Pick path optimization is the process of finding an efficient route for workers or robots to collect items from warehouse storage locations. It matters because travel time often takes up the largest share of order picking labor. A better route can reduce walking distance, speed up order fulfillment, lower fatigue, and improve on-time shipping.
In large warehouses, even small savings per order can add up to major cost reductions each day.
The problem is similar to finding a shortest path through a network of aisles, cross-aisles, storage zones, and packing stations. A route must obey warehouse constraints such as one-way aisles, blocked paths, equipment limits, item priorities, and order batching rules. Common methods include S-shape routing, return routing, largest-gap routing, and algorithmic approaches based on graphs.
Modern systems use warehouse management software, barcode scans, RFID, robots, and real-time data to update routes as orders and inventory change.
Understanding Logistics & Warehouse Systems: Pick Path Optimization
The best route depends on the physical design of the building. Long aisles create a different problem from a warehouse with many short aisles and several cross paths. Fast-moving products are often placed near packing areas because they are requested often.
This is called slotting. Good slotting can make route planning easier before any software chooses a path. Heavy goods may need low shelves or equipment access.
Fragile goods may need to be collected late so they are not crushed. Chilled, hazardous, or secure products can sit in separate zones. A route therefore has to respect the order in which items should be handled, not just the distance between them.
Different routing rules make different tradeoffs. In an S-shape route, a picker enters each needed aisle and leaves from the far end, creating a pattern that uses cross-aisles efficiently when many locations are needed. In a return route, the picker enters an aisle only as far as necessary, then turns back.
This can work well for small orders with items near the aisle entrance. Largest-gap methods examine the longest empty section within an aisle. They try to avoid walking through that empty section.
No single rule wins in every case. A route that works for ten scattered items may be wasteful for two nearby items.
Computer systems treat the warehouse as connected locations with permitted links between them. They can compare many possible visit orders, then select one that meets the rules. This resembles the travelling salesperson problem, where many places must be visited with little unnecessary movement.
Real warehouses add complications. A lift truck may not fit in a pedestrian lane. A one-way aisle cannot be used in reverse.
A robot may need charging time. An urgent order may need to move ahead of a normal order.
Because the number of possible routes grows very quickly, systems often use practical shortcuts rather than testing every possible route. A very good answer found quickly is often more useful than a perfect answer found too late.
Students can see similar ideas in supermarkets, school libraries, hospitals, and delivery routes. A supermarket places popular items so staff can refill shelves efficiently, though customer convenience may lead to a different layout. In a warehouse, a route plan is only as reliable as its data.
If an item is recorded in the wrong bin, the planned route sends someone to the wrong place. Barcode scans confirm picks and help update stock records. Managers should measure walking, waiting, searching, picking errors, congestion, and worker safety.
Reducing distance is valuable, but it should not create crowded crossings, rushed lifting, or confusing instructions. Good optimization balances speed with accuracy, safety, and the real conditions on the floor.
Key Facts
- Total pick time = travel time + search time + pick time + paperwork or scan time.
- Travel time = travel distance / average walking speed.
- Distance saved = old route distance - optimized route distance.
- Percent improvement = (distance saved / old route distance) × 100%.
- A warehouse route can be modeled as a graph where nodes are locations and edges are walkable paths.
- Batch picking reduces repeated travel by grouping multiple orders into one route.
Vocabulary
- Pick path
- A pick path is the route a worker or robot follows to collect items for one or more orders.
- Warehouse management system
- A warehouse management system is software that tracks inventory, orders, locations, and picking instructions.
- Order batching
- Order batching is the practice of grouping several orders so their items can be picked in a single trip.
- Slotting
- Slotting is the process of assigning products to storage locations to improve picking speed and space use.
- Graph model
- A graph model represents warehouse locations as nodes and travel paths as edges so routes can be analyzed mathematically.
Common Mistakes to Avoid
- Minimizing aisle count instead of distance is wrong because fewer aisles can still mean longer travel if the route includes long backtracking paths.
- Ignoring the starting and ending locations is wrong because a route that looks short between picks may be inefficient when the packing station or depot is included.
- Treating all items as equally important is wrong because high-demand products should often be placed closer to packing areas to reduce repeated travel.
- Using a fixed route when conditions change is wrong because blocked aisles, priority orders, and inventory moves can make the original path inefficient or impossible.
Practice Questions
- 1 A picker follows a 420 m route at an average walking speed of 1.4 m/s. How many seconds does the travel portion of the pick take?
- 2 An old pick route is 680 m and an optimized route is 510 m. Calculate the distance saved and the percent improvement.
- 3 A warehouse has one packing station at the front and fast-moving items stored at the far back. Explain how slotting changes could improve pick path efficiency without changing the routing algorithm.