Simultaneous assembly planning and assembly system design using multi-objective genetic algorithms

Authors:
Karim Hamza;Juan F. Reyes-Luna;Kazuhiro Saitou
Affiliations:
Mechanical Engineering Department, University of Michigan, Ann Arbor, MI;Mechanical Engineering Department, University of Michigan, Ann Arbor, MI;Mechanical Engineering Department, University of Michigan, Ann Arbor, MI
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

Citing 6
Cited 0

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Abstract

This paper aims to demonstrate the application of multi-objective evolutionary optimization, namely an adaptation of NSGA-II, to simultaneously optimize the assembly sequence plan as well as selection of the type and number of assembly stations for a production shop that produces three different models of wind propelled ventilators. The decision variables, which are the assembly sequences of each product and the machine selection at each assembly station, are encoded in a manner that allows efficient implementation of a repair operator to maintain the feasibility of the offspring. Test runs are conducted for the sample assembly system using a crossover operator tailored for the proposed encoding and some conventional crossover schemes. The results show overall good performance for all schemes with the best performance achieved by the tailored crossover, which illustrates the applicability of multi-objective GA's. The presented framework proposed is generic to be applicable to other products and assembly systems.