Optimizing Warehouse Picking Routes with Indoor Routing APIs

Order Grouping and Sequencing

Order grouping, often called batching, involves combining multiple customer orders into a single picking batch. This strategy reduces the total distance pickers need to travel, as several orders can be fulfilled simultaneously. Sequencing then determines the most efficient order in which the batches or order lines are picked to further minimize time and distance.

• Genetic Algorithms (GAs) and metaheuristics are popular in solving the order batching problem, especially as it becomes computationally hard (NP-hard) with increased order complexity. These algorithms encode groups of orders as chromosomes and seek combinations that minimize total route length or completion time. They have demonstrated significant reductions (up to 18%) in makespan compared to rule-based heuristics.
  
  
• Rule-based methods (like First-Fit, Best-Fit, and Seed Algorithms) are also widely used for assigning orders to batches, often prioritizing orders based on size or picking location similarity. These approaches seek to balance load and optimize picker efficiency within device or batch capacity constraints.
  
  

Shortest Path Algorithms for Indoor Spaces

Once groupings or batches are defined, shortest path algorithms determine the optimal sequence and route a picker should follow inside the warehouse.

• SPRP (Shortest Path Routing Problem) is a framework that adapts classic pathfinding (like Dijkstra’s or A*) to warehouse environments, considering aisle layouts, cross aisles, and storage positions.
  
  
• Commercial Solvers and Custom Solutions (like OptiRoute™) often extend these algorithms, factoring in real-time constraints such as congestion or temporary blockages.
  
  
• These methods transform the warehouse into a graph, where nodes represent pick locations and edges represent paths. By solving a variation of the Traveling Salesman Problem (TSP) specific to the warehouse’s geometry, the picker’s distance is minimized.
  
  

Clustering Strategies and Wave Picking

Clustering strategies go beyond basic batching by intelligently assigning similar orders or items to the same batch based on proximity or product type, further reducing travel distance.

• Clustering Algorithms segment orders by spatial or temporal similarity so pickers remain within a defined zone for the batch, minimizing zig-zag movements.
  
  
• Wave Picking orchestrates order releases in scheduled “waves,” grouping orders that can be fulfilled together during a specific time window. This increases throughput by aligning picking with dispatch or shipping windows and can be coordinated with clustering algorithms for added efficiency.
  
  

Combined, these algorithms contribute to a holistic optimization approach that helps in grouping orders for batching, sequencing them logically, calculating shortest routes using indoor-aware algorithms, and refining the process with clustering and wave-based release strategies.