MRP Theory combines the use of Input–Output Analysis and Laplace transforms, enabling the development of a theoretical background for multi‐level, multi‐stage production–inventory systems together with their economic evaluation, in particular applying the Net Present Value principle (NPV). In a recent paper (Grubbström et al., 2010), a general method for solving the dynamic lotsizing problem for a general assembly system was presented. It was shown there that the optimal production (completion) times had to be chosen from the set of times generated by the Lot‐For‐Lot (L4L) solution. Thereby, the problem could be stated in binary form by which the values of the binary decision variables represented either to make a production batch, or not, at each such time. Based on these potential times for production, the problem of maximising the Net Present Value or minimising the average cost could be solved, applying a single‐item optimal dynamic lotsizing method, such as the Wagner–Whitin algorithm or the Triple Algorithm, combined with dynamic programming. This current paper follows up the former paper by investigating the complexity defined as the number of possible feasible solutions (production plans) to compare. We therefore investigate how properties of external demand timing and properties of requirements (Bill‐of‐Materials) have consequences on the size of this solution space. Explicit expressions are developed for how the total number of feasible production plans depends on numbers of external demand events on different levels for, in particular, the two extreme cases of a serial system and a full system (the latter, in which items have requirements of all existing types of subordinate items). A formula is also suggested for general systems falling in between these two extremes. For the most complex full system, it is shown that the number of feasible plans will be the product of elements taken from Sylvester's sequence (an instance of doubly exponential sequences) raised to powers depending on numbers of external demand events.
