INTELLIGENT APPROACHES FOR ENHANCING WAREHOUSE EFFICIENCY: ITEM PLACEMENT, ORDER PICKING, AND ROBOTIC AUTOMATION

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 o