Data-driven coordination of subproblems in enterprise-wide optimization under organizational considerations
File(s)
Published version
Author(s)
van de Berg, Damien
Petsagkourakis, Panagiotis
Shah, Nilay
del Rio-Chanona, Ehecatl Antonio
Type
Journal Article
Abstract
While decomposition techniques in mathematical programming are usually designed for numerical efficiency, coordination problems within enterprise-wide optimization are often limited by organizational rather than numerical considerations. We propose a “data-driven” coordination framework which manages to recover the same optimum as the equivalent centralized formulation while allowing coordinating agents to retain autonomy, privacy, and flexibility over their own objectives, constraints, and variables. This approach updates the coordinated, or shared, variables based on derivative-free optimization (DFO) using only coordinated variables to agent-level optimal subproblem evaluation “data.” We compare the performance of our framework using different DFO solvers (CUATRO, Py-BOBYQA, DIRECT-L, GPyOpt) against conventional distributed optimization (ADMM) on three case studies: collaborative learning, facility location, and multiobjective blending. We show that in low-dimensional and nonconvex subproblems, the exploration-exploitation trade-offs of DFO solvers can be leveraged to converge faster and to a better solution than in distributed optimization.
Date Issued
2023-04
Date Acceptance
2022-11-27
Citation
AIChE Journal, 2023, 69 (4), pp.1-24
ISSN
0001-1541
Publisher
Wiley
Start Page
1
End Page
24
Journal / Book Title
AIChE Journal
Volume
69
Issue
4
Copyright Statement
© 2022 The Authors. AIChE Journal published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000916888700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
ADMM
BENDERS DECOMPOSITION
CHAIN NETWORK DESIGN
coordination
data-driven optimization
DECOMPOSITION ALGORITHM
distributed optimization
Engineering
Engineering, Chemical
expensive black-box
FRAMEWORK
GLOBAL OPTIMIZATION
OPERATIONS
Science & Technology
SEARCH
SUPPLY CHAIN
Technology
UNCERTAINTY
Publication Status
Published
Article Number
e17977
Date Publish Online
2022-12-01