DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-1 ( Jan, 2023 ) | |||||||||
Paper Title |
Application of Artificial Intelligence in Warehouse Picking Optimization - A Systematic Literature Review | |||||||||
Author Name |
Ayoub Bensoltana, Rajaa Benabbou, Jamal Benhra | |||||||||
Affilition |
1,2,3 LARILE LABORATORY OSIL Team ENSEM, Hassan II University of Casablanca, Casablanca, Morocco | |||||||||
Pages |
85-93 | |||||||||
Abstract |
Abstract - Context: Excellent logistic performance can open up new markets while customers expect speed, quality, and minimize costs. Warehouses and material handling systems are the core elements within the goods flow and build the connection between producer and costumers. They are an essential component of any supply chain. Technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), enable more efficient functions into logistics operations Objective: The aim of this study is to analyze the previous research on the warehouse order picking process. The analysis is carried out with respect to different perspectives such as the area of application, type of application, AI method applied. Method: In this paper, we conduct a comprehensive systematic literature review on the various applications of artificial intelligence (AI) approaches in warehouse optimization, focusing especially on the Order Picking. The methodology adopted was applied to numerous studies from the Scopus database. In total, we selected 63 studies relevant to our topic. Results: The results indicate that the number of articles in this domain is increasing every year, the most used methods are those of metaheuristics, Conclusions: Even though there is a growing interest in applying AI techniques in the selection process, the number of techniques covered so far by current research is quite limited in neural networks. Keywords - Artificial Intelligence, Warehouse Picking Process, Warehouse optimization, Neural networks, Order Picking, Systematic literature review. | |||||||||
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