Gemini query: So, net, Optilogic’s sequential optimization’s steps are all fixed forecast under the covers
“Yes, exactly. “Under the covers,” every single step of Optilogic’s sequential optimization solver operates on a fixed forecast.
The underlying mechanics execute step-by-step as follows:
[ Fixed Input Demand Forecast ]
│
▼
┌──────────────────────────────────────────────────────────────┐
│ STEP 1: SOLVE PRIORITY 1 │
│ Objective: Maximize Profit │
│ Data Engine: Evaluates variables against the FIXED forecast │
│ Result: Solved Baseline Peak Profit (e.g., $10M) │
└──────────────────────────────┬───────────────────────────────┘
│ (Lock Profit >= $9.5M with 5% Tolerance)
▼
┌──────────────────────────────────────────────────────────────┐
│ STEP 2: SOLVE PRIORITY 2 │
│ Objective: Minimize Lead Time / Service Distance │
│ Data Engine: Evaluates variables against the SAME fixed │
│ forecast, constrained by Step 1’s bounded profit│
└──────────────────────────────────────────────────────────────┘
Why It Works This Way
- Mathematical Determinism: In any Mixed-Integer Linear Programming (MILP) solver—whether Optilogic’s engine, Gurobi, or CPLEX—the demand column () is a static parameter vector. The solver cannot change mid-calculation.
- Sequential Chaining: Step 2 takes the solution outputs (facility flows, routing choices, bound constraints) of Step 1 as its starting state, but the underlying customer demand numbers remain 100% static across the entire chain.
- What Fluctuation Exists is Output-Driven: When Optilogic “chooses” to drop an order or alter a fulfillment path in Step 1 or Step 2, it is not modifying the forecast; it is adjusting a decision variable (e.g., ) to satisfy the current priority objective.
Net-net: The sequential optimization steps are completely deterministic and rely on a fixed forecast baseline throughout.”
QED: OIS will makes sequential optimization most profitable.