Policy Scenario Integer Programming — Discrete Optimization Across Policy Alternatives

Stochastic integer programming integrates all scenarios into a single model with explicit probability weights, optimizing an expected or worst-case objective jointly. PSIP solves a separate, independent model for each scenario and then compares results. PSIP is simpler to set up and easier to communicate to stakeholders; stochastic IP is more rigorous when scenarios have known probabilities and decisions must be made before uncertainty resolves.

There is no fixed threshold, but PSIP becomes cumbersome beyond 10–20 scenarios because interpretation complexity grows faster than computational cost. For large scenario sets, consider clustering scenarios or switching to two-stage stochastic programming.

Yes. Most IP solvers handle mixed integer programs with binary (0/1) and general integer variables simultaneously. The scenario structure applies to problem parameters, not to the variable types.

Infeasibility under a scenario is itself valuable information — it signals that the policy imposes constraints that cannot be simultaneously satisfied. Report infeasible scenarios explicitly and use constraint relaxation or feasibility analysis to identify the binding constraints.

The objective function structure is usually kept constant so results are comparable, but cost coefficients or weights can be scenario-specific. Changing the objective form across scenarios makes cross-scenario comparison methodologically difficult.