We consider the problem of determining an optimal release schedule for a production facility as it transitions from producing an older product to a newer one. We use simple queueing ideas to model the impact of the new product introduction on the throughput rate and cycle time of both products. We then incorporate the learning gained from accumulated experience in producing the new product to formulate a deterministic optimization model for release planning during product transitions. Since the model is nonlinear and non-convex we use a genetic algorithm to obtain near-optimal solutions. The structure of the optimal solution provides insights into the relationships between the different costs involved in the model. Computational experiments show that the release planning model produces high-quality solutions in reasonable CPU times, and reflects the behavior of the system realistically. The principal insight is that careful management of releases during product transitions has the potential to mitigate many of the adverse effects frequently observed in practice such as longer cycle times for both old and new products.

中文翻译：

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新产品推出期间容量争用的简单模型

我们考虑在生产设施从生产旧产品过渡到新产品时确定最佳发布计划的问题。我们使用简单的排队思想来模拟新产品推出对两种产品的吞吐率和周期时间的影响。然后，我们结合从生产新产品中积累的经验中获得的知识，为产品转换期间的发布计划制定确定性优化模型。由于模型是非线性和非凸的，我们使用遗传算法来获得接近最优的解。最佳解决方案的结构提供了对模型中涉及的不同成本之间关系的洞察。计算实验表明，发布计划模型在合理的 CPU 时间内产生了高质量的解决方案，并真实地反映系统的行为。主要见解是，在产品转换期间仔细管理发布有可能减轻许多在实践中经常观察到的不利影响，例如新旧产品的周期时间更长。