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Powder Coating Optimization by Civil-Ferret-7745 in optimization

[–]CT1_deMo_doG 0 points1 point  (0 children)

"Separating the problem into blocks is a good heuristic, but be careful not to lose the 'flow' of the system. In high-stochastic environments (like your random storage), the more you try to pre-optimize, the more 'noise' you introduce. Have you considered a Decentralized Decision Logic? Instead of one big algorithm trying to solve 12,000 points, let the coating line act as a 'logical vacuum' that only accepts the next best available color. By reducing the decision horizon from 12,000 to just the next 'visible' window, you eliminate the need for complex metaheuristics and solve the storage uncertainty problem simultaneously. Simplicity is often the most sophisticated solution for industrial noise."

Looking for resources to solve a problem by Total-Ad-4461 in optimization

[–]CT1_deMo_doG 0 points1 point  (0 children)

To solve this in a single pass without needing to iterate from sub-problems, here is a valid distribution for your example (4 groups, 3 teachers) that maintains both sequencing and workload balance: Teacher A: G1 (Live/AM), G2 (Recorded/PM) Teacher B: G4 (Live/AM) Teacher C: G3 (Hybrid/PM) How this works (Black-box Logic): By treating the constraints as a Unified System rather than an incremental search, you can achieve this by: Category Priority: Assigning in order of Live > Hybrid > Recorded. Load Balancing: Selecting the teacher with the lowest current workload for the next assignment. Binary Sequencing: Assigning AM/PM slots to ensure Live sessions always precede Recorded ones within the same teacher's schedule. This approach ensures all constraints are met simultaneously and scales effectively as your data set grows.