decision-optimization-modelinglisted

# Decision Optimization Modeling Translate an operational decision into a falsifiable mathematical model whose recommended solution can be independently checked. Read [references/optimization-modeling-method.md](references/optimization-modeling-method.md) before selecting a formulation or solver. ## Workflow 1. Define the decision owner, controllable actions, entities, horizon, frequency, latency, downstream effects, baseline policy, and terminal decision artifact. Separate controllable choices from forecasts and facts. 2. Declare sets, indices, parameters, units, sources, timestamps, uncertainty, missingness, and lineage. Reject inputs whose meaning or unit cannot be reconciled. 3. Define decision variables and domains before writing the objective. Include state, recourse, slack, and activation variables only when their operational meaning is explicit. 4. State the objective in business or system units. For multiple objectives, declare priority, lexicographic order, Pareto treatment, or calibrated trade-off weights; never hide policy choices inside arbitrary coefficients. 5. Encode hard constraints separately from soft preferences and penalties. Bind every constraint to its operational rule, source, tolerance, and reason for being hard or relaxable. 6. Choose deterministic, scenario-based stochastic, chance-constrained, or robust treatment according to the uncertainty and decision timing. State distributional and independence assumption