A crew task allocation and sequencing method considering workload distribution and minimum task completion time

Abstract

Purpose

In situations where the crew is reduced, the optimization of crew task allocation and sequencing (CTAS) can significantly enhance the operational efficiency of the man-machine system by rationally distributing workload and minimizing task completion time. Existing related studies exhibit a limited consideration of workload distribution and involve the violation of precedence constraints in the solution process. This study proposes a CTAS method to address these issues.

Design/methodology/approach

The method defines visual, auditory, cognitive and psychomotor (VACP) load balancing objectives and integrates them with workload balancing and minimum task completion time to ensure equitable workload distribution and task execution efficiency, and then a multi-objective optimization model for CTAS is constructed. Subsequently, it designs a population initialization strategy and a repair mechanism to maintain sequence feasibility, and utilizes them to improve the non-dominated sorting genetic algorithm III (NSGA-III) for solving the CTAS model.

Findings

The CTAS method is validated through a numerical example involving a mission with a specific type of armored vehicle. The results demonstrate that the method achieves equitable workload distribution by integrating VACP load balancing and workload balancing. Moreover, the improved NSGA-III maintains sequence feasibility and thus reduces computation time.

Originality/value

The study can achieve equitable workload distribution and enhance the search efficiency of the optimal CTAS scheme. It provides a novel perspective for task planners in objective determination and solution methodologies for CTAS.

Citation

Sun, J., Yu, S., Chu, J., Cun, W., Wang, H., Chen, C., Li, F. and Huang, Y. (2024), "A crew task allocation and sequencing method considering workload distribution and minimum task completion time", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-01-2024-0096