Evolving Priority Rules for Online Yard Crane Scheduling with Incomplete Tasks Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{jin:2024:CEC,
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author = "Chenwei Jin and Ruibin Bai and Huayan Zhang",
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title = "Evolving Priority Rules for Online Yard Crane
Scheduling with Incomplete Tasks Data",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Cranes,
Uncertainty, Processor scheduling, Loading,
Evolutionary computation, Throughput, yard crane,
online scheduling, genetic program-ming, priority
rules",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10611875",
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abstract = "In the last decade, the surge in global container port
throughput has heightened the need for terminal
efficiency. The loading process plays a crucial role in
overall port performance. However, the unpredictable
arrival of external trucks poses challenges for yard
cranes in scheduling both internal loading tasks and
external truck tasks simultaneously. Existing
approaches on yard crane scheduling, considering
uncertain arrivals, typically rely on prior knowledge,
which often fails to fully capture the nature of the
real-life uncertainties. In response, we propose an
online scheduling approach guided by a two-stage
decision model, eliminating the need for prior
knowledge of uncertain arrival and has the ability to
dynamically adapt to different scenarios. In the
look-ahead stage, future tasks are filtered dynamically
to eliminate undesired tasks, followed by a priority
rule guided selection stage, where the task with the
highest priority is selected. Genetic Programming (GP)
is employed for automated evolution of priority rules
without human intervention. Realistic experiments
showcase the effectiveness of the proposed dynamic
look-ahead method compared to static minimum and
maximum look-ahead, as well as the superiority of
GP-evolved priority rules compared to manually crafted
priority rules in terms of both performance and
simplicity. A comprehensive analysis of GP-evolved
rules highlights GP's proficiency in problem
understanding and rule extraction, comparable to human
experts.",
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notes = "also known as \cite{10611875}
WCCI 2024",
- }
Genetic Programming entries for
Chenwei Jin
Ruibin Bai
Huayan Zhang
Citations