Evaluation of Attrition Response Strategies in Manual Assembly Production Systems
Open Access Depositedoptimization-based rebalancing (reoptimization) and backfilling. Attrition is modeled stochastically within a discrete-event simulation to demonstrate its continued impact over time, while a learning methodology is included to show worker performance as system memory.The methodology evaluates two strategies across 525 benchmark instances taking consideration of attrition rates, team sizing, individual and aggregated task learning, and efficiency. The result is that reoptimizing the assembly line will consistently improve cost performance and efficiency over time by up to 6%, especially in small teams. This performance margin decreases to 1-2% on larger teams where backfilling can preserve the benefits on task-specific learning and minimize system disruption. The analysis demonstrates that the worker attrition creates persistent, nonlinear productivity losses that extend outside the scope static assembly line balancing and assignment models, where workforce attrition and task learning over time are not explicitly incorporated. There are dynamic extensions in the literature but rarely integrate stochastic attrition and task learning to establish a decision-support analysis. While reoptimizing the assembly line is robust and provides better performance, the value requires more accurate predictions of attrition percent and stability, which are critical data points for relative benefit over backfilling. The results of the research show that workforce assignment under uncertainty, due task-specific learning where worker productivity improves with experience and stochastic workforce attrition which removes that experience, can be reframed for decision-support analysis because of the links among system performance, attrition, and managerial judgment. Therefore, enabling decision makers to determine when reoptimization is appropriate, whether backfill is sufficient, and whether there is a performance benefit to justify the investments required for advancement in attrition forecasting or the selective process automation for the production system.
In low-rate manual assembly production systems, workforce turnover is a critical point of uncertainty that complicates production planning and system control. This research models response strategies to attrition in Python with a PuLP optimization to evaluate two main options
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Cahalane_gwu_0075A_17863.pdf | 2026-06-24 | Open Access |
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Cahalane_Praxis_Appendix1.pdf | 2026-06-24 | Open Access |
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Cahalane_Praxis_Appendix3_Code.pdf | 2026-06-24 | Open Access |
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