A Data-driven Truck Dispatching Algorithm for a Sequence-constrained Less-than-truckload Container Transshipment Problem

Jiahui Gong; Jianjun Chen; Jun Qi; Haiyang Zhang

A Data-driven Truck Dispatching Algorithm for a Sequence-constrained Less-than-truckload Container Transshipment Problem

Jiahui Gong
, Jianjun Chen^*
, Jun Qi
, Haiyang Zhang

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Container handling optimization in ports significantly influences logistics chain efficiency and cost control, vital for economic benefits. Traditional research prioritizes quay crane (QC) scheduling, while truck dynamic scheduling often ties directly to specific QCs, prolonging QC operation times and affecting port throughput and efficiency. To tackle this, the paper introduces a dynamic truck dispatching algorithm with two task allocation strategies. Experimental results reveal our algorithm
reduces total QC make span by 10.8% in general when compared to traditional methods and has increased effectiveness in large scale problems.

Original language	English
Title of host publication	IEEE International Conference on Industrial Informatics (INDIN)
Publication status	Published - 17 Aug 2024

Cite this

@inproceedings{be9c1eb3b93c4e95aa5f2e1f7295e90c,

title = "A Data-driven Truck Dispatching Algorithm for a Sequence-constrained Less-than-truckload Container Transshipment Problem",

abstract = "Container handling optimization in ports significantly influences logistics chain efficiency and cost control, vital for economic benefits. Traditional research prioritizes quay crane (QC) scheduling, while truck dynamic scheduling often ties directly to specific QCs, prolonging QC operation times and affecting port throughput and efficiency. To tackle this, the paper introduces a dynamic truck dispatching algorithm with two task allocation strategies. Experimental results reveal our algorithm reduces total QC make span by 10.8\% in general when compared to traditional methods and has increased effectiveness in large scale problems.",

author = "Jiahui Gong and Jianjun Chen and Jun Qi and Haiyang Zhang",

year = "2024",

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day = "17",

language = "English",

booktitle = "IEEE International Conference on Industrial Informatics (INDIN)",

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AU - Gong, Jiahui

AU - Chen, Jianjun

AU - Qi, Jun

AU - Zhang, Haiyang

PY - 2024/8/17

Y1 - 2024/8/17

N2 - Container handling optimization in ports significantly influences logistics chain efficiency and cost control, vital for economic benefits. Traditional research prioritizes quay crane (QC) scheduling, while truck dynamic scheduling often ties directly to specific QCs, prolonging QC operation times and affecting port throughput and efficiency. To tackle this, the paper introduces a dynamic truck dispatching algorithm with two task allocation strategies. Experimental results reveal our algorithm reduces total QC make span by 10.8% in general when compared to traditional methods and has increased effectiveness in large scale problems.

AB - Container handling optimization in ports significantly influences logistics chain efficiency and cost control, vital for economic benefits. Traditional research prioritizes quay crane (QC) scheduling, while truck dynamic scheduling often ties directly to specific QCs, prolonging QC operation times and affecting port throughput and efficiency. To tackle this, the paper introduces a dynamic truck dispatching algorithm with two task allocation strategies. Experimental results reveal our algorithm reduces total QC make span by 10.8% in general when compared to traditional methods and has increased effectiveness in large scale problems.

M3 - Conference Proceeding

BT - IEEE International Conference on Industrial Informatics (INDIN)

ER -

A Data-driven Truck Dispatching Algorithm for a Sequence-constrained Less-than-truckload Container Transshipment Problem

Abstract

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