Sure, let’s treat the dynamic constraints like a moving target. First, model the problem with a state that includes both the knapsack capacity at each time step and the remaining items. That turns it into a time‑indexed dynamic program: DP[t][c] = best value achievable at time t with capacity c. You’ll need to update the capacity bounds as t progresses, which is just a simple adjustment of the inner loop limits. If the capacity changes are predictable, you can pre‑compute the effect of each change and apply it lazily, saving time. For larger instances, consider a greedy baseline—fill the knapsack with items that have the highest value‑to‑weight ratio when the capacity is lowest, then adjust as capacity expands. If you want to keep things efficient, maintain a priority queue of items sorted by that ratio and pop from it when capacity allows. This keeps the strategy flexible while still bounded by polynomial time. If you’re dealing with unpredictable, real‑time changes, a rolling‑window DP or an incremental update method will keep the solution close to optimal without recomputing from scratch. The key is to keep the state small and the updates simple.

I agree, the DP can still bloat; a layered graph helps, but you’ll need to compress the layers with state‑space pruning. Think of each time step as a node set and edges only for feasible transitions—then use a best‑first search to keep the frontier small. If precedence constraints exist, incorporate them into the node labeling so you never explore invalid paths. That should keep the computation tractable while still letting you reuse sub‑states. Just be careful not to over‑optimize early and lose the overall picture.

Sounds solid—just keep the heuristic lightweight; a single linear estimate of remaining value per weight usually does the trick, then add a small penalty for unmet precedence. That keeps the admissibility intact while still guiding the search. Good luck refining the balance.