Research map / Learning theory and algorithms / Combinatorial and search optimization
Rolling Horizon Based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RH3 rolling horizon instead of RH0 online scheduling (ablation) | PDPTW solver · Chattanooga paratransit, 6 fleets | service rate: 77.7% -> 85.6% | Helped |
| Use RH2 rolling horizon instead of RH0 online scheduling (ablation) | PDPTW solver · NYC Scenario 2 | average service rate: 75.6% -> 79.0% | Helped |
| Increase rolling-horizon window size (ablation) | PDPTW solver · NYC Scenario 2 | average service rate | Helped |
| Increase rolling-horizon window size (ablation) | PDPTW solver · Chattanooga paratransit | compute time | Hurt |
| Increase rolling-horizon window size from 5 to 10 (ablation) | PDPTW solver · PDPTW benchmark instances | compute time: 0.42 -> 0.44; 0.50 -> 0.50; 0.49 -> 0.50; 0.49 -> 0.60; 0.50 -> 0.52; 0.39 -> 0.40 | Hurt |
| Increase rolling-horizon window size from 5 to 10 (ablation) | PDPTW solver · PDPTW benchmark instances | service rate: 98 -> 100; 96.15 -> 100 | Helped |
| Increase rolling-horizon window size from 5 to 10 (ablation) | PDPTW solver · PDPTW benchmark instances | VMT: 1095 -> 1127; 1116 -> 1140; 1172 -> 1163; 1165 -> 1085; 1209 -> 1120; 1981 -> 2021 | Mixed |
| Use rolling horizon rather than LKH3 | PDPTW solver · Six adjusted PDPTW benchmark instances | compute time: 12.05 sec to 50.85 sec; less than 0.6 seconds | Helped |
| Use rolling horizon rather than LKH3 | PDPTW solver · Six adjusted PDPTW benchmark instances | VMT: 6.23% to 13.57% | Hurt |
| Use RH2 instead of Google OR-Tools GLS for large instances | PDPTW solver · NYC Scenario 2 | feasible solutions: RH2 solves any instance within 1 second per request; GLS cannot get any feasible solution in 29 among 31 instances withi | Helped |
| Use a larger window to incorporate known future requests | PDPTW solver · NYC Scenario 1 | service rate | No clear effect |
| Use rolling horizon instead of GLS | PDPTW solver · NYC Scenario 1 | average delay time | Helped |
Closest papers by meaning
- Hybrid-Balance GFlowNet for Solving Vehicle Routing Problems (NeurIPS 2025 · similarity 0.84)
- SVRPBench: A Realistic Benchmark for Stochastic Vehicle Routing Problem (NeurIPS 2025 · similarity 0.83)
- CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention (ICML 2025 · similarity 0.81)
- Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems (NeurIPS 2025 · similarity 0.81)
- Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling (ICLR 2025 · similarity 0.81)
- Rethinking Light Decoder-based Solvers for Vehicle Routing Problems (ICLR 2025 · similarity 0.81)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.