The introduction of a new product into an operating factory can have significant adverse impacts on the throughput and cycle time of all products produced in the factory, and thus needs to be managed carefully. In previous work we proposed a production planning model for new product introductions that captures the impact of additional variability caused by the new product and of learning as experience producing the new product is gained. This paper extends the earlier work by incorporating learning through deliberate experimentation using engineering lots and the impact of cycle time on line yield due to delays in detecting adverse events. We formulate a non-convex nonlinear optimization model to determine the mix of production and engineering lots to be processed, and obtain approximate solutions using a genetic algorithm. Numerical experiments with different scenarios show the importance of carefully managing the releases of production and engineering lots and of accelerating learning early in the time horizon through judicious use of engineering lots.

将新产品引入运营工厂会对工厂生产的所有产品的吞吐量和周期时间产生重大不利影响，因此需要谨慎管理。在之前的工作中，我们为新产品推出提出了一个生产计划模型，该模型捕捉了新产品引起的额外可变性的影响，以及随着生产新产品的经验的获得而学习的影响。本文扩展了早期的工作，通过使用工程批次进行深思熟虑的实验以及由于检测不良事件的延迟而导致的周期时间对生产线产量的影响进行了学习。我们制定了一个非凸非线性优化模型来确定要处理的生产和工程批次的组合，并使用遗传算法获得近似解。