A genetic algorithm for the optimisation of assembly sequences

  • Authors:

  • Romeo M. Marian;Lee H. S. Luong;Kazem Abhary

  • Affiliations:

  • School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Mawson Lakes, SA, Australia;School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Mawson Lakes, SA, Australia;School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, Mawson Lakes, SA, Australia

  • Venue:
  • Computers and Industrial Engineering - Special issue: Sustainability and globalization: Selected papers from the 32 nd ICC&IE
  • Year:
  • 2006

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Abstract

This paper describes a Genetic Algorithm (GA) designed to optimise the Assembly Sequence Planning Problem (ASPP), an extremely diverse, large scale and highly constrained combinatorial problem. The modelling of the ASPP problem, Which has to be able to encode any industrial-size product with realistic constraints, and the GA have been designed to accommodate any type of assembly plan and component. A number of specific modelling issues necessary for understanding the manner in which the algorithm works and how it relates to real-life problems, are succinctly presented, as they have to be taken into account/adapted/solved prior to Solving and Optimising (S/O) the problem. The GA has a classical structure but modified genetic operators, to avoid the combinatorial explosion. It works only with feasible assembly sequences and has the ability to search the entire solution space of full-scale, unabridged problems of industrial size. A case study illustrates the application of the proposed GA for a 25-components product.