Warehouse Travel Optimization

Reduce travel 30-70% with AI-based batch and pick path optimization

Labor is the single largest operating cost in most DCs, and travel often accounts for half of all labor time, especially in order picking. Utilizing an exclusive AI-based process called Dynamic Work Optimization, Lucas helps you reduce travel 30-70% by optimizing assignments of work and by defining optimal pick sequences or warehouse pick path and travel optimization.

Dynamic Work Optimization has allowed customers to more than double picking productivity and cut labor hours in half without making any changes to their warehouse layout or adding expensive automation or robotics.

Key Takeaways: 

  • AI creates optimized work assignments and pick paths.
  • AI travel optimization reduces warehouse travel by thirty to seventy percent.
  • Intelligent batch picking utilizes advanced mathematical models over FIFO.
  • Waveless order release minimizes turnaround times for continuous streaming.
  • AI-based batch and pick path optimization doubles productivity and halves labor hours.

How Does Pick Path & Travel Optimization Work?

Lucas uses order, inventory, and location information from WMS and other systems and Jennifer™ – the brain, voice and orchestration engine of the Lucas system – applies real-time optimization algorithms and warehouse picking strategies to create optimal work assignments. Jennifer™ then uses a virtual model of your facility to define an optimal pick sequence or travel path.

Maximize pick density with dynamic prioritization

Jennifer™ prioritizes all available work assignments based on rules that you can configure and adjust throughout the day or week. Rules can be based on ship dates, routes, carriers, order types and more. Watch the reducing travel in food service webinar →

Skyrocket productivity with intelligent batch picking

Rather than following a simple first-in-first-out batching method, Jennifer™ uses mathematical models to create optimized batches of pick lines and assigns them to pickers on-demand, as they request work. In contrast to typical WMS batching rules, the Lucas models weigh a variety of factors to drive batch picking optimization, including:

1) Order priority
2) Product locations
3) Product dimensions
4) Starting and ending points
5) Potential travel path
6) User permissions

Read more about intelligent batching →

Cut travel in half with pick path optimization

After batches are created, Lucas applies multiple algorithms to determine an optimized path for the user to take through the warehouse to complete their work. The algorithms consider aisle directions (one-way aisles, for example), base item designations, and other factors to determine the most efficient pick path.

Optimize robots and humans simultaneously

While Jennifer™ orchestrates and optimizes within picking zones, a DC can add robots for conveyance between pick zones or other staging locations. This can further reduce worker travel to and from fixed drop off or induction points. Likewise, Jennifer™ can be used to optimize the travel of robots for full pallet moves, both in inbound and outbound operations.

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Minimize turnaround times with waveless order release

As an added bonus for DCs processing higher volumes of ecommerce orders, the solution supports continuous order streaming or so-called waveless order release. Waveless picking eliminates the constraints of “waves” and works well for any operation that needs to handle a continuous stream of incoming orders with short delivery timeframes.

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Free travel optimization demo

Lucas Dynamic Warehouse Optimization reduces travel by 30-70 percent in batch or cluster-picking operations. In combination with the complete Lucas warehouse optimization solution, several customers have seen greater than 100% improvements in productivity and 50 percent or more significant reductions in picking hours per shift.

This 8-minute on-demand demo will describe our unique approach to reducing travel and explain the process that enables dramatic picking productivity increases.

Frequently Asked Questions

Travel optimization is the use of AI and software algorithms to minimize the distance and time warehouse workers spend walking or driving between pick locations. In a typical distribution center, travel represents the single largest portion of a picker's shift, with workers averaging 5–15 miles per day. Travel optimization uses intelligent batching, pick path algorithms, and dynamic prioritization to increase pick density (more picks per trip) and compute shorter routes through the facility, all without requiring physical layout changes or fixed automation infrastructure. 

By reducing the distance workers travel between picks, travel optimization allows the same workforce to complete significantly more work in the same number of hours. Lucas's Dynamic Work Optimization engine has been shown to reduce travel 30–70% compared to conventional picking methods, with some customers more than doubling pick rates. This means fewer labor hours per order shipped, lower overtime, and reduced need for additional headcount, delivering savings that can reach seven figures annually without any investment in conveyors, robotics, or facility modifications. 

Travel optimization improves picking efficiency in three ways. First, intelligent batching groups orders with overlapping pick locations into a single assignment, increasing pick density. Second, travel path optimization algorithms compute the shortest possible route through assigned locations rather than following a fixed aisle-by-aisle sequence, typically saving 10–15% in travel distance on path optimization alone, with an additional 15–30% from batching. Third, dynamic prioritization continuously re-evaluates available orders in real time so that every new assignment is the most efficient batch possible at that moment. 

Lucas's travel optimization is powered by AI and machine learning algorithms embedded in the Dynamic Work Optimization engine. Key technologies include: a virtual warehouse map that models facility layout, aisle restrictions, and distances; real-time optimization algorithms that evaluate millions of order combinations in under a second; pick path computation using multiple pathfinding algorithms running in parallel; and voice-directed execution through Jennifer™ that guides workers hands-free along optimized routes.  

The solution runs on standard Android mobile devices and can also coordinate the movements of autonomous mobile robots (AMRs) alongside human workers. 

Travel optimization integrates with any WMS through our standard set of APIs that connect your WMS to the Lucas Warehouse Optimization Suite. The system receives order data, inventory locations, and product attributes from the WMS, then applies its optimization algorithms to create intelligent batches and optimized pick paths before directing work to the floor. No changes to the WMS are required.  

The optimization layer sits between the WMS and the voice execution, adding intelligence without disruption. Integration is fast and productionized, with a plug-and-play API library that enables dynamic optimization from day one of deployment. 

Key metrics that improve include: picks per hour (often doubling in cart-pick operations), travel distance per assignment (reduced 30–70%), units per labor hour, order throughput, and overall labor cost per order shipped. The system also tracks and improves adjusted productivity rates, task completion times, and travel time as a percentage of total shift time.  

Additionally, because optimization supports waveless order processing, order cycle time and on-time shipment rates improve as work is continuously prioritized and released rather than waiting for wave completion.