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，在文献离散零件制造系统的设计。该提案的工业合作伙伴是位于新斯科舍省特伦顿的DSME Trenton或dsn，一家风力塔和涡轮机制造商。本提案中概述的工作将在他们的风力涡轮机生产制造设施中进行测试。传统上，大批量低品种的产品是使用产品或流水线生产的。当产品数量少而品种多时，使用作业车间或工艺布局组织。对于中等数量和品种，成组技术或细胞制造是工业中主要的设计选择。其他被认为与中间数量和品种制造相关的布局组织有分形布局、全息布局、虚拟制造单元等。设计制造系统的主要挑战是其随机性和动态性。考虑随机方面很重要，因为在产品需求、产品组合、产品路线、由于故障或预防性维护而导致的机器可用性以及可用制造技术的选择等方面存在不确定性。动态方面是指随着时间的推移，设施设计的变化。在我们的方法中，我们提出了两类混合整数规划模型用于细胞制造系统的设计。第一个混合整数模型是非空间的，试图解决动态群体技术单元形成问题，其目标是最小化单元间运输成本、机器获取成本和机器从一个单元迁移到另一个单元的成本之和。第二种混合整数规划模型是空间规划模型，它试图给出制造系统在每个周期内的最优布局。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具有随机鲁棒性。本研究开发的模型将在DSTN中得到应用。反过来，该公司将受益于其风力涡轮机生产设施的布局设计建议。

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.