Problem:
Kuehne+Nagel needed to design a new warehouse and choose the best algorithm for multi-order picking. Workers used trolleys with capacity and weight constraints, which limited how many cartons could be picked at the same time. A strict picking algorithm was required to keep routes efficient and maintain high picking accuracy.
Solution:
The team built a detailed warehouse simulation to test and validate different picking strategies. The model represented the warehouse layout, trolley movements, operator behavior, and real order data. Picking routes were optimized using two criteria: maximizing cartons per tour and maximizing article overlap within each tour.
Results:
- Increased trolley utilization from 58% to 94%.
- Validated the optimal picking algorithm before warehouse launch.
- Reduced total picking distance and tour duration.
- Improved service level within existing equipment constraints.
- Provided data to justify investment decisions for the client.
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.
Warehouse Layout and 3D Animation
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.