Deep Learning--Accelerated Multi-Start Large Neighborhood Search for Real-time Freight Bundling

TL;DR

This paper introduces a deep learning-accelerated hybrid approach combining neural networks and metaheuristics to efficiently solve real-time freight bundling problems modeled as a complex pickup-and-delivery routing problem, achieving near-optimal solutions.

Contribution

It presents the first integration of neural network-based constructive policies with multi-start large neighborhood search for real-time freight routing optimization.

Findings

Outperforms state-of-the-art neural and metaheuristic methods in solution quality.

Achieves less than 2% optimality gap in revenue compared to exact methods.

Demonstrates broad applicability to similar combinatorial routing problems.

Abstract

Online Freight Exchange Systems (OFEX) play a crucial role in modern freight logistics by facilitating real-time matching between shippers and carrier. However, efficient combinatorial bundling of transporation jobs remains a bottleneck. We model the OFEX combinatorial bundling problem as a multi-commodity one-to-one pickup-and-delivery selective traveling salesperson problem (m1-PDSTSP), which optimizes revenue-driven freight bundling under capacity, precedence, and route-length constraints. The key challenge is to couple combinatorial bundle selection with pickup-and-delivery routing under sub-second latency. We propose a learning--accelerated hybrid search pipeline that pairs a Transformer Neural Network-based constructive policy with an innovative Multi-Start Large Neighborhood Search (MSLNS) metaheuristic within a rolling-horizon scheme in which the platform repeatedly freezes the current marketplace into a static snapshot and solves it under a short time budget. This pairing leverages the low-latency, high-quality inference of the learning-based constructor alongside the robustness of improvement search; the multi-start design and plausible seeds help LNS to explore the solution space more efficiently. Across benchmarks, our method outperforms state-of-the-art neural combinatorial optimization and metaheuristic baselines in solution quality with comparable time, achieving an optimality gap of less than 2\% in total revenue relative to the best available exact baseline method. To our knowledge, this is the first work to establish that a Deep Neural Network-based constructor can reliably provide high-quality seeds for (multi-start) improvement heuristics, with applicability beyond the \textit{m1-PDSTSP} to a broad class of selective traveling salesperson problems and pickup and delivery problems.