Route Optimiser
Turn warehouse travel into faster, cheaper delivery routes.
Explain the route optimiser in a stakeholder-friendly way, with clear logic, measurable value, and the analytics behind the path search engine.
See the logic
Route Optimiser
Explain the route optimiser in a stakeholder-friendly way, with clear logic, measurable value, and the analytics behind the path search engine.
See the logicThe tool turns a warehouse-style picking map into a smart route plan. It helps teams avoid wasted backtracking, avoid blocked aisles, and compare a basic route against a faster, optimized path.
The system models the space as a grid with open cells, shelf walls, and a single starting point (the door). It also places the items to collect on free cells.
Every walkable square becomes a location on the map. The computer only considers valid moves and avoids obstacles, so it always finds a route that can actually be followed on the floor.
This is the route optimisation view seen on the dashboard: start point, items, and the grid of walkable space.
The optimiser uses proven search algorithms to compute the shortest physical distance between picks, then applies a greedy nearest-neighbour strategy to determine the order. This two-stage approach separates path-finding (avoiding walls) from route-sequencing (minimizing backtracking).
Combines the actual distance traveled and an estimate to the goal. It is the smartest choice for finding a shortest path around shelves and obstacles.
Explores all reachable squares evenly until the shortest path is found. It is reliable when every step has the same cost.
Searches layer by layer from the start. It is simple and effective for grids where moves are equal, and it guarantees a valid path.
Watch how inefficient backtracking (left) becomes an optimized sweep (right). The naive route bounces between distant picks; the optimised route uses nearest-neighbour logic to minimize total distance.
Each coloured line shows a segment of the route visiting one pick point. Red paths show backtracking inefficiency; green paths show optimized clustering. Both routes avoid shelves; order is the only difference.
Visits items in the order they were generated. This is intuitive but wasteful: the route often backtracks to distant items, creating a zigzag pattern. Each jump between unrelated picks adds travel distance.
At each step, chooses the nearest unvisited item. This greedy approach clusters picks together and avoids long jumps. The result is a more logical sweep through the warehouse.
Smart algorithm that combines actual distance travelled with an estimate to the goal. It prunes impossible routes early, making it fast for real-world grids with obstacles. Perfect for warehouses with shelves and aisles.
Explores all reachable cells uniformly, expanding outward like a ripple. Guarantees the shortest path but slower than A*. Works well when all steps have equal cost.
Searches layer-by-layer from start. Simple and guaranteed to find shortest path on uniform grids. Less intelligent than A*, so explores more cells unnecessarily.
This tool makes optimisation tangible: it turns a warehouse layout into measurable improvements in travel time, cost, and reliability.
Use this presentation to walk stakeholders through the business problem, the search logic, and the benefit of choosing a smarter path.
Open the live optimiser