3.9 Article

Automated Calibration of Farm-Sale Mixed Linear Programming Models using Bi-Level Programming

Journal

GERMAN JOURNAL OF AGRICULTURAL ECONOMICS
Volume 70, Issue 3, Pages 165-181

Publisher

DEUTSCHER FACHVERLAG GMBH
DOI: 10.30430/70.2021.3.165-181

Keywords

linear programming; mixed linear programming; calibration; bi-level programming; farm-scale model

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The study introduces a method for calibrating Linear and Mixed Integer Programs using a bi-level estimator, which effectively addresses non-convexity issues. Monte-Carlo analysis was conducted to assess the effectiveness of the approach.
We calibrate Linear and Mixed Integer Programs with a bi-level estimator, minimizing under First-order -conditions (FOC) conditions a penalty function considering the calibration fit and deviations from given parameters. To deal with non-convexity, a heuristic generates restart points from current best-fit parameters and their means. Monte-Carlo analysis assesses the approach by drawing parameters for a model optimizing acreages under maximal crop shares, a land balance and annual plus intra-annual labour constraints; a variant comprises integer based investments. Resulting optimal solutions perturbed by white noise provide calibration targets. The approach recovers the true parameters and thus allows for systematic and automated calibration.

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