Efficient Time-Dependent Shortest Path Finding on Cargo Network

Elton Chun-Chai Li, Ziyi Liu, Ruiyuan Zhang, Sean Shing Fung Lau, Yehong Xu, Xiaofang Zhou

Abstract: Surging e-commerce and global trade necessitate highly efficient cargo terminal operations. Modern automated terminals, crucial for supply chains, employ complex networks of static and movable equipment. This integration introduces a core challenge: movable equipment creates dynamic connectivity and state-dependent travel times, rendering classic shortest path algorithms based on static edge weights ineffective. Unlike typical time-dependent problems driven by external factors such as traffic congestion or fixed schedules, our dynamics stem from internal equipment state, presenting a unique optimization challenge. We address the problem of finding optimal cargo routes within these dynamic environments. We propose a novel approach by modeling the terminal as a cargo network, where virtual edges induced by movable equipment are explicitly materialised and edge costs reflect the status of the real-time equipment. We propose an efficient Dijkstra's-based algorithm to solve the cargo routing problem within this framework considering the system dynamics. The primary contributions of this paper are this novel modeling technique for dynamic terminals and the adapted algorithm for optimal routing, offering significant benefits for logistics optimization and automated warehouse design. Experimental results demonstrate that our approach significantly reduces cargo travel times compared to baseline methods, offering substantial improvements for logistics efficiency in automated terminals.

External IDs:dblp:conf/bigdataconf/LiLZLXZ25