Abstract
The Internet of Things (IoT) has become an important strategy in the current round of global economic growth and technological development and provides a new path for the intelligent development of the logistics industry. With the development of the economy, the demand for logistics benefits is becoming more important. The appropriate use of technologies related to IoT to improve logistics efficiency, such as cloud computing, mobile computing and data mining, has become a topic of considerable research interest. Picking operations are currently an extremely important and cumbersome aspect of logistics center tasks. To shorten the picking distance and improve work efficiency, this paper uses the genetic algorithm, ant colony algorithm and cuckoo algorithm to optimize the picking path in a fishbone-layout warehouse and establishes an optimized model of the warehouse picking path under the fishbone layout. Data-mining technology is used to simulate the model and obtain the simulation data under the condition of multiple orders. The results provide a theoretical basis for the study of the fishbone-layout picking path model and has certain practical significance for the efficient operation of logistics enterprises. Through optimization, it is conducive to the sustainable development of enterprises and to achieving long-term profitability.
Highlights
Warehouse layout plays an important role in the logistics activities of the whole warehouse [1,2,3,4,5,6]
Reference [17] first questioned the traditional layout of shelves, where the fishbone layout was proposed and, under certain assumptions, it was verified that the fishbone layout reduced costs by an average of 23.5% compared with the traditional layout
When the number of points to be picked increases, the optimization efficiency of the ant colony algorithm for the picking path is stable at 20–30% compared with that of the S-picking strategy
Summary
Warehouse layout plays an important role in the logistics activities of the whole warehouse [1,2,3,4,5,6]. Reference [37] introduced the 2-opt optimization operator based on the traditional cuckoo algorithm, proposed an adaptive discrete cuckoo algorithm (ADCS) and effectively solved the picking path optimization problem of TSP problems. The genetic algorithm, ant colony algorithm, and cuckoo algorithm were applied to solve the optimization problem of the picking path in this paper. The intelligent optimization algorithms of the genetic algorithm, ant colony algorithm and cuckoo algorithm are applied to the optimization problem of the fishbone-layout picking path, which may benefit fishbone layout research. The establishment of the hybrid picking path model of the fishbone layout provides a decision-making basis for the efficient management and optimization control of the warehouse system and provides new ideas and methods for supply chain management theory and the basic operations technology of logistics distribution
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