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Data-driven coordination of expensive subproblems in enterprise-wide optimization
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  • Damien van de Berg,
  • Panagiotis Petsagkourakis,
  • Nilay Shah,
  • Ehecatl Del Rio-Chanona
Damien van de Berg
Imperial College London

Corresponding Author:[email protected]

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Panagiotis Petsagkourakis
Imperial College London
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Nilay Shah
Imperial College
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Ehecatl Del Rio-Chanona
Imperial College London
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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 multi-objective 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
27 Jun 2022Submitted to AIChE Journal
01 Jul 2022Submission Checks Completed
01 Jul 2022Assigned to Editor
11 Jul 2022Reviewer(s) Assigned
19 Sep 2022Editorial Decision: Revise Major
02 Nov 20221st Revision Received
05 Nov 2022Submission Checks Completed
05 Nov 2022Assigned to Editor
05 Nov 2022Review(s) Completed, Editorial Evaluation Pending
07 Nov 2022Reviewer(s) Assigned
27 Nov 2022Editorial Decision: Accept