In the context of the future industry, companies have urged to innovate the manufactured products. Today, additive manufacturing makes it possible to respond to the needs of the market in terms of customized production. The recent advances in additive manufacturing technologies have caused a considerable increase in the number of products manufactured by additive processes in industries. In order to satisfy customers' demands and make the investment in additive machines profitable, it is necessary to deal with the production organization in additive manufacturing. This research focuses on the scheduling and nesting problem of production with technological constraints. The objective is to minimize the total delay of the parts to be produced and to maximize the use rate of the additive manufacturing machines. Two models are proposed for powder-based laser technologies and multi jet fusion technology, to estimate the production time in additive manufacturing based on real data. The nesting and scheduling problem is modelled by mixed linear programming. A small example is used to validate the proposed model using the Cplex solver. Due to the NP-hardness of the problem studied, this research develops a heuristic approach to solve large-sized instances. Computational experiments conducted on small and medium size instances indicate that the proposed heuristic is capable to give better solutions within a reasonable time. To evaluate the heuristic performances on large instances, a comparison of the heuristic results is performed with the lower bounds obtained by relaxing the model. The numerical results show that the solutions found by our heuristic are near to the lower bounds proposed.

在未来的行业中，公司敦促创新制成品。如今，增材制造使得可以根据定制生产来响应市场需求。增材制造技术的最新进展已导致工业中通过增材制造工艺生产的产品数量大大增加。为了满足客户的需求并使增材机械的投资获利，有必要与增材制造中的生产组织打交道。这项研究集中在具有技术约束的生产的调度和嵌套问题上。目的是使要生产的零件的总延迟最小化，并使增材制造机器的使用率最大化。提出了两种基于粉末的激光技术和多射流熔合技术的模型，以基于真实数据估算增材制造的生产时间。嵌套和调度问题是通过混合线性规划建模的。一个小例子用于使用Cplex求解器验证所提出的模型。由于所研究问题的NP难点，本研究开发了一种启发式方法来解决大型实例。在中小型实例上进行的计算实验表明，所提出的启发式方法能够在合理的时间内给出更好的解决方案。为了评估大型实例上的启发式性能，将启发式结果与通过放宽模型获得的下限进行比较。