Summary of A Bi-objective Approach to Last-mile Delivery Routing Considering Driver Preferences, by Juan Pablo Mesa et al.
A Bi-Objective Approach to Last-Mile Delivery Routing Considering Driver Preferences
by Juan Pablo Mesa, Alejandro Montoya, Raul Ramos-Pollán, Mauricio Toro
First submitted to arxiv on: 25 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to tackle the Multi-Objective Vehicle Routing Problem (MOVRP) in the transportation and logistics industry, which considers drivers’ and operators’ decisions and preferences. The paper evaluates two methods: visually attractive route planning and data mining of historical driver behavior. Experimental results using a real-world Amazon dataset show that data mining outperforms visual attractiveness metrics. A bi-objective problem is also proposed to balance routing costs and route similarity. To solve this, a two-stage GRASP algorithm with heuristic box splitting is developed. The approach aims to approximate the Pareto front and generate non-dominated solutions for decision-makers to identify trade-offs between objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The MOVRP paper proposes new ways to plan routes that drivers like and are efficient. It compares two methods: making routes look nice and using data from past driver behavior. Data mining does better than visual attractiveness. The paper also tries to balance costs and route similarity by creating a special problem with two objectives. A special algorithm is designed to solve this problem and find the best solutions. |
