Sequencing to minimize work overload in assembly lines with product options
Management Science
Sequencing JIT mixed-model assembly lines
Management Science
Level schedules for mixed-model, Just-in-Time processes
Management Science
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Sequencing mixed-model assembly lines with genetic algorithms
Computers and Industrial Engineering
Algorithms for sequencing mixed models on an assembly line in a JIT production system
Computers and Industrial Engineering
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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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 MS 1992 [IIE Trans. 24 (1992) 121] 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 setup 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 stratumniche cubicle performs better than the genetic algorithm with the other selection mechanisms.