Authors: Xiaoyue Liu, Jingze Li, Benoit Montreuil

Published on: February 09, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.06227

Summary

• What is new: This research introduces a novel two-stage stochastic optimization model tailored for the deployment of logistics hub capacities in the face of uncertain demands and geographical disruptions.
• Why this is important: The explosive growth of e-commerce has stressed modern logistics, leading to vulnerable supplies, volatile demands, and fragile transportation networks.
• What the research proposes: The paper proposes a solution through a two-stage stochastic optimization model that smartly deploys hub capacity to enhance delivery timeliness, consolidation, and network resilience, while minimizing costs.
• Results: Simulation results reveal the model’s effectiveness in various scenarios, demonstrating improved network resilience, delivery timeliness, and cost-effectiveness under uncertain conditions.

Technical Details

Technological frameworks used: Stochastic Optimization

Models used: Two-stage stochastic optimization model

Data used: Automotive delivery-to-dealer network dataset in the Southeast US

Potential Impact

Logistics and supply chain companies, particularly those involved in e-commerce and automotive distribution, could significantly benefit or need to adapt to these insights for improved resilience and efficiency.

Want to implement this idea in a business?

We have generated a startup concept here: ResiLogi.