Skip to content
Open access

A Line-Based Algorithm for Container Routing in Shipping Networks

Jul 2026 · Future Transportation · Vol 6, pp. 160 · 1 citation · 83 references

Abstract

This paper proposes an integrated routing framework for liner shipping networks in which the routing decision concerns the movement of one or more containers from an origin to a destination, jointly addressing topological feasibility, temporal consistency, and cost–time trade-offs. The methodology combines a label-setting routing algorithm with a post-processing phase that enables multi-criteria analysis and clustering of origin–destination pairs. Within this framework, each container route explicitly accounts for service schedules, frequencies, dwell time, transshipment constraints, and port-specific handling costs, thereby ensuring the generation of temporally feasible routes over large-scale liner shipping networks. Two optimality criteria are considered for the container routing problem: time and cost. Computational experiments on a real-inspired network demonstrate the scalability of the proposed approach and highlight the difference between optimal time and cost-routing choices for containers. Further insights are obtained through clustering analyses, which reveal heterogeneous routing profiles and distinct trade-off patterns across origin–destination pairs, providing additional management insights beyond aggregate performance indicators. Overall, the proposed procedure offers a flexible and extensible tool for analyzing container movements within liner shipping services and supports advanced decision-making in maritime network design and service planning.

Read PDF

Similar papers

Open access 2026

CROSS-DOCK SCHEDULING WITH ROUTING DECISIONS: AN INTEGRATED APPROACH

ABSTRACT Defining delivery routes is an effective way to reduce transportation costs. In particular, a cross-dock distribution center in a retail network requires coordinated decisions on delivery routes and internal operations, leading to the cross-dock scheduling with routing decisions problem. To the best of our kno...

E. D. Bernardes, F. Toledo · 0 citations
Open access Sep 2026

An Agent for Collaborative Optimization of Route Planning and Three-Dimensional Loading in Land Logistics

This study sets out to build an intelligent agent that plans vehicle routes and handles three-dimensional cargo loading at the same time and suggests that tying routing and loading together in a closed-loop setup makes plans easier to carry out.

Jing-Xiang Wang, Xin-Yue Cai, Yu-Feng Xin et al. · 0 citations
Open access Aug 2026

Joint Fleet Sizing and Routing for Multi-Truck–Multi-Drone Collaborative Delivery

Truck–multi-drone collaborative delivery can reduce last-mile costs, but fleet sizing and routing are often optimized separately, making it difficult to match resources with demand under a delivery-period constraint. This study addresses the scenario of collaborative delivery involving multiple trucks and multiple dron...

Feng-Jie Xie, Guo-Jin Zhang, Yu-Hua Jia · 0 citations
Open access Aug 2026

Research on Route Optimization for Truck–Drone Delivery Considering En Route Synchronization

Truck–UAV collaborative delivery can improve last-mile logistics efficiency, but fixed-node rendezvous often causes waiting loss and service delay. To address this problem, this paper proposes a route optimization method integrating en route synchronization, pseudo-node insertion, and GAT-PPO. Pseudo-nodes are generate...

Shu-Kang Zheng, Gen-Hua Ma, Hanpei Yang et al. · 0 citations
Open access Aug 2026

The Collaborative Green Vehicle Routing Problem with Time-Dependent Travel Speeds

This study investigates the collaborative green vehicle routing problem with time-dependent travel speeds (CGVRP-TD), which integrates horizontal collaboration among multiple depots with time-dependent traffic conditions. The problem jointly optimizes customer allocation, vehicle routing, and departure-time decisions t...

Juan Li, Yang Yu, Min Huang et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.