Optimal control is a convenient way to develop both supply chain process optimization models and describe the dynamics of process fulfillment. A rich diversity of knowledge has been developed for the integration of optimization and simulation methods with applications to supply chain management at conceptual, informational, and computational levels. At the same time, model-algorithmic integration and alignment frameworks have received less attention. The importance of this level should not be underestimated since synthesis and analysis problems in supply chains imply tight intersections between and within the models (e.g., objective functions and constraint systems). This paper seeks to bring the discussion forward by carefully elaborating on the issues of optimization and simulation model and algorithm integration and providing implementation guidance. Conventionally, optimization has pre-dominantly been used at the planning level while dynamic system control was frequently investigated using simulation models. This study develops an integrated optimization-simulation framework at the model-algorithmic level for the given domain. We offer insights on how to describe planning and control in a unified model-algorithmic complex with consideration of uncertainty factors which are anticipated at the planning and confronted at the control stages. The developed theoretical framework was exemplified by a combined optimization-simulation modelling of the SC design and planning problem with disruption risks consideration in anyLogistix.