Abstract
To improve the efficiency of warehouse operations, reasonable optimization of picking operations has become an important task of the modern supply chain. For the purpose of optimization of order picking in warehouses, a new fruit fly optimization algorithm, particle swarm optimization, random weight, and weight decrease model are used to solve the mathematical model. Further optimization is achieved through the analysis of the warehouse shelves and screening of the optimal solution of the picking time. In addition, simulation experiments are conducted in the MATLAB environment through programming. The shortest picking time is found out and chosen as an optimized method by taking advantage of the effectiveness of these six algorithms in the picking optimization and comparing the data obtained under the simulation. The result shows that the optimization capacity of RWFOA is better and the picking efficiency is the best.
Highlights
As a part of the logistics, the efficiency of the automated warehouse is largely dependent on the efficiency of order picking. erefore, the picking plays an important role in the automated warehouse for improving the efficiency of picking operation
E main structure of this paper is as follows: Section 1 introduces the motive and purpose of this study; Section 2 presents the literature review; Section 3 introduces research methods—original fly optimization algorithm (FOA), original particle swarm algorithm (PSA), random weight algorithm, weight decrease, and related literature; Section 4 introduces case description; Section 5 presents results and discussion; and Section 6 puts forward the research conclusions and suggestions
The FOA and Particle Swarm Optimization (PSO) must be modified in order to effectively manage the discrete variables associated with optimization issues. erefore, Random Weight (RW) and Weight Decrease (WD) were integrated into FOA and PSO to improve its advantage and to look for the better optimal order-picking time
Summary
As a part of the logistics, the efficiency of the automated warehouse is largely dependent on the efficiency of order picking. erefore, the picking plays an important role in the automated warehouse for improving the efficiency of picking operation. As a part of the logistics, the efficiency of the automated warehouse is largely dependent on the efficiency of order picking. Erefore, the picking plays an important role in the automated warehouse for improving the efficiency of picking operation. The automated stereoscopic warehouse provides more orderly and standardized management and the error rate is small, for small batch warehouse, frequent warehousing, and warehouses with various products, logistics storage becomes more stringent, and requirements for the efficiency of the logistics are higher, and the efficiency of picking needs to be improved. The ant colony algorithm [7], genetic algorithm [8] and multipopulation fruit fly optimization algorithm [9], which have been used to solve the picking operation problems, yielded good results. Based on the existing research, we will use the new fruit fly optimization algorithm and particle swarm optimization to solve the mathematical model. E main structure of this paper is as follows: Section 1 introduces the motive and purpose of this study; Section 2 presents the literature review; Section 3 introduces research methods—original FOA, original particle swarm algorithm (PSA), random weight algorithm, weight decrease, and related literature; Section 4 introduces case description; Section 5 presents results and discussion; and Section 6 puts forward the research conclusions and suggestions
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