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A graph-based ant colony optimization approach for process planning.

JinFeng Wang1, XiaoLiang Fan1, Shuting Wan1

  • 1School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China.

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Summary
This summary is machine-generated.

This study introduces an Ant Colony Optimization (ACO) approach for complex process planning, optimizing operation sequencing, resource selection, and setup plans to minimize total production costs.

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Area of Science:

  • Manufacturing Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Process planning is a complex combinatorial optimization problem with numerous constraints.
  • Existing methods may not efficiently handle the simultaneous consideration of multiple planning aspects.

Purpose of the Study:

  • To develop and evaluate an Ant Colony Optimization (ACO) approach for complex process planning.
  • To optimize operation sequencing, resource selection, and setup plans concurrently.
  • To minimize total production costs (TPC) in manufacturing.

Main Methods:

  • Modeling the process planning problem as a constrained combinatorial optimization problem.
  • Utilizing a weighted directed graph to represent operations, precedence constraints, and paths.
  • Applying ACO algorithm to traverse the graph and find optimal process plans.

Main Results:

  • The proposed ACO approach effectively addresses process planning by integrating sequencing, resource selection, and setup planning.
  • Experimental results demonstrate the feasibility and efficiency of the ACO method.
  • Analysis of two case studies shows the influence of ACO parameters on system performance.

Conclusions:

  • The developed ACO approach provides an efficient and effective solution for complex process planning problems.
  • This method offers a robust framework for minimizing total production costs in manufacturing.
  • The study validates the applicability of ACO in optimizing integrated manufacturing decisions.