# Warehouse Simulation for Choosing Optimal Picking Algorithm

• Warehouse Operations

Kuehne+Nagel, a leading global provider of logistics solutions, was involved in planning a new warehouse for one of their clients. The warehouse would process 13K order lines or 750 picking cartons per day. The project included the development of the best algorithm for multi-order picking. It was planned that the orders in the warehouse would be served by workers with trolleys (or fangos). Workers with trolleys would pick the goods and put them in cartons by order. Kuehne+Nagel experts used AnyLogic simulation to choose the right algorithm for building optimal picking tours.

### Problem:

Trolleys planned for usage in this warehouse (see the picture) can carry up to 8 cartons at a time, 4 of which are positioned on the trolley’s weighing scales. Weighing scales are used to increase picking accuracy by issuing an alarm signal when the weight of the picked goods does not match that in the master data.

The operator is able to fill only those cartons positioned on the scales. When a carton on the scales becomes full, he swaps it with the next empty one that he carries. So, only 4 cartons are always available for simultaneous filling. In addition, the articles for one carton can be stored at any location along the operator’s route.

These were the reasons the warehouse needed a strict algorithm for building optimal picking tours to service incoming orders.

### Solution:

The Kuehne+Nagel experts came up with the required algorithm. Their idea was that an operator’s picking route would always be straight, so that the operator would never have to return back after swapping cartons. This means that the maximum number of cartons (8) cannot always be assigned to one tour. For example, one carton can contain articles from both the first and last locations of the route, so it cannot be swapped until it is full.

The experts built an AnyLogic simulation model of the warehouse to test and validate the suggested algorithm using real historical data. The detailed model reflected the physical layout of the warehouse, articles’ places of storage, movements of trolley operators, incoming orders, trolley occupation, and service level. The operators moved and picked goods according to the suggested algorithm.

The experts optimized operators’ routes by two criteria:

• Maximizing average cartons quantity per tour.
• Maximizing article overlap in each tour (picking the same article for multiple cartons in one tour is preferable).

The modelers uploaded an Excel file containing 260K of real order data from March 2014 to the model, and then ran the model using this as input data. Carton building (assigning different order lines to different cartons according to picking sequence) was done in the corporate warehouse management system.

The output statistics included the average number of cartons filled per tour, total duration of serving orders, total tours’ distance, average trolley utilization, and mean time of a tour.

### Results:

The statistics received from the model were then compared to the March 2014 statistics from the old warehouse. The result of the warehouse operation’s simulation showed that with the suggested layout configuration, equipment, and movement algorithm, the trolley utilization rate would rise from 58% to 94%.

These results will be used by Kuehne+Nagel to prove the investment efficiency for the client.

Also, the model will be used for choosing the right warehouse layout and article distribution among the warehouse. The developers will also vary trolley numbers to find the best balance between service level and staff workload.

## Similar case studies

• ### Modeling Operations at Pharmaceutical Distribution Warehouses

Cardinal Health, a billion dollar pharmaceutical distribution and logistics firm, manages multiple products from brand name pharmaceuticals and generic drugs to over the counter drugs, health & beauty items and their own private label. They face a multitude of typical distribution warehouse challenges that are further complicated by the nature of pharmaceutical products. Brian Heath, Director of Advanced Analytics at Cardinal Health, and an experienced user of AnyLogic software, employed agent based modeling to solve various business problems, saving Cardinal Health over \$3 Million annually.
• ### Evaluating Introduction of Warehouse Automation Systems

Symbotic is a warehouse automation solutions provider. Its systems are based on mobile robots that can travel freely throughout a dense storage structure, accessing products in all locations and handling them at a very high throughput rate. The company needed a tool to help their customers learn the impact of warehouse reorganization and compare capital investments against expected operational savings before the actual introduction of automation systems.
• ### Choosing the Right Warehouse Layout for a Leading FMCG Retailer

One of the biggest FMCG retailers in Eastern Europe supplies to several hundred shops, across multiple regions, through a single distribution center. The company planned to change the arrangement of pallet racks and conveyors in the center. To evaluate the capacity of the new layout and to measure the effectiveness of the warehouse, they decided to have the AnyLogic Company build a simulation model of the warehouse.