NSGA-II Performance for Green Pharmaceutical Vehicle Routing with Automated Lockers

A 5.5 KB Excel dataset by Zahra Samadi Bahrami, last updated in May 2026, details the performance of an improved NSGA-II algorithm applied to a pharmaceutical supply chain model. The model optimizes routing for medicine delivery to patients and automated lockers, balancing costs, greenhouse gas emissions, and patient satisfaction. Experimental results indicate cost reductions of 14.74% and 13.511% across two proposed delivery scenarios.

Use Cases

• Benchmarking genetic algorithm performance based on reported DM and MID metrics.
• Analyzing trade-offs between cost, emissions, and patient satisfaction in vehicle routing problems.
• Evaluating the impact of automated medicine lockers on pharmaceutical logistics costs.
• Modeling triage-based patient assignment in home healthcare delivery services.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Results are verified through real-world applications as mentioned in the description.
• Specific cost reduction percentages (14.74% and 13.511%) are provided for model validation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's small size suggests it contains summary results rather than raw operational data.

Provenance

Source

figshare, author Zahra Samadi Bahrami.

Collection Method

Likely contains algorithm performance metrics from solving a mixed integer linear programming model.

Freshness

Last updated 2026-05-29 17:48:59; freshness should be verified.