This study theorizes how the optimal control paradigm can be applied to modeling and algorithmic solutions in manufacturing systems with dynamically changing hybrid structural-logical-terminal constraints. Our study conceptualizes and operationalizes a unique class of problems with both flexible machines and flexible jobs that can be frequently encountered in manufacturing systems with individualized products when process and schedule are created simultaneously. We offer a model to schedule jobs in manufacturing systems when the structural-logical terminal constraints are changing dynamically , and an algorithm to obtain a tractable solution analytically within the proven axiomatic of the optimal program control and mathematical optimization. We develop a dynamic decomposition methodology for modeling and control of schedules in highly flexible production systems combining the advantages of continuous and discrete optimization. The findings suggest that our approach can be of value for approaching problems with a simultaneous process design (i.e., task composition) and operation sequencing (i.e., service composition). Utilizing the outcomes of this research could also support the consideration of dynamics in the operations control. The operations execution can be accurately modeled in continuous time as state variables the updates of which allow for data-driven control of machine availability and capacity disturbances. Besides, the method developed theorizes further generalized insights into decomposition methods for scheduling and is supported by an analytical analysis and an algorithmic realization.