Last Mile Delivery With Stochastic Travel Times Considering Dual Services
DOI: 10.1109/ACCESS.2019.2950442
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the Green Vehicle Routing Problem with Dual Services and Stochastic Travel Times (GVRP-DS-STT), focusing on optimizing last-mile delivery operations. The research is motivated by the need to reduce greenhouse gas emissions and operational costs in urban logistics, where traditional fleets are transitioning to alternative fuel vehicles with limited ranges. The study specifically examines a system combining Home Delivery (HD), which requires strict time windows, and Customer Pickup (CP) via intelligent express boxes, which offers time flexibility. Unlike previous studies that assigned separate vehicles to HD and CP services, this work allows a single vehicle to serve both, aiming to balance delivery costs, service levels, and environmental impact while accounting for real-world uncertainties like traffic congestion and weather that cause stochastic travel times. To solve this problem, the authors formulate a two-stage stochastic optimization model with recourse. The first stage determines the deterministic routing plan, while the second stage calculates expected recourse costs arising from stochastic travel time deviations, such as missed time windows or route duration violations. Due to the computational complexity of large-scale instances, the authors develop a hybrid heuristic algorithm integrating a sampling strategy (HH-S). This method consists of a greedy-based initial solution generation phase and an improvement heuristic using late acceptance and four neighborhood search strategies (greedy and random removal/reinsertion). The algorithm employs Sample Average Approximation (SAA) to estimate expected values and validate solution optimality. Computational experiments were conducted on generated instances ranging from 30 to 120 customers, with CP customer percentages varying from 0% to 50%. The results indicate that increasing the proportion of CP customers significantly reduces total operational costs, the number of vehicles required, and improves vehicle loading rates, particularly in larger instances. The study also finds that while strict time windows increase operational costs, a higher percentage of CP customers mitigates this impact by providing scheduling flexibility. Furthermore, the stochastic model outperforms deterministic models, reducing total costs by an average of 11.03% despite higher computational times. The stochastic approach proves more robust in handling real-world variability, confirming the economic and environmental benefits of integrating dual services under uncertain conditions.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | DOAJ | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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