Cell formation and layout design for the blade manufacturing facility at DSME Trenton inc. or DSTN.

这是一个建议，以扩大现有的方法在文献中的离散零件制造系统的设计。 该提案的工业合作伙伴是DSME特伦顿或DSME，一家位于新斯科舍省特伦顿的风塔和涡轮机制造商。 本建议书中概述的工作将在其风力涡轮机生产制造设施中进行测试。 传统上，使用产品线或流水线来制造具有低种类的大批量产品。 当产品数量少而品种多时，使用作业车间或工艺布局组织。 对于中等数量和种类，成组技术或单元制造是工业中的主要设计选择。 其他布局组织被认为是相关的中间量和品种的制造分形布局，合弄布局，虚拟制造单元等的主要挑战，在设计一个制造系统是它的随机和动态方面。 随机因素是重要的考虑因素，因为产品需求，产品组合，产品路线，由于故障或预防性维护，以及制造技术的选择，机器的可用性存在不确定性。 动态方面是指设施的设计随着时间的推移而发生变化。 在我们的方法中，我们提出了两个家庭的混合整数规划模型的单元制造系统的设计。 第一个混合整数模型是非空间的，并试图解决动态成组技术的细胞形成的问题，其目标是最小化的细胞间的运输成本，机器购置成本，以及从一个细胞到另一个搬迁机器的成本的总和。 第二个混合整数规划模型是空间的，并试图提出一个最佳布局的制造系统在每个时期。 这两个模型都将建立在鲁棒优化的背景下，其目标是确保所设计的系统是随机鲁棒的。 在这项研究中开发的模型将应用于在DINDLE。 反过来，该公司将成为其风力涡轮机生产设施布局设计建议的受益者。

This is a proposal to extend the existing approaches in the literature for the design of discrete parts manufacturing systems. The industrial partner for this proposal is DSME Trenton or DSTN, a manufacturer of wind towers and turbines in Trenton, Nova Scotia. The work outlined in this proposal will be tested in their manufacturing facility for wind turbine production. Traditionally, high volume products with low variety are manufactured using product or flow lines. The job shop or process layout organization is used when product volumes are low and the variety high. For intermediate volume and variety, group technology or cellular manufacturing is the predominant design choice in industry. Other layout organizations considered relevant for intermediate volume and variety manufacturing are fractal layouts, holonic layouts, virtual manufacturing cells, etc. The main challenge in designing a manufacturing system are its stochastic and dynamic aspects. Stochastic aspects are important to take into account because there is uncertainty in product demand, product mix, product routings, availability of machines due to breakdown or preventive maintenance, and choice of manufacturing technologies available. The dynamic aspect refers to the change in the design of the facility over time. In our methodology, we propose two families of mixed-integer programming models for the design of a cellular manufacturing system. The first mixed-integer model is non-spatial and tries to solve the dynamic group technology cell formation problem where the objective is to minimize the sum of inter-cell transportation costs, machine acquisition costs, and the cost of relocating machines from one cell to another. The second mixed integer programming model is spatial and tries to come up with an optimal layout for a manufacturing system in each period. Both models will be built in the context of robust optimization, whose goal is to ensure that the designed system is stochastically robust. The models developed in this research will be applied at DSTN. In turn, the company will be the beneficiary of layout design recommendations for their wind turbine production facility.