Code and Data Repository for Approximate Resolution of Stochastic Choice-based Discrete Planning

This repository provides data for the problem and code for the proposed sampling-based Benders decomposition (SBBD) algorthm and the benchmarking solution approaches.

Authors

Publication Details

Journal
INFORMS journal on computing
Published
2025-02-18
DOI
https://doi.org/10.1287/ijoc.2024.0694.cd
Citations
1
Primary Topic
Simulation Techniques and Applications
Type
article
Field-Weighted Citation Impact
2.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Code and Data Repository for Approximate Resolution of Stochastic Choice-based Discrete Planning

Jiajie Zhang, Gerardo Berbeglia, Yun Hui Lin
1 citations
INFORMS journal on computing
Simulation Techniques and Applications
2.11
article

Code and Data Repository for Approximate Resolution of Stochastic Choice-based Discrete Planning

Jiajie Zhang, Gerardo Berbeglia, Yun Hui Lin
article en
1 citations

Abstract

This repository provides data for the problem and code for the proposed sampling-based Benders decomposition (SBBD) algorthm and the benchmarking solution approaches.

INFORMS journal on computing
Openalex Percentile: Top 16%
Simulation Techniques and Applications
2.11
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Code and Data Repository for Approximate Resolution of Stochastic Choice-based Discrete Planning — Jiajie Zhang, Gerardo Berbeglia, et al. · INFORMS journal on computing (2025) | TGRS Research Map | TGRS