Picking path optimization is the process of determining the most efficient route warehouse pickers should take to retrieve products for order fulfillment. By reducing unnecessary travel between picking locations, warehouses can improve picking speed, labor efficiency, and overall productivity.
Key Takeaways
- Picking path optimization is a critical aspect of warehouse management, with picking accounting for over 50% of a warehouse’s labor efforts. Efficiently optimizing this process can significantly boost warehouse productivity.
- Heuristics and algorithms are essential tools in the optimization process. While heuristics are practical, ‘good enough’ solutions for complex problems, algorithms provide step-by-step solutions to specific problems.
- Two core algorithmic problems underpin the picking path optimization process: The Traveling Salesman Problem and the Shortest Path Problem. Several algorithms, like Dijkstra’s algorithm and the Ant Colony Optimization algorithm, have been developed to tackle these issues.
- Automated applications exist to run these algorithms for picking path optimization, although the selected tools must align with the warehouse’s specific optimization goals.
- Warehouse management software, such as Logiwa WMS, can significantly aid in the process of pick path optimization, improving overall warehouse efficiency.
Managing and optimizing a warehouse in the 21st century is a whole new ballgame.
In the past, it was enough to keep a clean, well-organized facility and schedule a decent number of pickers.
Now, warehouses stay competitive by optimizing every possible area of work—from picking to packing to shipping. With new warehouses cropping up every day, and customer demand for speedy deliveries rising, order fulfillment centers don’t have the option of phoning it in. They must analyze their warehouses from top to bottom to look for inefficiencies and nip them in the bud.
One warehouse process that’s ripe for warehouse optimization is the pick path. According to some estimates, picking takes up over 50% of a warehouse’s labor efforts. This isn’t surprising considering that, despite warehouse technological advancements in areas like automated storage and retrieval systems, picking is still a largely human-led process.
Nevertheless, it’s possible to further optimize picking and, more specifically, the pick path. Oftentimes, the biggest cause of inefficiency during the picking process is motion waste – the unnecessary movement that makes a given task take longer than it should.
Walking path optimization—or picking path optimization, depending on who you talk to—is the process of finding the fastest way to navigate the warehouse in order to pick products quickly, accurately, and efficiently by using various picking methods such as wave picking, zone picking.
In this article, we will walk you through some methods for walking path optimization for pickers:
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How Heuristics Support Picking Path Optimization
If you research algorithms to optimize any area of warehouse management, you’ll likely hear the word “heuristic” a lot. It’s one of those words tossed in to confuse readers into throwing their hands up in the air and giving up on the entire endeavor.
In the context of warehouse optimization, a heuristic or heuristic technique is a method of accomplishing a specific goal that is suitable for practical purposes, but isn’t guaranteed to be perfect.
For example, when you apply a rule of thumb or make an educated guess, you’re using a heuristic technique.
When talking about warehouse optimization, researchers come up with processes and algorithms designed to address warehouse inefficiencies without necessarily producing a perfect approach. This is because at the core of many warehouse management problems are difficult math problems that are still being worked out by academics.
If the warehouse industry waited for a perfect solution to every math problem at the foundation of warehouse management problems, (see: bin packing and knapsack problem) we’d be working in very inefficient warehouses for a very long time!
So, whenever you hear someone use the word “heuristic,” remember that they’re just referring to an “okay for now” method. It’s a way of saying the solution isn’t perfect. In fact, you can probably ignore the term altogether whenever you see it.
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How Algorithms Support Picking Path Optimization
“Algorithm” is another word that’ll pop up often when you read about topics like picking path warehouse optimization. It sounds complicated, but it’s not. An algorithm is just a sequenced list of instructions. When you bake a cake, you use a recipe and that recipe can be considered an algorithm. If you were to build a machine that created a cake for you from scratch, and all you needed to do was upload the recipe, you’d essentially be feeding your “cake-making machine” an algorithm that it would use to make a dessert. The algorithm would let the machine know that it needs to crack the eggs before putting them in the bowl. In warehouse optimization scenarios, there are algorithms for any number of processes. In the case of walking path optimization, a warehouse manager may have an algorithm where he or she can plug in certain variables and obtain an optimized pick path.
Order picking accounts for 60% of your warehouse operational costs. Make sure you’re using the most cost-effective order picking strategy by reading our detailed guide where we compare the 3 most common order picking methods.
Two Core Problems in Picking Path Optimization
Remember when we mentioned that larger math problems sit at the center of many warehouse optimization problems? The same idea applies to the walking path optimization process. In this area, we’re faced with two big algorithmic problems:
- The Traveling Salesman Problem
- The Shortest Path Problem
Both problems are tricky algorithmic problems to solve when faced with a large data collection set. This is why industry professionals devise “heuristics” to provide workable solutions in a warehouse productivity context.
The Traveling Salesman Problem
While the traveling salesman job may be dying out, the traveling salesman problem is still alive and kicking. Suppose you’re a traveling salesman with several locations to visit. How do you take the shortest (quickest) route to visit all of these destinations once and ultimately wind up back in your starting city? Now, you could figure this out through trial and error, which is known as the “brute force” method. If you’re only hitting four or five locations, you map out every possible route and pick the shortest route. But what if you’re hitting up dozens of locations spread out across a large geography? In the time it takes you to map out every possible route and pick the best one, you could’ve completed your trip. Normally, this is where algorithms come in to make life easier. You’ve got a problem. You plug in the variables. The algorithm does the time-intensive number-crunching quickly and boom, you’ve got an answer. But the traveling salesman problem is classified as an NP-hard problem, which means it’s rather difficult to solve in a reasonable amount of time. This is why “heuristics” exist. They’re our “good enough” solutions to keep things moving.
The Shortest Path Problem
As the name implies, the shortest path problem is about finding the shortest path between two points, also known as “nodes” or “vertices” as they’re referred to in graph theory. Connecting these vertices are lines or “edges.” The shortest path problem finds the shortest path between two nodes in a weighted graph—a graph where the edges (the lines between two points) have a specific value. That value could be the distance or even the cost. In other words, a weighted graph represents the labor cost associated with moving through your warehouse, and the shortest path problem is about finding the quickest, cheapest way to move from one point to the next. When the values are positive, the shortest path problem is considered solvable in a reasonable amount of time, unlike the traveling salesman problem. Since most warehouse managers aren’t in the habit of labeling graphs with negative dollar values or negative measurements, that’s good news. In fact, a number of algorithms exist for tackling the shortest path problem.
Algorithms Used for Picking Path Optimization
As you can probably tell by now, pick path optimization requires warehouses to address both the traveling salesman problem and the shortest path problem.
- You must solve the traveling salesman problem for all storage spots in a warehouse. The start and endpoint is the shipping area.
- Simultaneously, as you move through the traveling salesman problem while filling the order, you also must address the shortest path problem while moving from a given item location (node or vertices) to all other item locations.
In other words, you need to find the shortest point between all the nodes before you’re able to find the shortest path through all the nodes.
This is because there’s no guarantee that every node is connected with one edge, which is necessary for a standard approach to the traveling salesman problem. As a result, you must find the shortest distance between each node first.
There are a number of different algorithms for each problem.
| Traveling Salesperson Problem | Shortest Path Problem |
| Exhaustive Search AlgorithmThis approach considers every possible tour path. If the exhaustive search method is completed, it will definitely find the shortest route, but it is highly complex and unviable with a large amount of data.The exhaustive search method is not considered an efficient algorithm for picking path optimization. | Dijkstra’s AlgorithmThe shortest distance between a select number of starting points and all other vertices is found.It’s a useful and popular starting point for picking path optimization. As a result, there has been an extensive amount of research on this algorithm resulting in enhancements like:
|
| Nearest Neighbor AlgorithmThis is a straightforward approach in which you start at the point closest to your starting point and continue through your path by moving to the nearest item. Naturally, there’s no guarantee that you’re going to take the best route this way. It just eliminates having to think about the process.This is not a recommended approach for warehouse picking path optimization. | Floyd’s AlgorithmThe optimal distance between all points is found.While allowing a person to quickly move on to solving the traveling salesperson part of the problem, Floyd’s algorithm isn’t considered as efficient as Dijkstra’s algorithm for picking path optimization. |
| Multi-Fragment Heuristic AlgorithmThis algorithm considers the edges of a graph (or distances in the warehouse layout) rather than the vertices (points or storage locations in a warehouse layout). It sorts the edges by their weightings to find the shortest distance.While the multi-fragment heuristic algorithm is considered a better approach than the nearest neighbor strategy, it doesn’t promise any accuracy.This is not a recommended approach for warehouse picking path optimization. | |
| Ant Colony Optimization AlgorithmAnts take off in random directions to find a food source, leaving behind pheromones as they travel to and from the source. The more pheromones, the better the travel route, so more ants use this path. In a warehouse, if workers first take off in random directions and then communicate the results of their path in real-time, the optimal pick path can be found in a reasonable time frame.This is a recommended approach in terms of its accuracy, but there are reservations because of how difficult it can be to execute. | |
| Twice Around the Tree AlgorithmThis algorithm uses a spanning tree to find an optimal route by generating a list of vertices while walking around the spanning tree.It’s a recommended approach for picking path warehouse optimization due to the amount of time it takes and its accuracy level. | |
| Christofides’ AlgorithmAn enhancement of the twice around the tree algorithm that uses a minimum spanning tree to create a Hamiltonian circuit (a path that visits every point once). |
Warehouse Layout Optimization. Optimize storage locations, aisle configurations, and picking areas to reduce picker travel and create more efficient picking paths.
Can Picking Path Optimization Algorithms Be Automated?
Researchers at the Edinboro University of Pennsylvania have outlined a small case study of how these algorithms can be applied to a pick path optimization project.
Using a C# Windows application, a warehouse manager can create a graph representing the warehouse layout. They’d then ask the application to use the nearest neighbor approach to solve for the traveling salesman challenge and then use Dijkstra’s algorithm to solve for the shortest path part of the challenge.
The problem with using this application is that it doesn’t employ the recommended algorithms. As explained earlier, the nearest neighbor algorithm is not the most accurate way to determine the shortest route.
Pick Path Optimization Is A Math-Based Approach To An Old Problem
Pick path optimization can be a lot of work, particularly for those who aren’t a fan of algorithms. But, understanding algorithms can help you understand “heuristics” or rules-of-thumb for optimizing your warehouse.
While easy-to-use applications for these algorithms may be hard to come by, it is possible to start applying tactics inspired by these methods. As the warehouse industry becomes more competitive, continually acquiring knowledge about optimization techniques is non-negotiable.
Ready To Optimize Picking Paths for Warehouse Efficiency?
Logiwa warehouse management software optimizes the directed putaway and picking path, saving your order pickers’ effort and speeding up your warehouse operations. Schedule a WMS demo of Logiwa today and learn more..
Picking Path Optimization FAQs
What is picking path optimization?
Picking path optimization is the process of finding an efficient route for warehouse pickers to retrieve products for order fulfillment. Optimized pick paths can reduce unnecessary travel, improve picking speed, and increase warehouse productivity.
Why is picking path optimization important?
Picking path optimization helps reduce the time and labor spent traveling between inventory locations. More efficient routes can reduce motion waste, improve picking productivity, and help warehouses process orders more efficiently.
What are heuristics in picking path optimization?
Heuristics are practical methods or techniques used to accomplish a specific goal, even though they may not guarantee a perfect solution. When it comes to walking path optimization, heuristics provide workable solutions to address inefficiencies without the need for complex mathematical calculations. They serve as rule-of-thumb approaches to optimize walking paths for pickers in warehouses.
How are algorithms used for picking path optimization?
Picking path optimization algorithms calculate efficient routes based on factors such as warehouse layout, item locations, and travel distances. They help determine how pickers can move between required locations with less unnecessary travel.
What problems do picking path optimization algorithms solve?
The two main algorithmic problems in picking path optimization are the Traveling Salesman Problem and the Shortest Path Problem. The Traveling Salesman Problem involves finding the shortest route to visit multiple locations and return to the starting point, while the Shortest Path Problem focuses on finding the shortest path between two points in a weighted graph. These problems are challenging to solve efficiently, but heuristics and algorithms offer viable solutions.
What algorithms can be used for picking path optimization?
Several algorithms can be used for walking pick path optimization, including Dijkstra’s algorithm, the Nearest Neighbor algorithm, Floyd’s algorithm, the Multi-Fragment Heuristic algorithm, the Ant Colony Optimization algorithm, the Twice Around the Tree algorithm, and Christofides’ algorithm. Each algorithm has its own approach and level of accuracy, and the choice depends on the specific requirements and constraints of the warehouse.
Can picking path optimization be automated?
Yes. Warehouse software can automate picking path optimization by using inventory locations, warehouse layouts, and order data to calculate efficient picker routes. The effectiveness of automated routing depends on the optimization methods and operational data used.
How can a WMS support picking path optimization?
A warehouse management system can support picking path optimization by coordinating inventory locations, orders, and picking workflows. This can help reduce picker travel, improve labor efficiency, and create more efficient fulfillment processes
