We study the staffing and scheduling problem of a multiskilled workforce with uncertain demand. We formulate the problem as a two-stage stochastic integer program. The first stage considers strategic decisions, including recruiting permanent staff from an available pool and training them with additional skills, and the second stage focuses on operational decisions, involving the allocation of the multiskilled workforce and the hiring of temporary staff to accommodate uncertain demand. To effectively solve problems of practical sizes, we develop a novel solution algorithm based on the logic-based Benders’ decomposition (LBBD) approach, incorporating a customized analytical cut. We validate our approach through a case study using the data from a prefabrication company, demonstrating the significant cost savings achieved through workforce multiskilling. Our experimental results show that the proposed method is substantially more efficient than the latest Gurobi solver, up to 133 times faster and on average 29 times faster than directly solving the monolithic deterministic equivalent problem (MDEP).

中文翻译：

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多技能劳动力人员配置和调度：一种基于逻辑的 Benders 分解方法


我们研究了需求不确定的多技能劳动力的人员配备和调度问题。我们将问题表述为两阶段随机整数程序。第一阶段考虑战略决策，包括从可用人才库中招聘长期员工并对其进行额外技能培训，第二阶段侧重于运营决策，涉及分配多技能劳动力和雇用临时员工以适应不确定的需求。为了有效解决实际规模的问题，我们开发了一种基于逻辑的 Benders 分解 （LBBD） 方法的新型求解算法，并结合了定制的分析切割。我们通过使用预制公司的数据进行案例研究来验证我们的方法，证明通过劳动力多技能实现的显著成本节约。我们的实验结果表明，所提出的方法比最新的 Gurobi 求解器效率高得多，比直接求解整体确定性等效问题 （MDEP） 快 133 倍，平均快 29 倍。