Cellular manufacturing systems considering human characteristics

三十多年来，成组技术和细胞制造被认为是许多制造问题的解决方案。通过减少设置、物料处理、在制品和制造交货时间，实现单元制造已经帮助许多组织转向精益制造。这些好处通常是通过将具有相似特征的零件和产品分组为家族，并将共享零件家族过程的不同机器分组为机器单元来实现的。一个可能被忽视的问题是细胞制造的人的方面。很少有研究通过采取顺序方法来解决这个问题，从而解决了操作员到细胞的分配问题。也就是说，形成了第一部分族和机器细胞。然后，操作员被分配到单元。然而，这种方法可能不能完全解决问题，因为许多操作员可能被分配到不合适的单元，而他们的技能没有得到充分利用。操作人员可能有不同的技能和不同的工作偏好。此外，他们的生产力取决于他们在工作上的经验。如果没有考虑到这些重要的因素，将导致作业人员失去动力和兴趣，并降低系统的生产率。本研究提出了一种同时考虑操作人员技能匹配、经验和偏好的方法来设计机器单元和零件族。建议的研究将检查现有的绩效指标，以便将其扩展到感兴趣的多维问题。它还将回顾许多现有的机器单元/零件族形成技术，以研究它们在机器-零件-操作员分组问题中的性能，并将提出一个具有广泛适用性的广义分组问题。利用所提出的性能模型，选择最佳的分组方法。

For more than three decades, group technology and cellular manufacturing have been considered as solutions to many manufacturing problems. Implementing cellular manufacturing has helped many organizations move toward lean manufacturing by reducing setups, material handling, work-in-process, and manufacturing lead times. These benefits are usually achieved by grouping parts and products with similar characteristics into families, and grouping dissimilar machines that share the process of part families into machine cells. One issue that is likely to be overlooked is the human aspects of the cellular manufacturing. Few studies have addressed the allocation of operators to cells by taking a sequential approach to this problem. That is, first part families and machine cells are formed. Then, operators are assigned to cells. This approach, however, may not fully address the problem, as many operators may be allocated to inappropriate cells where their skills are not fully utilized. Operators may have different skills and different preferences towards jobs. Also, their productivity depends on their experience on the job. Failure to consider these important elements will result in loss of motivation and interest for operators, and low productivity for the system. This research proposes a simultaneous approach that considers operators' skill match and their experience and preference while designing machine cells and part families. The proposed research will examine the existing performance measures in order to extend them to the multi-dimensional problem of interest. It will also revisit many existing machine cell/part family formation techniques to investigate their performance for the machine-part-operator grouping problem and will propose a generalized grouping problem with a wide range of applicability. Using the proposed performance models, the best grouping method will be selected.