Balancing mixed-model assembly lines with sequence-dependent tasks via hybrid genetic algorithm
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Close connections existing among sequence-dependent tasks should be emphasized while assembling products within automotive or electronic industries. This paper addresses the mixed-model assembly line balancing problem with sequence-dependent tasks with two objectives, the minimization of cycle time and workload variance. A hybrid genetic algorithm with novel logic strings was proposed to address the problem. First, both the sequence-dependent connections and precedence relations are integrated into the combined precedence graph so as to transform the original problem into the single-model assembly line balancing problem and to decrease the computational complexity. Second, three heuristic factors are hybridized into the process of initialization with the purpose of improving the quality of initial solution population. Third, considering sequence-dependent tasks, logic strings are designed to ensure the feasibility of chromosomes during two-point crossover and insertion mutation operations. Computational studies have demonstrated that the proposed algorithm can solve problems to near-optimality and even optimality with less computational effort.
KeywordsMixed-model assembly line balancing Sequence-dependent tasks Combined precedence graph Hybrid genetic algorithm Elite preservation strategy
The authors would like to say thanks to the Computer-Aided Systems Laboratory of Princeton University and Dongfeng Peugot Citroen Automobile Company.
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