OPTIMISATION · SC-23
Operations Simulation & What-If Engine
A discrete-event and Monte Carlo environment for testing operational changes before implementation.
Starting point
Allocation, pricing, scheduling or capacity problems require explicit trade-offs that heuristics often hide.
01 · BUSINESS PROBLEM
Operational decisions become expensive when constraints are handled manually.
Allocation, pricing, scheduling or capacity problems require explicit trade-offs that heuristics often hide.
Competing objectives
Cost, service and utilization pull the decision in different directions.
Real constraints
Capacity, skills, stock or policy limits make naive rules infeasible.
No scenario view
Teams cannot quantify what changes before implementing it.
02 · DECISION LOGIC
Make the trade-off explicit before choosing the action.
The system compares feasible alternatives under the constraints that actually govern operations.
Decision sequence
Each stage answers a different operating question
Represent objectives and constraints.
Create feasible alternatives.
Quantify cost, service and risk.
Return a defensible operating plan.
Decision rule — Optimization is useful when the constraints are as real as the objective.
03 · WHAT CHANGED
From operating constraints to a feasible decision.
The model keeps objectives, constraints, scenarios and outputs inspectable.
Define the decision variables and constraints.
Keep a simple heuristic as a baseline.
Compare feasible scenarios under uncertainty.
Return the plan with the trade-offs visible.
04 · ARCHITECTURE
A modular path from input to decision.
Inputs → preparation → core logic → validation → decision output
SC-23 · SYSTEM ARCHITECTURE
Inputs → preparation → core logic → validation → decision output
Public portfolio implementation
Inputs
Source signals
Capture the operating inputs required by the system. [Python]
Preparation layer
Normalize context and create a stable analytical contract. [SimPy]
Core system
Core engine
Run the main analytical or automation logic. [NumPy]
Decision logic
Apply the rule, model or orchestration logic that changes the decision. [Pandas]
Validation
Validation
Test outputs against explicit quality criteria. [Monte Carlo]
Controls
Keep approvals, thresholds or constraints visible. [Plotly]
Decision output
Decision output
Expose the result in a form the user can act on. [FastAPI]
Monitoring
Record outcomes, exceptions and evidence for iteration. [Docker]
Integration boundaries
Python
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
SimPy
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
NumPy
Defined responsibility inside the system; replaceable if another tool fits the requirement better.
05 · EVIDENCE & ECONOMICS
Measure what changes the decision.
Public implementation, inspectable technical proof and decision-focused validation.
Solution quality
Constraints
9
Representative public example.
Scenarios
4
Representative public example.
Feasible solutions
100%
Representative public example.
Constraint coverage
Baselines
2
Representative public example.
Objectives
3
Representative public example.
Reference economics
€500k
Reference scenario
5%
Illustrative improvement
€25k
Decision value
06 · TECHNICAL PROOF
Review the code behind the project.
Tools used
BUSINESS CONCLUSION
Optimization creates value when it changes the allocation, not when it only produces a better objective function.
The useful result is a feasible action plan with transparent constraints and economics.