Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

In this paper, we propose to design a large-scale intelligent collaborative platform for freight forwarders. This platform is based on a mathematical programming formulation and an efficient solution approach. Forwarders are middlemen who procure container capacities from carriers and sell them to shippers to serve their transport requests. However, due to demand uncertainty, they often either over-procure or under-procure capacities. We address this with our proposed platform where forwarders can collaborate and share capacities, allowing one's transport requests to be potentially shipped on another forwarder's container. The result is lower total costs for all participating forwarders. The collaboration can be formulated as an integer linear program we call the Freight Forwarders' Collaboration Problem (FFCP). It is a variant of the bin-packing problem, hence it is NP-Hard. In order to solve large-scale FFCP instances efficiently, we propose a two-step approach involving an initial greedy assignment followed by a fine-tuning step. Computational experiments have shown that our approach can offer a significant reduction of run-time between 77% and 97%, without any loss of solution quality.

Keywords

Upper bound, Costs, Uncertainty, Collaboration, Containers, Distance measurement, Computational intelligence

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2023 IEEE Symposium Series on Computational Intelligence, Mexico, December 5-8

First Page

1767

Last Page

1772

ISBN

9781665430647

Identifier

10.1109/SSCI52147.2023.10371994

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/SSCI52147.2023.10371994

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