Genetic algorithms for sequencing problems in mixed model assembly lines

Mixed model assembly lines are a type of production line where a variety of product models similar in product characteristics are assembled. The effective utilisation of these lines requires that a schedule for assembling the different products be determined. In this paper, the performance of genetic algorithms for sequencing problems in mixed model assembly lines is investigated. The problem first considered is a comparison between a existing heuristic and the proposed genetic algorithm to get the constant usage of every part used by the line considering variation at multi levels (Number of levels fixed as four, level 1 – product, level 2 – subassembly, level 3 – component, level 4 – raw-materials) for various test-bed problems. The algorithms proposed by Miltenburg and Sinnamon hereafter referred to as MS1992 and the proposed genetic algorithm (GA) applied to mixed model assembly line are compared. Results of evaluation indicate that the GA performs better over MS1992 on 25 of the 40 problems investigated. The other problem solved is a multiple objective sequencing problem in mixed model assembly lines. Three practically important objectives are minimizing total utility work keeping a constant rate of part-usage, minimizing the variability in parts usage and minimizing total set up cost. In this paper, the performance of the selection mechanisms, the Pareto stratum-niche cubicle and the selection based on scalar fitness function value are compared with respect to the objective of minimising variation in part-usage, minimising total utility work and minimising the setup cost. Results of evaluation indicate that the genetic algorithm that uses the Pareto stratum-niche cubicle performs better than the genetic algorithm with the other selection mechanisms.