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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Published on: August 29, 2025

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A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling.

Emma Hart1, Kevin Sim2

  • 1School of Computing, Edinburgh Napier University, Edinburgh, EH10, UK e.hart@napier.ac.uk.

Evolutionary Computation
|April 28, 2016
PubMed
Summary
This summary is machine-generated.

A new hyper-heuristic method, NELLI-GP, evolves an ensemble of heuristics for job-shop scheduling problems (JSSP). This approach outperforms existing methods on benchmark problems, offering new insights into instance characteristics.

Keywords:
Job-shop-schedulingdispatching rulegenetic programming.heuristic ensemblehyper-heuristic

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Computer Science

Background:

  • Job-shop scheduling problems (JSSP) are complex combinatorial optimization challenges.
  • Existing methods for JSSP include dispatching rules and genetic programming, with varying performance.
  • Hyper-heuristic methods offer a meta-level approach to automate the design of heuristics.

Purpose of the Study:

  • To introduce NELLI-GP, a novel hyper-heuristic method for solving JSSP.
  • To evolve an ensemble of heuristics using a divide-and-conquer strategy.
  • To compare NELLI-GP's performance against existing dispatching rules, genetic programming, and state-of-the-art hyper-heuristics.

Main Methods:

  • NELLI-GP evolves an ensemble of heuristics, where each heuristic specializes in a subset of instances.
  • A novel heuristic generator evolves heuristics composed of linear sequences of dispatching rules, represented and evolved using tree structures.
  • The method employs a divide-and-conquer approach for ensemble construction.

Main Results:

  • The evolved NELLI-GP ensemble significantly outperforms existing dispatching rules and standard genetic programming on new test instances.
  • NELLI-GP achieves superior results on 210 benchmark JSSP problems compared to two state-of-the-art hyper-heuristic approaches.
  • Analysis reveals relationships between evolved heuristics and instance features, providing insights into instance similarity.

Conclusions:

  • NELLI-GP represents a significant advancement in hyper-heuristic methods for JSSP.
  • The ensemble approach with specialized heuristics enhances problem-solving capabilities.
  • The study offers valuable insights into the characteristics of JSSP instances and their impact on heuristic performance.