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A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints.

Mijodrag Milosevic1, Robert Cep2, Lenka Cepova2

  • 1Department of Production Engineering, Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia.

Materials (Basel, Switzerland)
|December 10, 2021
PubMed
Summary

A novel hybrid grey wolf optimizer (HGWO) optimizes process planning by integrating genetic strategies for enhanced global search. This approach effectively finds optimal machining plans, though computational time requires further improvement.

Keywords:
constraint handlingcrossovergrey wolf optimizermutationprecedence constraintsprocess planning optimizationselection

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Area of Science:

  • Manufacturing Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Process planning optimization is an NP-hard combinatorial problem crucial in manufacturing.
  • Existing metaheuristic approaches require enhancements for efficiency and global search capabilities.

Purpose of the Study:

  • To propose a novel hybrid grey wolf optimizer (HGWO) for optimizing complex process planning problems.
  • To improve the global search abilities and convergence of the traditional grey wolf optimizer (GWO) using genetic strategies.

Main Methods:

  • The hybrid grey wolf optimizer (HGWO) integrates genetic algorithms (selection, crossover, mutation) with GWO.
  • Precedence constraints are modeled using operation precedence graphs and adjacency matrices.
  • A constraint handling heuristic procedure is employed to ensure feasible solutions.

Main Results:

  • The HGWO demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans for prismatic parts.
  • Comparative analysis confirmed the HGWO's ability to minimize total weighted machining costs.
  • While efficient, the HGWO's computational time was found to be higher than some traditional and hybrid algorithms.

Conclusions:

  • The proposed HGWO approach is effective for process planning optimization.
  • Further research is needed to improve the computational efficiency and performance of the HGWO.
  • The study highlights potential directions for enhancing metaheuristic algorithms in manufacturing optimization.