LAPSE:2025.0169
Published Article
LAPSE:2025.0169
Data-Driven Chance-Constrained Mixed Integer Nonlinear Bi-level Optimisation Via Copulas: Application To Integrated Planning And Scheduling Problems
June 27, 2025
Abstract
Planning and scheduling are integral components of process supply chains. The presence of data correlation, particularly multivariate demand data dependency, can pose significant challenges to the decision-making process. This necessitates the consideration of dependency structures inherent in the underlying data to generate good-quality, feasible solutions to optimisation problems such as planning and scheduling. This work proposes a chance-constrained optimisation framework integrated with copulas, a non-parametric data estimation technique to forecast uncertain demand levels in accordance with specified risk thresholds in the context of a planning and scheduling problem. We focus on the integrated planning and scheduling problem following a bi-level optimisation formulation. The estimated demand forecasts are subsequently utilised within the Data-driven Optimisation of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework to solve the integrated optimisation problem, and derive decisions with guaranteed demand satisfaction rates. Computational experiments demonstrate that our proposed copula-based chance-constrained optimisation framework can incorporate demand correlation and achieve higher joint demand satisfaction rate, lower total costs with higher efficiency.
Planning and scheduling are integral components of process supply chains. The presence of data correlation, particularly multivariate demand data dependency, can pose significant challenges to the decision-making process. This necessitates the consideration of dependency structures inherent in the underlying data to generate good-quality, feasible solutions to optimisation problems such as planning and scheduling. This work proposes a chance-constrained optimisation framework integrated with copulas, a non-parametric data estimation technique to forecast uncertain demand levels in accordance with specified risk thresholds in the context of a planning and scheduling problem. We focus on the integrated planning and scheduling problem following a bi-level optimisation formulation. The estimated demand forecasts are subsequently utilised within the Data-driven Optimisation of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework to solve the integrated optimisation problem, and derive decisions with guaranteed demand satisfaction rates. Computational experiments demonstrate that our proposed copula-based chance-constrained optimisation framework can incorporate demand correlation and achieve higher joint demand satisfaction rate, lower total costs with higher efficiency.
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Keywords
Bi-level Optimization, Copula Theory, Data-driven optimization, Derivative Free Optimization, Planning & Scheduling
Subject
Suggested Citation
Johnn SN, Nikkhah H, Tsai ML, Avraamidou S, Beykal B, Charitopoulos VM. Data-Driven Chance-Constrained Mixed Integer Nonlinear Bi-level Optimisation Via Copulas: Application To Integrated Planning And Scheduling Problems. Systems and Control Transactions 4:117-122 (2025) https://doi.org/10.69997/sct.169891
Author Affiliations
Johnn SN: University College London, Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, London, WC1E 7JE, UK
Nikkhah H: University of Connecticut, Department of Chemical & Biomolecular Engineering, Storrs, CT, USA; University of Connecticut, Center for Clean Energy Engineering, Storrs, CT, USA
Tsai ML: University of Wisconsin-Madison, Department of Chemical & Biological Engineering, Madison, WI, USA
Avraamidou S: University of Wisconsin-Madison, Department of Chemical & Biological Engineering, Madison, WI, USA
Beykal B: University of Connecticut, Department of Chemical & Biomolecular Engineering, Storrs, CT, USA; University of Connecticut, Center for Clean Energy Engineering, Storrs, CT, USA
Charitopoulos VM: University College London, Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, London, WC1E 7JE, UK
Nikkhah H: University of Connecticut, Department of Chemical & Biomolecular Engineering, Storrs, CT, USA; University of Connecticut, Center for Clean Energy Engineering, Storrs, CT, USA
Tsai ML: University of Wisconsin-Madison, Department of Chemical & Biological Engineering, Madison, WI, USA
Avraamidou S: University of Wisconsin-Madison, Department of Chemical & Biological Engineering, Madison, WI, USA
Beykal B: University of Connecticut, Department of Chemical & Biomolecular Engineering, Storrs, CT, USA; University of Connecticut, Center for Clean Energy Engineering, Storrs, CT, USA
Charitopoulos VM: University College London, Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, London, WC1E 7JE, UK
Journal Name
Systems and Control Transactions
Volume
4
First Page
117
Last Page
122
Year
2025
Publication Date
2025-07-01
Version Comments
Original Submission
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PII: 0117-0122-1280-SCT-4-2025, Publication Type: Journal Article
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LAPSE:2025.0169
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https://doi.org/10.69997/sct.169891
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Jun 27, 2025
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References Cited
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