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Related Experiment Video

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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

Su Nguyen¹, Yi Mei², Bing Xue³

¹Centre for Data Analytics and Cognition, La Trobe University, Australia p.nguyen4@latrobe.edu.au.

Evolutionary Computation

|June 5, 2018

Summary

This summary is machine-generated.

Automated design of dispatching rules using a novel hybrid genetic programming algorithm significantly improves dynamic job shop scheduling. Evolved rules are more effective, smaller, and contain more relevant attributes than traditional methods.

Keywords:

Genetic programming hyper-heuristics.job shop production scheduling

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Area of Science:

Operations Research
Artificial Intelligence
Manufacturing Systems Engineering

Background:

Manual design of dispatching rules for production systems is inefficient and time-consuming.
Automated methods, particularly genetic programming, show promise but face challenges with large search spaces.
Existing research highlights the need for improved algorithms to discover high-quality dispatching rules.

Purpose of the Study:

To develop a novel hybrid genetic programming algorithm for automated dispatching rule discovery.
To enhance the efficiency and effectiveness of dynamic job shop scheduling.
To address the limitations of traditional genetic programming in large heuristic search spaces.

Main Methods:

Implementation of a new hybrid genetic programming algorithm.
Introduction of a new problem representation and local search heuristic.
Utilization of efficient fitness evaluators for rule assessment.

Main Results:

The developed hybrid genetic programming algorithm effectively discovers high-quality dispatching rules.
Evolved rules demonstrate superior performance compared to existing methods.
Discovered rules are significantly smaller and more relevant.

Conclusions:

The novel hybrid genetic programming approach offers a more effective and efficient method for designing dispatching rules.
This advancement contributes to optimizing dynamic job shop scheduling processes.
The method successfully overcomes limitations associated with large search spaces in genetic programming.