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An improved ant colony optimization approach for optimization of process planning.

JinFeng Wang1, XiaoLiang Fan1, Haimin Ding1

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

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This study enhances ant colony optimization (ACO) for computer-aided process planning (CAPP) by using a weighted graph to minimize total production costs. The improved ACO approach demonstrates effectiveness and efficiency in complex manufacturing environments.

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Area of Science:

  • Manufacturing Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Computer-aided process planning (CAPP) is crucial for integrating computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs).
  • Traditional CAPP methods face challenges in optimizing complex production processes and minimizing costs effectively.

Purpose of the Study:

  • To develop an improved ant colony optimization (ACO) approach for solving the process planning problem.
  • To minimize total production costs (TPCs) by representing the process planning problem as a weighted graph.
  • To enhance ACO with novel pheromone updating strategies and methods to avoid local convergence.

Main Methods:

  • The process planning problem is modeled using a weighted graph comprising nodes (operations), directed arcs (precedence constraints), and undirected arcs (possible paths).
  • An improved Ant Colony Optimization (ACO) algorithm is employed, incorporating Global and Local Update Rules for pheromone management.
  • A strategy to control the repetition of process plans is implemented to prevent local convergence.

Main Results:

  • The proposed weighted graph representation effectively models the complexities of the process planning problem.
  • The enhanced ACO approach, with its refined pheromone updating strategy, successfully minimizes total production costs.
  • Extensive comparative experiments validate the feasibility and superior efficiency of the developed method over standard approaches.

Conclusions:

  • The improved ACO algorithm provides an effective and efficient solution for computer-aided process planning.
  • The weighted graph model and enhanced pheromone updating strategy contribute to achieving optimal solutions for minimizing production costs.
  • This research offers a valuable contribution to optimizing manufacturing processes within computer-integrated environments.