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alirezaaminzadeh/lastmile-reoptimization-policy
lastmile-reoptimization-policy is a other model from alirezaaminzadeh. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for lastmileadapt. The card lists the license as apache-2.0.
Lightweight policy selector that recommends a re-optimization strategy during intraday last-mile delivery operations.
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From the Hugging Face model README
Lightweight policy selector that recommends a re-optimization strategy during intraday last-mile delivery operations.
Given fleet state features at a disruption point, predict the best strategy among:
| Strategy | When to use |
|---|---|
| Rolling Horizon | Active disruptions, need low route churn |
| Reinforcement Learning | High re-optimization frequency |
| Monte Carlo / Scenario Tree | High demand/traffic uncertainty |
| RouteFinder | Large instance, need fast full replan |
| OR-Tools | Tight time windows, feasibility focus |
| ALNS | High capacity utilization |
n_active_orders, n_vehicles_available, capacity_utilization, traffic_index, disruption_severity, time_progress, pending_injections, failed_vehicles, avg_time_window_slack, route_fragmentation, stochastic_demand_var, reopt_frequency
| File | Description |
|---|---|
config.json | Model configuration and objective weights |
policy_weights.json | Linear policy weights and biases |
The selector optimizes for composite cost and schedule stability:
objective = distance + 8 × lateness + 12 × route_change_penalty
Apache-2.0