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Piecewise Linear Model for Multiskilled Workforce Scheduling Problems considering Learning Effect and Project Quality

机译：考虑学习效果和项目质量的多技能劳动力调度问题的分段线性模型

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摘要

Workforce scheduling is an important and common task for projects with high labour intensities. It becomes particularly complex when employees have multiple skills and the employees' productivity changes along with their learning of knowledge according to the tasks they are assigned to. Till now, in this context, only little work has considered the minimum quality limit of tasks and the quality learning effect. In this research, the workforce scheduling model is developed for assigning tasks to multiskilled workforce by considering learning of knowledge and requirements of project quality. By using piecewise linearization to learning curve, the mixed 0-1 nonlinear programming model (MNLP) is transformed into a mixed 0-1 linear programming model (MLP). After that, the MLP model is further improved by taking account of the upper bound of employees' experiences accumulation, and the stable performance of mature employees. Computational experiments are provided using randomly generated instances based on the investigation of a software company. The results demonstrate that the proposed MLPs can precisely approach the original MNLP model but can be calculated in much less time.
机译：劳动力调度是高劳动强度项目的重要且常见的任务。当员工具有多种技能并且员工的生产力随着他们分配给他们的任务的知识的学习而变化时，情况变得尤为复杂。到目前为止，在这种情况下，只有很少的工作考虑了任务的最低质量限制和质量学习效果。在这项研究中，通过考虑对知识和项目质量要求的学习，开发了劳动力调度模型，以将任务分配给多技能的劳动力。通过使用分段线性化学习曲线，将混合0-1非线性规划模型（MNLP）转换为混合0-1线性规划模型（MLP）。此后，通过考虑员工经验积累的上限以及成熟员工的稳定表现，进一步改进了MLP模型。基于软件公司的调查，使用随机生成的实例提供计算实验。结果表明，提出的MLP可以精确地逼近原始MNLP模型，但是可以在更少的时间内计算出来。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第2期|3728934.1-3728934.11|共11页
  • 作者

    Qin Shujin; Liu Shixin; Kuang Hanbin;

  • 作者单位

    Northeastern Univ, Coll Informat Sci & Engn, 3-11 Wenhua Rd, Shenyang 110819, Peoples R China;

    Northeastern Univ, Coll Informat Sci & Engn, 3-11 Wenhua Rd, Shenyang 110819, Peoples R China;

    Northeastern Univ, Coll Informat Sci & Engn, 3-11 Wenhua Rd, Shenyang 110819, Peoples R China;

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