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Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse.
Jianglong Yang1, Li Zhou2, Huwei Liu1
1School of Management Engineering, Capital University of Economics and Business, Beijing, China.
Plos One
|April 5, 2021
Summary
Optimizing order picking in dense mobile racks significantly cuts time costs. Hierarchical clustering and a hybrid genetic algorithm minimize picking time for warehouse efficiency.
Area of Science:
- Operations Research
- Logistics Management
- Industrial Engineering
Background:
- Dense mobile racks enhance storage utilization but pose order picking challenges.
- Efficient order picking is crucial for minimizing time costs in warehouses.
Purpose of the Study:
- To optimize order picking processes in dense mobile rack systems.
- To minimize picking time by developing an efficient mathematical model and algorithm.
Main Methods:
- Orders are batched using hierarchical clustering based on channel location.
- A mathematical model is developed for virtual order clusters to optimize picking and rack movement.
- A hybrid genetic algorithm is designed to solve the optimization problem.
Main Results:
- The study establishes a model for optimizing order picking in dense mobile rack warehouses.
- The hybrid genetic algorithm effectively addresses the order picking optimization problem.
- Analysis of algorithm characteristics provides a reference for similar warehouse optimization challenges.
Conclusions:
- Optimized order picking in dense mobile racks leads to significant time cost reduction.
- The proposed methodology offers a valuable framework for improving warehouse operational efficiency.
- This research provides practical insights for solving complex order picking optimization problems.
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