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Multilevel hybrid method for optimal buffer sizing and inspection stations positioning.

Fatima Zahra Mhada1, Mohamed Ouzineb2, Robert Pellerin3

  • 1GERAD and University Mohammed V-Rabat/ENSIAS, B.P. 713, Rabat-Instituts, Madinat Al Irfane, Rabat, Morocco.

Springerplus
|December 21, 2016
PubMed
Summary
This summary is machine-generated.

This study presents a fast algorithm for optimizing manufacturing systems by efficiently determining inspection station locations and buffer sizes. The hybrid approach significantly reduces computation time for production lines with 10-30 machines.

Keywords:
Combinatorial optimizationInspectionMetaheuristicsProductionQuality

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Area of Science:

  • Operations Research
  • Manufacturing Systems Engineering

Background:

  • Designing competitive manufacturing systems requires balancing productivity, quality, and cost.
  • Decision-making involves complex trade-offs across numerous variables and iterative analyses.

Purpose of the Study:

  • To develop a computationally efficient algorithm for the buffer sizing and inspection positioning problem in large production lines.
  • To provide a method that aids decision-makers in optimizing manufacturing system design.

Main Methods:

  • A multilevel hybrid search method combining genetic algorithms and tabu search was developed.
  • An exact method was integrated to optimize buffer sizes in polynomial time for identified inspection station locations.

Main Results:

  • The proposed hybrid algorithm significantly reduces solution time compared to purely exact methods.
  • The method provides valuable insights for optimizing inspection station placement and buffer allocation.
  • Effective for production lines ranging from 10 to 30 machines.

Conclusions:

  • The hybrid heuristic-exact algorithm offers a fast and effective solution for complex manufacturing system design problems.
  • This approach enhances the ability to achieve high productivity and quality at a reasonable cost.
  • Optimized buffer sizing and inspection positioning are crucial for competitive manufacturing.