# SPOT: Spatio-Temporal Pattern Mining and Optimization for Load Consolidation in Freight Transportation Networks

arXiv: https://arxiv.org/abs/2504.09680 · HTML: https://mlsanity.com/paper/2504.09680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 76 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Contextual Stochastic Optimization for Omnichannel Multi-Courier Order Fulfillment Under Delivery Time Uncertainty](https://mlsanity.com/paper/2409.06918/) (2024)
- [An Optimization Framework for Efficient and Sustainable Logistics Operations via Transportation Mode Optimization and Shipment Consolidation: A Case Study for GE Gas Power](https://mlsanity.com/paper/2212.03662/) (2022)
- [Confidence-Aware Deep Learning for Load Plan Adjustments in the Parcel Service Industry](https://mlsanity.com/paper/2411.17502/) (2024)
- [A fast and effective MIP-based heuristic for a selective and periodic inventory routing problem in reverse logistics](https://mlsanity.com/paper/2004.04188/) (2020)

## Built on by (most cited first, 1 of 1)

- [Conformal Predictive Distributions for Order Fulfillment Time Forecasting](https://mlsanity.com/paper/2505.17340/) (2025)

## Abstract (arXiv)

Freight consolidation has significant potential to reduce transportation costs and mitigate congestion and pollution. An effective load consolidation plan relies on carefully chosen consolidation points to ensure alignment with existing transportation management processes, such as driver scheduling, personnel planning, and terminal operations. This complexity represents a significant challenge when searching for optimal consolidation strategies. Traditional optimization-based methods provide exact solutions, but their computational complexity makes them impractical for large-scale instances and they fail to leverage historical data. Machine learning-based approaches address these issues but often ignore operational constraints, leading to infeasible consolidation plans. This work proposes SPOT, an end-to-end approach that integrates the benefits of machine learning (ML) and optimization for load consolidation. The ML component plays a key role in the planning phase by identifying the consolidation points through spatio-temporal clustering and constrained frequent itemset mining, while the optimization selects the most cost-effective feasible consolidation routes for a given operational day. Extensive experiments conducted on industrial load data demonstrate that SPOT significantly reduces travel distance and transportation costs (by about 50% on large terminals) compared to the existing industry-standard load planning strategy and a neighborhood-based heuristic. Moreover, the ML component provides valuable tactical-level insights by identifying frequently recurring consolidation opportunities that guide proactive planning. In addition, SPOT is computationally efficient and can be easily scaled to accommodate large transportation networks.