The growing need for fast and efficient solution techniques for solving dynamic optimization problems is driven by a broad spectrum of applications in scheduling and control. We propose a novel framework for dynamic optimization that utilizes a multiple shooting “backbone” with discrete rather than continuous subproblems, thereby eliminating need for repeated time-integration. A Lagrangian relaxation (LR)-based decomposition scheme is proposed, which dualizes the state continuity requirements between subproblems and enables parallel solution of the problem. We demonstrate the applicability of our method on two case studies: the Van der Pol oscillator and a batch reactor.

调度和控制中的广泛应用推动了对快速有效的解决方案技术的不断增长的需求，这些技术用于解决动态优化问题。我们提出了一种动态优化的新颖框架，该框架利用具有离散而非连续子问题的多重射击“主干”，从而消除了对重复时间积分的需求。提出了一种基于拉格朗日松弛（LR）的分解方案，该方案将子问题之间的状态连续性要求双重化，并且可以并行解决问题。我们在两个案例研究中证明了我们的方法的适用性：Van der Pol振荡器和间歇反应器。