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Updated: May 26, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

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Published on: September 16, 2022

Data-Driven Chance Constrained Mixed Integer Nonlinear Bilevel Optimization via Copulas.

Syu-Ning Johnn1, Hasan Nikkhah2,3, Meng-Lin Tsai4

  • 1Department of Chemical Engineering, The Sargent Centre for Process Systems Engineering, University College London, London WC1E 7JE, U.K.

Industrial & Engineering Chemistry Research
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a data-driven probabilistic framework using chance constrained programming (CCP) and copulas to optimize process supply chains. The approach effectively handles complex demand data, improving decision-making for profitability and responsiveness.

Related Experiment Videos

Last Updated: May 26, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Operations Research
  • Data Science
  • Supply Chain Management

Background:

  • Process supply chains rely on planning and scheduling for profitability and responsiveness.
  • Real-world demand data presents challenges like multivariate dependencies, noise, and uncertainties, complicating optimization.
  • Data-driven optimization is crucial for managing complex data structures and improving decision-making.

Purpose of the Study:

  • To propose a novel data-driven probabilistic framework integrating chance constrained programming (CCP) and copulas.
  • To accurately model variable dependencies and correlations in complex demand data for process supply chains.
  • To enhance decision-making for profitability and responsiveness under demand uncertainty.

Main Methods:

  • Developed a probabilistic framework combining chance constrained programming (CCP) and copulas.
  • Utilized copulas to capture dependency structures among uncertain parameters with correlations.
  • Integrated the framework with the Data-driven Optimization of bilevel Mixed-Integer NOnlinear problems (DOMINO) algorithm.

Main Results:

  • The framework accurately models variable dependencies, even with complex distributions and nontrivial dependencies.
  • Achieved higher joint demand satisfaction rates compared to traditional methods.
  • Demonstrated lower total costs and increased operational efficiency in case studies.

Conclusions:

  • The copula-based chance constrained optimization framework effectively incorporates demand correlation for improved supply chain performance.
  • This data-driven approach enhances decision-making by providing guaranteed demand satisfaction rates.
  • The proposed method offers a robust solution for optimizing planning and scheduling in process supply chains.