Multi-Stop Route Planning: Optimising Complex Delivery Schedules

Why manual planning breaks down as stop count grows

At 5-6 stops, an experienced planner can often eyeball a reasonable route on a map. By 15-20 stops, that intuition starts missing real inefficiencies - a stop that's geographically close but logically out of sequence, a backtrack that isn't obvious from a zoomed-out map view. Beyond that, manual planning tends to fall back on habit ("we always do this neighbourhood first") rather than actual optimisation, because there's no practical way to evaluate the full space of orderings by hand.

The algorithmic approach: construction, then improvement

Most practical route optimisers use a two-phase approach rather than trying to evaluate every possible ordering (which becomes computationally infeasible well before you reach real-world stop counts). First, a construction heuristic - commonly nearest-neighbor, which repeatedly jumps to the closest unvisited stop - builds a reasonable starting route quickly. Second, a local-search improvement pass - commonly 2-opt, which looks for pairs of route segments that can be swapped to shorten the total distance - refines that starting route by removing obvious inefficiencies like crossed paths.

This combination gets very close to optimal in practice, in a fraction of the time a true exhaustive search would take, which is what makes real-time route optimisation possible even for routes with dozens of stops.

Layering in time windows and customer preferences

Multi-stop complexity compounds further once you add customer-specific constraints: a narrow delivery window, a preference for morning vs. afternoon service, a stop that requires a signature and therefore more service time. Each of these shrinks the set of valid orderings, and a good multi-stop planner needs to search for a sequence that's both short and compliant with every constraint simultaneously - not optimise distance first and check feasibility after.

Multi-vehicle complexity: routing and assignment together

Once a business has more than one vehicle, the problem gains a second dimension: not just what order to visit stops in, but which vehicle should visit which stops at all. This is the true Vehicle Routing Problem, and it's typically solved with algorithms like the savings algorithm - which starts with one vehicle per stop and progressively merges routes wherever combining two stops onto the same vehicle saves the most total distance, subject to capacity limits - followed by the same kind of local-search refinement used for single-vehicle routes.

Practical guidance for complex delivery days

For operations running 15+ stops across multiple vehicles regularly, the case for automated multi-stop planning is straightforward: the search space is too large for manual planning to reliably find good solutions, and the gap between a decent manual route and an actually optimised one tends to widen as stop count grows. The practical move is the same one that applies to adopting route optimisation generally - start with your most complex day, compare the optimised plan against what a planner would have built by hand, and let that comparison make the case. Our guide to route planning software for UK multi-drop distributors covers what to look for.