Balancing Mixed-model Assembly Lines With Sequence-dependent Tasks Via Hybrid Genetic Algorithm

The standardization of commodities makes the price competition fiercer, and thediversification of product demands brings about unprecedented challenges to manufacturingindustry. Developed gradually in such industrial environment, mixed-model assembly line is aspecial flow-line production system where several models are produced on the line with littlefacility changes, low production cost and fast response speed. The diversity of automotiveproduct demands necessitates mixed-model assembly line to produce different productsconcurrently efficiently and competitively. Therefore, mixed-model assembly line balancing hasbecome the key point in manufacturing development.In this work, mixed-model assembly line balancing problem with sequence-dependent taskswas considered. According to the characteristics of sequence-dependent relations, this problemwas tackled with the combination of traditional genetic algorithm and heuristic factors.The characteristics of mixed-model assembly line balancing problem andsequence-dependent relations were analyzed respectively, and the integration of mixed-modelassembly line balancing problem and sequence-dependent relation provided a theoretical basisfor practical production.The mathematical model of mixed-model assembly line balancing problem withsequence-dependent tasks was formulated and the traditional genetic algorithm was improvedfrom three aspects. First, sequence-dependent connections and precedence relations among taskswere both integrated into combined precedence graph, transforming mixed-model assembly linebalancing into single-model version. Second, the process of initial population was complementedby introducing three heuristic factors: processing times, the number of immediate successors andthe number of updated tasks. Third, considering sequence-dependent tasks, novel logic stringswere designed to ensure the feasibility of chromosomes during crossover and mutationoperations.Numerical experiments based on mixed-model assembly line balancing problem withsequence-dependent tasks were studied with hybrid genetic algorithm. The solutions haveverified that the whole population after heuristic initialization is of better quality, the bestsolutions converge to near-optimality (even optimality) with less computational efforts, and mostcases outperform the current best solutions. The relevant experiments have demonstrated that theproposed hybrid genetic algorithm functions effectively and efficiently.