2026-10-12T02:32:05Z https://www.scidb.cn/oai

10.57760/sciencedb.0122t 2026-09-10T14:05:40Z

2026-09-10 Computational Materials for Attraction Alliance Operations under Weekday--Holiday Demand Uncertainty: A Double-Ball Wasserstein Distributionally Robust Optimization Model doi:10.57760/sciencedb.0122t en This dataset provides the computational materials supporting the numerical experiments of the manuscript "Attraction Alliance Operations under Weekday--Holiday Demand Uncertainty: A Double-Ball Wasserstein Distributionally Robust Optimization Model".The repository contains the Python implementations of the proposed distributionally robust optimization models, the branch-and-Benders-cut algorithm, acceleration strategies, and parameter sensitivity experiments.The provided files include model implementations, algorithm implementations, processed experimental data, generated demand scenarios, attraction parameters, and experiment scripts.The original ticketing data used to calibrate the experiments were obtained from participating tourism attractions and cannot be publicly released due to confidentiality agreements. The dataset provided here contains the processed parameters and computational materials required to reproduce the reported numerical experiments. Distributionally robust optimization; Branch-and-Benders-cut algorithm; Tourism attraction alliance Linspace Chan PUBLIC https://api.github.com/licenses/cc0-1.0 dataset Science Data Bank