TY - GEN
T1 - Decentralized Supply Chain Optimization via Swarm Intelligence
AU - Singh, Karan
AU - Liu, Hsin Ping
AU - Phoa, Frederick Kin Hing
AU - Lin, Shau Ping
AU - Chen-Burger, Yun-Heh Jessica
N1 - Funding Information:
This work is partially supported by the Academia Sinica grant number AS-TP-109-M07 and the Ministry of Science and Technology (Taiwan) grant numbers 107-2118-M-001-011-MY3 and 109-2321-B-001-013.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022/6/26
Y1 - 2022/6/26
N2 - When optimised, supply chains can bring tremendous benefits to all its participants. Supply chains therefore can be framed as a networked optimization problem to which swarm intelligence techniques can be applied. Given recent trends of globalization and e-commerce, we propose a supply chain that uses an open e-commerce business model, where all participants have equal access to the market and are free to trade with each other based on mutually agreed prices and quantities. Based on this model, we improve upon the Particle Swarm Optimization algorithm with constriction coefficient (CPSO), and we demonstrate the use of a new random jump algorithm for consistent and efficient handling of constraint violations. We also develop a new metric called the ‘improvement multiplier’ for comparing the performance of an algorithm when applied to a problem with different configurations.
AB - When optimised, supply chains can bring tremendous benefits to all its participants. Supply chains therefore can be framed as a networked optimization problem to which swarm intelligence techniques can be applied. Given recent trends of globalization and e-commerce, we propose a supply chain that uses an open e-commerce business model, where all participants have equal access to the market and are free to trade with each other based on mutually agreed prices and quantities. Based on this model, we improve upon the Particle Swarm Optimization algorithm with constriction coefficient (CPSO), and we demonstrate the use of a new random jump algorithm for consistent and efficient handling of constraint violations. We also develop a new metric called the ‘improvement multiplier’ for comparing the performance of an algorithm when applied to a problem with different configurations.
KW - E-commerce
KW - Networked optimization problem
KW - Particle Swarm Optimization
KW - Supply chain
KW - Swarm intelligence
UR - http://www.scopus.com/inward/record.url?scp=85134318126&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-09677-8_36
DO - 10.1007/978-3-031-09677-8_36
M3 - Conference contribution
AN - SCOPUS:85134318126
SN - 9783031096761
T3 - Lecture Notes in Computer Science
SP - 432
EP - 441
BT - Advances in Swarm Intelligence. ICSI 2022
A2 - Tan, Ying
A2 - Shi, Yuhui
A2 - Niu, Ben
PB - Springer
T2 - 13th International Conference on Swarm Intelligence 2022
Y2 - 15 July 2022 through 19 July 2022
ER -