The production efficiency of an assembly line depends on worker characteristics and operational constraints in manufacturing systems. In many practical assembly settings, workers are required to move between stations, making walking times a relevant component of total operational time. This study investigated assembly lines with walking workers, which were rarely examined simultaneously in existing studies, considering both walking times between workstations and worker-dependent task times to analyze their joint impact on line performance. To address this problem, it was formulated as a mixed-integer linear programming model and supported by a lower bound, a constructive heuristic, and a hybrid solution strategy integrating a genetic algorithm–based meta-heuristic to efficiently solve larger instances. Computational experiments were conducted using a benchmark test instances under different levels of task time variability and walking times between stations. The results demonstrated the effectiveness of the proposed method in terms of solution quality and computational efficiency. The hybrid meta-heuristic was able to obtain better-quality solutions compared with algorithms in the literature. The results also revealed how worker variability and walking times influenced the overall production rate. Higher variability in worker-dependent task durations can improve the production rate at a fixed average task time, whereas longer walking times reduce system efficiency. These findings offered practical implications for the design and balancing of assembly lines with walking workers.
Citation: Murat ŞAHİN. Modeling and analysis of the walking worker assembly line balancing problem considering worker-dependent task durations and walking times to increase production rate[J]. Journal of Industrial and Management Optimization, 2026, 22(4): 1571-1608. doi: 10.3934/jimo.2026058
The production efficiency of an assembly line depends on worker characteristics and operational constraints in manufacturing systems. In many practical assembly settings, workers are required to move between stations, making walking times a relevant component of total operational time. This study investigated assembly lines with walking workers, which were rarely examined simultaneously in existing studies, considering both walking times between workstations and worker-dependent task times to analyze their joint impact on line performance. To address this problem, it was formulated as a mixed-integer linear programming model and supported by a lower bound, a constructive heuristic, and a hybrid solution strategy integrating a genetic algorithm–based meta-heuristic to efficiently solve larger instances. Computational experiments were conducted using a benchmark test instances under different levels of task time variability and walking times between stations. The results demonstrated the effectiveness of the proposed method in terms of solution quality and computational efficiency. The hybrid meta-heuristic was able to obtain better-quality solutions compared with algorithms in the literature. The results also revealed how worker variability and walking times influenced the overall production rate. Higher variability in worker-dependent task durations can improve the production rate at a fixed average task time, whereas longer walking times reduce system efficiency. These findings offered practical implications for the design and balancing of assembly lines with walking workers.
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Murat ŞAHİN. Modeling and analysis of the walking worker assembly line balancing problem considering worker-dependent task durations and walking times to increase production rate[J]. Journal of Industrial and Management Optimization, 2026, 22(4): 1571-1608. doi: 10.3934/jimo.2026058
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