Evolving solutions of mixed-model assembly line balancing problems by chaining heuristic optimization methods: track: evolutionary combinatorial optimization and meta-heuristics

  • Authors:

  • Santiago Enrique Conant-Pablos;Halley Jarumy Ferrer-Soriano;Hugo Terashima-Marin

  • Affiliations:

  • ITESM Campus Monterrey, Monterrey, Mexico;ITESM Campus Monterrey, Monterrey, Mexico;ITESM Campus Monterrey, Monterrey, Mexico

  • Venue:
  • Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2011

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Abstract

Mixed-model Assembly Line Balancing (MALB) is needed on production of a variety of models on the same assembly line as required by just-in-time manufacturing. This paper presents an approach that applies Computational Intelligence techniques for solving MALB problems. The proposed solution consists in a heuristic optimization method that works in three stages: first, it creates an initial population of based on heuristics from classic assembly line balancing methods; second, it uses a memetic algorithm to maximize the line balancing level; and finally, it uses a min-conflicts algorithm to find a solution that better conforms to a set of preferences while trying to maintain the line efficiency of the previous stage. The results yielded by this method demonstrated to be competitive solutions and very close to the optimal.