Replication data and code for “Processing Location for Cross-Border Perishables with Arrival-Rate–Conserving Conversion: Thresholds, Strategy Frontiers, and a China–US Case” (v1.0)

Published: 22 August 2025| Version 1 | DOI: 10.17632/sdw9rkgf2y.1
Contributor:
QINGZE ZHU

Description

This data article is related to the research article entitled “Processing Location for Cross-Border Perishables with Arrival-Rate–Conserving Conversion: Thresholds, Strategy Frontiers, and a China–US Case”. It consists of solver-ready Python code, calibrated inputs (in a CSV file), and the outputs generated to construct the decision-rule cards, uncertainty bands, and figures in the manuscript. Folder structure: /code, /input, /results, /figures/main, /figures/supplement, /docs. Altering the input CSVs and running the script again regenerates the thresholds and rule cards for lanes/commodities. No proprietary data are included. License: CC BY 4.0.

Files

Steps to reproduce

Environment. Python ≥3.10; packages: pulp, pandas, numpy, matplotlib Install. pip install pulp pandas numpy matplotlib Run. python ./code/state_dependent_model.py --constants ./input/constants.csv --params ./input/parameters_main.csv --scenarios ./input/sensitivity_scenarios.csv --outdir ./results --figdir ./figures Outputs regenerate the ocean and distance thresholds, decision cards, validation (model_validation_results.csv, validation_table.tex), and robustness plots (tornado/Monte-Carlo). Re-calibration. Replace /input/*.csv with new lane data (same headers), rerun.

Institutions

  • University of Wisconsin Madison

Categories

Operations Research, Supply Chain Management, Production Economics, Transportation Economics, Linear Programming, Network Optimization

Licence