profit among competitive companies. A distributed optimization can solve the optimization problems with limited information. A distributed optimization method using subgradient and consensus control methods has been proposed to solve continuous optimization problems. However, conventional distributed optimization methods using subgradient and consensus control methods cannot be applied to the supply chain planning for multiple companies including 0–1 decision variables. In this paper, we propose a new distributed optimization method for solving the supply chain planning problem for multiple companies by subgradient method and consensus control. By branching the cases 0–1 variables, an optimal solution can be obtained by the enumeration. A method to reduce the computational effort has been developed in the proposed method. From numerical experiments, it is confirmed that we can obtain an optimal solution by the reduction of the computation.
KW - Consensus control
KW - Distributed optimization
KW - Subgradient method
KW - Supply chain planning
UR - http://www.scopus.com/inward/record.url?scp=85138795340&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138795340&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16411-8_27
DO - 10.1007/978-3-031-16411-8_27
M3 - Conference contribution
AN - SCOPUS:85138795340
SN - 9783031164101
T3 - IFIP Advances in Information and Communication Technology
SP - 216
EP - 223
BT - Advances in Production Management Systems. Smart Manufacturing and Logistics Systems
A2 - Kim, Duck Young
A2 - von Cieminski, Gregor
A2 - Romero, David
PB - Springer Science and Business Media Deutschland GmbH
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2022
Y2 - 25 September 2022 through 29 September 2022
ER -