Weighted Objective Functions — An Interactive Transport Planning Model
Change what the planner is told to value, and watch the “best” route plan change with it. An objective function is a policy decision disguised as arithmetic.
Open simulation →What this simulation shows
Large Neighbourhood Search improves a route by temporarily destroying part of an existing solution and then repairing it. That larger intervention lets the optimiser escape the local traps where small route changes stop producing useful improvements.
The simulator uses a Wakefield depot and a set of illustrative Yorkshire deliveries. It is designed to make the central idea visible: a plan can look good locally while its structure prevents a better overall arrangement from being discovered.
How to use it
Choose a destroy operator to decide which deliveries are removed from the current plan. Then choose a repair strategy to decide how those deliveries are inserted again. Run the scenario repeatedly and watch the current, best, and initial route scores change.
The important question is not whether every iteration improves the score. A useful search sometimes accepts a worse intermediate arrangement because it creates a route structure from which a better solution can be found.
My Name Is Earl optimisation series
Large Neighbourhood Search
A Wakefield depot serves deliveries across Yorkshire. The current plan is deliberately damaged, then repaired to see whether a better set of routes emerges.
Initial—
Current—
Best—
Improvement—
Wakefield depot Delivery Removed during destroy phase
Leaflet-inspired schematic · route distances use the town coordinates shown
Iteration 0
What this result means
The initial plan is the baseline. Run an iteration to see whether destroying and repairing part of the plan finds a shorter set of routes.
The product lesson
Large Neighbourhood Search is a useful model for decision-support software because it exposes the trade-off between preserving a familiar plan and exploring a better one. A routing tool should help planners understand what was changed, why the new arrangement was considered, and whether the improvement survives the operational constraints that matter in practice.
How to use this simulation
Change one input at a time, run the model, and compare the result with the starting state. Then repeat the experiment with a different constraint or strategy so you can see which relationships drive the outcome.
What to look for
Look for trade-offs, thresholds, feedback loops, and points where a locally attractive decision produces a worse system-wide result. The simulation is intended to make the article's idea observable, not to predict a real operation.
Limitations
This is a deliberately simplified model. It omits the data quality, exceptions, human judgement, and operational constraints of a live system, so treat its behaviour as an illustration of a mechanism rather than as a planning recommendation.
Read the related article
Read: My Name Is Earl and Large Neighbourhood Search →
Explore the complete Transport Optimisation Explained series