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Warehouse order picking

8 grocery orders. 145 pick items. 5 trolleys. One inspectable walking plan.

Order picking treats warehouse trolley routes as ordered lists: every item, bucket limit, order split, product location, and walking-metre tradeoff stays visible on the plan an operator would actually walk.

8 Orders
145 Pick items
37 Products
5 Trolleys
3 Constraints
2 / 1 Hard / soft split

A picking plan is only useful if items are assigned to ordered trolley routes, the buckets can carry the grouped volume, and the walk is short enough to operate. Treating assignment and route order as separate cleanup steps makes the plan look tidy while order ownership or bucket capacity quietly fails.

The app keeps the isometric warehouse canvas, the trolley legend, the picking-plan cards, the input tables, score analysis, and the retained job on one surface.

Item coverage Every pick item appears in exactly one trolley route.
Bucket capacity Each order's volume fits four buckets on its trolley.
Order incidence Orders are kept on fewer trolleys where possible.
Walking distance Closed-route metres through the shelving grid stay a soft pressure.
Warehouse geometry Shelf, side, and row facts decide the aisle path drawn on the canvas.
Retained workflow Status, snapshots, score analysis, and lifecycle actions stay on the stock contract.
Runtime walkthrough Watch trolley routes appear across the warehouse, then read the picking plan card by card. Narrated walkthrough (2:01): the unassigned warehouse, the solve in progress, the drawn trolley routes, the picking-plan cards, the data panel, and score analysis.
Unassigned warehouse The unassigned baseline shows the warehouse and the demand before any route exists: 8 orders, 145 pick items, no active trolley.
  1. The stats panel states the demand before the solve: orders, items, active trolleys, closed metres.
  2. The warehouse canvas is drawn and waiting; no trolley route exists yet.
Mid-solve Mid-solve, the canvas already shows route ownership and pick markers while the solver keeps improving.
  1. The solver is still improving, with the route plan changing under it.
  2. Trolley paths and numbered pick markers appear as routes are constructed.
Picking workspace The finished plan is one inspectable warehouse surface: ordered trolley routes, numbered pick markers, and the legend that ties colour to trolley.
  1. Coloured paths show trolley ownership; numbered markers expose the pick order itself.
  2. The legend ties each colour to its trolley and item count.
Picking plan Below the canvas, each trolley card states its item count, bucket capacity, and the exact ordered list an operator would walk.
  1. Each trolley card carries its item count and bucket-volume capacity.
  2. The ordered item list is numbered, so the walking sequence is explicit.
Data panel The data panel regenerates the scenario through the published contract: order count, trolley count, and bucket size are inputs, not hidden constants.
  1. Scenario inputs are explicit: order count, trolley count, and bucket count.
  2. Generate New rebuilds the dataset through the same demo-data contract.
Score analysis Score analysis is small here on purpose: three constraints decide the plan, and their weights are readable at a glance.
  1. required_buckets is the hard rule; bucket overflow is not a preference.
  2. order_incidence and route_distance carry the soft pressure.