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Large Scale Middle Mile Network Design Through Efficient Local Search

Authors Carlo S. Sartori , Jian Shen , Anastasia Kireeva , Mattia Neroni , Philipp Loick

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OASIcs.ATMOS.2026.15.pdf
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Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
Keywords
  • Network design
  • Middle mile logistics
  • Black-box evaluation
  • Local search
  • Variable Neighborhood Search

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Abstract

E-commerce competition has intensified in recent years, further raising the delivery speed and cost efficiency standards that companies must meet. The middle mile network that connects fulfillment centers to local distribution centers plays a decisive role in achieving these standards. In this paper, we study the Middle Mile Network Design Problem, which determines optimal network investments that balance the trade-off between fastest delivery speed and minimal operational costs. The problem requires computing truck schedules, shipment routes, and parcel flows through intermediate facilities subject to complex capacity constraints. Two key challenges make this problem computationally hard to solve for standard exact and heuristic approaches: (i) the use of complex black-box evaluations to predict operational outcomes, and (ii) problem instances involving over 200,000 commodities that must be solved within hours for operational planning. We propose a metaheuristic based on variable neighborhood search with specialized local search operators alongside lazy neighborhood evaluation that enable efficient exploration of the high-dimensional solution space. Our algorithm achieves up to 7.6% solution improvement over an incumbent heuristic while reducing runtime by up to 44%.

Cite As Get BibTex

Carlo S. Sartori, Jian Shen, Anastasia Kireeva, Mattia Neroni, and Philipp Loick. Large Scale Middle Mile Network Design Through Efficient Local Search. In 26th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2026). Open Access Series in Informatics (OASIcs), Volume 147, pp. 15:1-15:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.ATMOS.2026.15

Author Details

Carlo S. Sartori
  • Amazon Science and Technology, Luxembourg
Jian Shen
  • Amazon Science and Technology, London, UK
Anastasia Kireeva
  • ETH Zurich, Switzerland
Mattia Neroni
  • Amazon Science and Technology, Barcelona, Spain
Philipp Loick
  • Amazon Science and Technology, Luxembourg

Acknowledgements

We would like to thank the three anonymous reviewers for their valuable feedback. Anastasia Kireeva has contributed to this paper during her internship at Amazon.

References

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