TY - JOUR
T1 - A decentralized coordination algorithm for multi-objective linear programming with block angular structure
AU - Sowah Okpoti, Evans
AU - Jeong, In Jae
N1 - Publisher Copyright:
© 2019 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - This article considers linear multi-objective programming problems with block angular structure, which are analogous to multi-disciplinary optimization environments where disciplines must collaborate to achieve a common overall goal. In this decentralized environment, a mechanism to guide locally optimized decision makers’ solutions to a Pareto-optimal solution without sharing the entire local information is developed. The mechanism is based on an augmented Lagrangian approach to generate a solution and is separated into two phases: phase I determines an ideal point for each of the single objectives and phase II searches for a compromise solution starting from a single ideal point. Theoretical results show that the algorithm converges and the solution generated is Pareto optimal. The algorithm’s effectiveness is demonstrated via an illustrative example and a real-world bi-objective re-entrant flow-shop production planning problem. The real-world experimental results showed that the decentralized method had an average 50% better performance compared to other centralized methods.
AB - This article considers linear multi-objective programming problems with block angular structure, which are analogous to multi-disciplinary optimization environments where disciplines must collaborate to achieve a common overall goal. In this decentralized environment, a mechanism to guide locally optimized decision makers’ solutions to a Pareto-optimal solution without sharing the entire local information is developed. The mechanism is based on an augmented Lagrangian approach to generate a solution and is separated into two phases: phase I determines an ideal point for each of the single objectives and phase II searches for a compromise solution starting from a single ideal point. Theoretical results show that the algorithm converges and the solution generated is Pareto optimal. The algorithm’s effectiveness is demonstrated via an illustrative example and a real-world bi-objective re-entrant flow-shop production planning problem. The real-world experimental results showed that the decentralized method had an average 50% better performance compared to other centralized methods.
KW - Collaborative optimization
KW - block angular structure
KW - decentralized coordination
KW - multi-agent
KW - multi-objective linear programming
UR - http://www.scopus.com/inward/record.url?scp=85076918889&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2019.1698035
DO - 10.1080/0305215X.2019.1698035
M3 - Article
AN - SCOPUS:85076918889
VL - 53
SP - 185
EP - 205
JO - Engineering Optimization
JF - Engineering Optimization
SN - 0305-215X
IS - 2
ER -