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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Data-Driven Chance-Constrained Mixed Integer Nonlinear Bi-level Optimisation Via Copulas: Application To Integrated

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Summary
This summary is machine-generated.

This study introduces a copula-based framework to manage supply chain planning and scheduling challenges caused by demand correlation. It ensures higher demand satisfaction and lower costs by optimizing decisions under uncertainty.

Keywords:
Bi-level OptimizationCopula TheoryData-driven optimizationDerivative Free OptimizationPlanning & Scheduling

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Data Science

Background:

  • Supply chain planning and scheduling face challenges due to correlated multivariate demand data.
  • Accurate demand forecasting considering data dependencies is crucial for effective optimization.
  • Existing methods may not adequately address the complexities of demand correlation in decision-making.

Purpose of the Study:

  • To propose a chance-constrained optimization framework integrated with copulas for planning and scheduling problems.
  • To forecast uncertain demand levels while considering specified risk thresholds and data dependencies.
  • To improve the quality and feasibility of solutions for integrated planning and scheduling.

Main Methods:

  • Utilized copulas, a non-parametric technique, for demand forecasting under uncertainty.
  • Employed a bi-level optimization formulation for the integrated planning and scheduling problem.
  • Integrated demand forecasts into the Data-driven Optimisation of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework.

Main Results:

  • The proposed framework successfully incorporates demand correlation into the optimization process.
  • Achieved a higher joint demand satisfaction rate compared to traditional methods.
  • Demonstrated lower total costs and increased efficiency in computational experiments.

Conclusions:

  • Copula-based chance-constrained optimization is effective for handling demand correlation in supply chains.
  • The framework provides a robust approach to decision-making under demand uncertainty.
  • This method enhances supply chain resilience and economic performance.