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PickupSimulo–Prototype of Intelligent Software to Support Warehouse Managers Decisions for Product Allocation Problem.

Authors :
Lorenc, Augustyn
Lerher, Tone
Source :
Applied Sciences (2076-3417); 12/1/2020, Vol. 10 Issue 23, p8683, 29p
Publication Year :
2020

Abstract

Featured Application: The results of the proposed research could be a useful tool in the selection of products classification while planning products allocation in a warehouse. Proposed model, as a developed software, can support the decision and decrease the risk of the inappropriate choice of product location planning. Based on the proposed research and the application of the proposed model Pickup Simulo, we can expect the reduction in the total warehouse costs to be between 10 and 16 per cent. The research results are, therefore, of significant importance for warehouse management. In this paper, a new model supporting decisions about product allocation in an order-picking shelf warehouse is presented. Industry 4.0 pays attention to inciting the processes, self-analysis and self-optimization of the short response time to market changes, and the maximum use of related data. Methods for solving the product allocation problem (PAP) are not enough to meet the requirements of Industry 4.0. The authors present a new approach for solving PAP. The novelty introduced in the model is based on correlated data—products parameters, clients' orders and warehouse layout. The proposed model contains elements of intelligence. The model, after product classification and allocation, analyzes its effectiveness by a simulation of the order-picking process. The application of artificial neural networks (ANN) as a part of the computing model enables the analysis of large data sets in a short time. The presented study has proved the proposed model, both for practical and scientific purposes. Relying on the research results, the total warehouse cost could be reduced by 10 to 16 per cent. With the use of the proposed model, it is possible to predict the effect of future actions before their execution. The model can be implemented in most conventional warehouses to raise the throughput performance of the order-picking process. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
10
Issue :
23
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
147542976
Full Text :
https://doi.org/10.3390/app10238683