Hybrid Genetic Algorithms for Order Assignment and Batching in Picking System: A Systematic Literature Review

The Order-Picking System (OPS) is vital for inbound logistics, ensuring efficient customer order fulfillment and minimizing costs. Efficient execution and implementation of OPS are critical to meeting customer demands and reducing dissatisfaction, necessitating a thorough examination of the process of order picking features. The order picking, a cornerstone of warehouse operations, involves meticulous selection and gathering of items from designated storage locations, unfolding through stages like order assignment and order batching. In order assignment, specific orders are methodically delegated to pickers or teams, considering factors like urgency, order size, item location, and picker availability. The overarching goal is to optimize resource utilization and simultaneously reduce the time needed for order fulfillment, ensuring a streamlined and efficient approach. Conversely, order batching strategically groups multiple orders for concurrent picking, aiming to minimize trips and enhance overall efficiency and productivity. Throughout the order-picking process, pickers utilize tools like pick lists, barcode scanners, and automated storage and retrieval systems (AS/RS) for precise item location and retrieval. Post-collection, items are transported to a dedicated packing area for meticulous preparations before shipping to the customer. Orchestrating the order-picking process requires careful planning, coordination, and execution for punctual and precise customer order fulfillment. This paper highlighted a systematic reviewing process which analyzed relevant research papers, with a primary focus on the problems of order assignment and batching—a key area within the order-picking process. The objective was to provide a comprehensive overview of hybrid Genetic Algorithm solutions for these challenges, achieved through a systematic review from 2018 to 2023 using Web of Science and Scopus databases. After screening, the relevant references were selected, focusing on terms like storage assignment problems. A thorough examination delved into various subcategories, encompassing recent approaches of genetic algorithms and openly accessible datasets. The resulting review offers a concise summary, highlighting key findings, challenges, and potential directions associated with hybrid genetic algorithms, specifically in relation to storage assignment, storage location assignment problems, and order batching issues.