The shift minimization personnel task scheduling problem is an NP-complete optimization problem that concerns the assignment of tasks to multi-skilled employees with a view to minimize the total number of assigned employees. Recent literature indicates that hybrid methods which combine exact and heuristic techniques such as matheuristics are efficient as regards to generating high quality solutions. The present work employs a constructive matheuristic (CMH): a decomposition-based method where sub-problems are solved to optimality using exact techniques. The optimal solutions of sub-problems are subsequently utilized to construct a feasible solution for the entire problem. Based on the study, a time-based CMH has been developed which, for the first time, solves all the difficult instances introduced by Smet et al. (Omega 46:64–73, 2014) to optimality. In addition, an automated CMH algorithm that utilizes instance-specific problem features has also been developed that produces high quality solutions over all current benchmark instances.

班次最小化人员任务计划问题是一个NP完全优化问题，涉及将任务分配给多技能员工，以最大程度地减少分配的员工总数。最近的文献表明，结合精确和启发式技术（例如数学）的混合方法对于生成高质量解决方案是有效的。本工作采用了一种建设性的数学方法（CMH）：一种基于分解的方法，其中使用精确的技术将子问题求解为最优。子问题的最优解随后被用于构建整个问题的可行解。基于这项研究，开发了基于时间的CMH，这首次解决了Smet等人提出的所有困难情况。（Ω46：64–73，2014）达到最佳状态。此外，还开发了利用特定于实例的问题特征的自动CMH算法，该算法可在所有当前基准实例上产生高质量的解决方案。