INTELLIGENT APPROACHES FOR ENHANCING WAREHOUSE EFFICIENCY: ITEM PLACEMENT, ORDER PICKING, AND ROBOTIC AUTOMATION
DOI:
https://doi.org/10.28925/2663-4023.2025.29.869Keywords:
product placement optimization, storage location assignment, order picking, machine learning, time series clustering, robotic systems, logistics centersAbstract
The article presents a systematic review of contemporary scientific approaches to solving the Storage Location Assignment Problem (SLAP), managing the Order Picking Problem (OPP), and implementing Robotic Mobile Fulfillment Systems (RMFS) in warehouse logistics using machine learning methods. The study was based on a selection of publications from the Scopus database, compiled according to the PRISMA 2020 methodology, ensuring transparency and reproducibility of the analysis. As a result, 20 scientific publications were selected, each containing experimental and applied results related to logistics center automation. Task types, methods, and implementation technologies structure the literature analysis. It covers classical heuristic algorithms, intelligent systems, hybrid strategies, and machine learning-based approaches, including deep reinforcement learning, time series clustering, and associative analysis. Emphasis is placed on practical applications that aim to improve storage efficiency, reduce transportation costs, shorten order-picking times, and minimize physical strain on personnel. Particular attention is given to robotic systems that optimize movement routes and reduce overall operational time. The most promising directions include algorithms combining historical data analysis, product correlations, and preliminary demand classification. It was found that combined models integrating temporal features and associative relationships are effective in adaptive inventory placement and order batching. The results are systematized based on features such as task type, applied method, presence of machine learning, and implementation environment. The article also outlines future research directions, including adaptive clustering with seasonality considerations, predictive storage updates, Long Short-Term Memory (LSTM) model integration, and attention to energy-related aspects. As a result, the conducted research enables us to form a comprehensive understanding of the current state of scientific approaches in warehouse process optimization, providing the basis for the development of complex intelligent management systems that prioritize flexibility, efficiency, and the sustainable development of logistics centers.
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