Work

Straddle carrier routing optimization at container terminals utilizing telemetry‐based prediction

Year: 2026

Type: article

Abstract: Efficient vehicle routing and scheduling for horizontal transport means on container terminals can reduce lead times and travel distances, resulting in fuel savings and productivity gains. We apply machine learning to emulate operational processes, bridging the gap between theoretical optimization models and real‐world practices at container terminals. Using telemetry data and predictions based on machine learning, we analyze travel times. This information is then incorporated into an optimization framework for horizontal transport dispatching. In a case study with straddle carrier operations for a container terminal in Hamburg, Germany, we focus on (meta)heuristics for solving related vehicle routing problems. Using a greedy algorithm, local search, and variable neighborhood descent, we achieve a reduction in travel times of 17% compared to current operations controlled by the terminal operating system.

Source: International Transactions in Operational Research

DOI: 10.1111/itor.70186

Authors: Julian Neugebauer, Ahmet Cürebal, Leonard Heilig, Stefan Voß

Institutions: Universität Hamburg, Pontificia Universidad Católica de Valparaíso

Language: English

Topic: Maritime Ports and Logistics

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,030