Model and Heuristics for the Multi-Manned Assembly Line Worker Integration and Balancing Problem. Michels, A. S. & Costa, A. M. International Journal of Production Research, Taylor & Francis, 2024.
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This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.
@article{michels24model,
  title = {Model and Heuristics for the Multi-Manned Assembly Line Worker Integration and Balancing Problem},
  author = {Michels, Adalberto Sato and Costa, Alysson M.},
  year = {2024},
  journal = {International Journal of Production Research},
  pages = {1--26},
  publisher = {Taylor \& Francis},
  issn = {0020-7543},
  doi = {10.1080/00207543.2024.2347572},
  urldate = {2024-05-24},
  abstract = {This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.},
  keywords = {Assembly line balancing,decomposition heuristic; proximity search,heterogeneous workforce integration,mixed-integer linear programming,multi-manned station},
  file = {/Users/acosta/Zotero/storage/XQ9JRQN3/Michels and Costa - 2024 - Model and heuristics for the multi-manned assembly.pdf}
}

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