Guide
Getting Started
SSPLAX models a system's flows, constraints, and objectives. It finds the best feasible plan for the selected objective, then shows which constraints are shaping the result.
The core loop
A typical SSPLAX session follows this pattern:
Pick a starter template for your domain or build a flow network from scratch. The model defines what moves through your system and how stages connect.
Choose what to optimize: maximize throughput, minimize cost, or minimize resource consumption. If there are minimum delivery requirements, set those too.
Set capacity limits, budgets, yield ratios, and cost rates. Record sources, owners, input confidence, and notes separately from uncertainty settings.
The solver returns the baseline answer, next move, active limits, solved flow network, marginal values, and robustness signals. You see which limits shape the answer and what to test next.
Stage Scenario B, use interventions or a plain-language what-if, run pressure and uncertainty checks, explore maps and frontiers, then build a decision brief.
Your first model
To get familiar with the workspace, open an example and change one input.
- 1. Go to Examples and open any scenario — Engineering Capacity is a good one to start with.
- 2. Click Open this model. You'll land in the workspace with the template loaded.
- 3. Pick an objective and proceed to the Constraints screen.
- 4. Drag a constraint slider — say, tighten a budget or lower a capacity limit — and hit Solve.
- 5. The Results screen opens on the baseline answer, then gives you Drivers, Robustness, Compare, Explore, and Report tabs. Start with the binding constraint and Next move.
You've now run a constrained optimization, found the active limits, and seen how the recommendation shifts when an assumption changes. The rest of the guide takes each step further.
Key concepts
These terms appear throughout SSPLAX. For full definitions, see the Glossary.
A stage in your system — a source, service, queue, or sink. Nodes are connected by edges.
A flow path between two nodes. Each edge carries a commodity (like throughput) with min/max bounds, cost, yield, and optional resource consumption.
A limit on the system: a budget cap, capacity ceiling, minimum delivery, or resource limit.
What you want to optimize — maximize throughput, minimize cost, or minimize resource use.
A constraint fully used up in the baseline answer — a binding constraint, in solver terms. Relaxing it can improve the answer.
How much the objective improves when you relax a limit by one unit, also called its shadow price. A high value flags a limit worth a closer look.
How much room is left before a limit becomes active, also called slack. Plenty of headroom usually means the limit is not driving the decision.