Supplier-Receiver Matching Data for Divisible Goods Allocation

A dataset by Vivien Kleinpeter from CIRAD, last updated in April 2026, illustrating a modified Gale-Shapley algorithm for many-to-many matching with divisible goods. The description references a small example with 7 suppliers and 15 receivers, and mentions successful deployment with thousands of entities. The method was developed for studying biomass exchanges in an insular territory to promote a circular economy.

Use Cases

• Testing matching algorithm performance based on the described bipartite graph structure with supplier stocks and receiver needs.
• Modeling biomass or agricultural by-product exchanges based on the described application for circular economy transitions.
• Benchmarking computational efficiency for large-scale matching problems based on the mention of deployments with thousands of suppliers and receivers.
• Studying preference-based allocation mechanisms based on the described use of ranked preference lists and cost-weighted edges.

Strengths

• Method is described as successfully deployed with several thousands of suppliers and receivers.
• Algorithm is designed to produce identical matching results irrespective of which side proposes first.
• Includes a concrete example case with 7 suppliers and 15 receivers for illustration.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

CIRAD Harvested Collection, author Vivien Kleinpeter.

Collection Method

Likely generated via simulation or from a case study to illustrate the proposed matching algorithm.

Time Range

null

Freshness

Last updated 2026-04-20 15:42:46; freshness should be verified.

Geography

Description mentions application within an insular territory, but specific location is not provided.