Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse

Area of Science:

• Operations Research
• Logistics Management
• Industrial Engineering

Background:

• Dense mobile racks enhance storage efficiency but complicate order picking.
• Optimizing order picking processes is crucial for reducing operational time costs in warehouses.

Purpose of the Study:

• To develop an optimization strategy for order picking in dense mobile rack systems.
• To minimize the total picking time by optimizing order cluster picking and rack movement.

Main Methods:

• Hierarchical clustering to divide multiple orders into batches based on channel location.
• Development of a mathematical model for virtual order clusters to optimize picking and rack movement.
• Application of a hybrid genetic algorithm to solve the optimization problem.

Main Results:

• The proposed model and hybrid genetic algorithm effectively optimize order picking in dense mobile rack warehouses.
• Significant reductions in picking time can be achieved through intelligent batching and rack positioning.
• Analysis of algorithm characteristics provides valuable insights for practical implementation.

Conclusions:

• The study provides a robust framework for optimizing order picking in dense mobile rack environments.
• The hybrid genetic algorithm offers an effective solution for minimizing picking time.
• This research serves as a reference for addressing order picking optimization challenges in modern warehouses.