Pone.0349445.t019: Pharmaceutical Supply Chain Routing Model Results

A research dataset supporting a novel model for enhancing pharmaceutical supply chain services in the post-pandemic era. The model uses a three-objective mixed integer linear programming approach for a green open vehicle routing problem, considering costs, greenhouse gas emissions, and patient satisfaction. The dataset, shared by Zahra Samadi Bahrami on figshare, contains results verified through real-world applications.

Use Cases

• Benchmarking vehicle routing algorithms based on the described three-objective (cost, emissions, satisfaction) model.
• Analyzing the trade-offs between home delivery and automated locker allocation for urgent and non-urgent patients.
• Evaluating the impact of traffic congestion risk on greenhouse gas emissions within pharmaceutical logistics.

Strengths

• The model is verified through real-world applications, suggesting practical relevance.
• Experimental results show specific cost reductions: 14.74% for one scenario and 13.511% for another.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (5.5 KB), indicating limited scope, likely containing only model outputs or summary statistics.

Provenance

Source

figshare, authored by Zahra Samadi Bahrami.

Collection Method

Likely contains the output data from solving a mixed integer linear programming model with an improved non-dominated sorting genetic algorithm-II and the LP-metric method.

Freshness

Last updated 2026-05-29 17:48:56; freshness should be verified.