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A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes.

I Lee1, R Sikora, M J Shaw

  • 1Beckman Inst. for Adv. Sci. & Technol., Illinois Univ., Urbana, IL.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
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This study introduces a novel genetic algorithm (GA) approach for concurrently solving lot sizing and sequencing problems. The method effectively optimizes flexible flow line scheduling, outperforming other optimization techniques.

Area of Science:

  • Operations Research
  • Computer Science
  • Manufacturing Engineering

Background:

  • Combinatorial optimization problems like TSP, QAP, and job shop scheduling are commonly addressed using genetic algorithms (GAs).
  • Existing GA approaches often require well-defined representations specific to each problem.
  • Lot sizing and sequencing are critical, related problems in manufacturing that often require concurrent optimization.

Purpose of the Study:

  • To present a novel approach for concurrently solving lot sizing and sequencing problems using genetic algorithms.
  • To develop a unified representation enabling GAs to evolve solutions by identifying and combining effective building blocks.
  • To evaluate the proposed GA approach's performance in flexible flow line scheduling.

Main Methods:

Related Experiment Videos

  • Development of a unified genetic algorithm (GA) representation for lot sizing and sequencing information.
  • Incorporation of a simulated annealing procedure to enhance solution quality.
  • Evaluation of the GA approach against pairwise exchange, tabu search, and simulated annealing for flexible flow line scheduling.

    • Main Results:

    • The proposed GA approach demonstrated efficacy in solving flexible flow line scheduling problems with variable lot sizes.
    • The unified representation allowed for effective evolution of chromosomes by replacing primitive genes with superior building blocks.
    • The GA method showed competitive or superior performance compared to alternative optimization techniques.

    Conclusions:

    • The novel GA approach with a unified representation is effective for concurrent lot sizing and sequencing.
    • This method offers a promising solution for optimizing flexible flow line scheduling in manufacturing.
    • The integration of simulated annealing further improves the performance of the genetic algorithm.