Smart Solution For Enhancing Storage Location Assignments In WMS Using Genetic Algorithm

Saleet, Hanan

doi:10.37624/ijert/13.11.2020.3456-3463

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…They mainly aimed at reducing costs by reducing movements (Viveros et al, 2021). Saleet (2020) says that SCM is more important than ever for WMS due to the COVID-19 pandemic. Therefore, Saleet (2020) used a generic solution to solve SLAP, which is a smart logistics solution using data analytics and genetic algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In this manner, this solution helps staff when they pick multiple products per order by reducing the distance of movement and time to bring back the products. Saleet (2020) aimed to optimise SLAP by using a genetic algorithm and we think the author was pretty much successful since there is only a 5.6% difference between the optimal solution and the solution which is proposed. Hirata et al (2019) worked on a case study that is related to an automobile factory to reduce total operational time.…”

Section: Resultsmentioning

confidence: 99%

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Current studies on storage location assignment problem

Şenyiğit

2022

GJBEM

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Although operations seem more important than everything in a business operation, warehouse management has great importance to the success of a business operation. Storage location assignment is very important because of the uncertainty in the number of incoming products, the necessity of answering unknown customers’ demands and the speed rate of response to customer demands. Besides, several factors affect warehouse management. Assigning products to appropriate locations is one of the most important and complex. This study aims to present recent academic works on the storage location assignment problem published from 2018 to 2021. These problems are assigned accordingly. Methods of solution, goals and some related considerations are considered while investigating the works. Recommendations are provided in the conclusion. Based on the reviewed articles, warehouse capacity, market, products, logistics resources and operational configuration are taken into account as restrictions or considerations. Keywords: Location, logistics, storage, warehouse management;

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Current studies on storage location assignment problem

Şenyiğit

2022

GJBEM

View full text Add to dashboard Cite

show abstract

“…Saleet [7] observed that the COVID-19 pandemic has posed increased challenges for warehouse management systems (WMS) within the lean supply chain. A crucial issue addressed by WMS is the Storage Location Assignment Problem (SLAP), involving the allocation of stock keeping units (SKUs) to appropriate storage locations.…”

Section: A Order Assignment-ga Relatedmentioning

confidence: 99%

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

Ou,

Ismail,

Sariff

2024

IEEE Access

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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.INDEX TERMS Order picking system, order assignment, order batching, hybrid genetic algorithms, systematic literature review

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“…warehouse efficiency. GA-based approaches are effective when dealing with large and complex storage facilities [21].…”

Section: Association Rules-based Abc Classificationmentioning

confidence: 99%

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

Kalkha,

Khiat,

Bahnasse

et al. 2024

IEEE Access

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In the rapidly evolving domain of e-commerce, effective warehouse management emerges as a critical factor for ensuring timely deliveries. This paper addresses the Storage Location Assignment Problem (SLAP) in e-commerce warehouses, a challenge intensified by varying product volumes and unpredictable demands. We introduce a novel Intelligent Storage Location Assignment (ISLA) method that utilizes advanced time series clustering algorithms specifically, Self-Organizing maps, dynamic time warping-Based k-means, and Agglomerative Hierarchical Clustering (AHC), to optimize order fulfillment and enhance warehouse efficiency. By clustering and positioning items with similar demand patterns, our approach minimizes order preparation time, reduces unnecessary warehouse movements, and improves operational flows. Our empirical evaluation, based on a real-world dataset from Kaggle, demonstrates the superiority of AHC in efficiently grouping high-turnover items, as evidenced by higher silhouette scores. Applying this method in simulations across various picking strategies such as s-shape, mid-point, discrete order picking, zone picking, and batch picking, we achieve significant efficiency improvements. Notably, our ISLA method results in up to 61% and 69% efficiency gains under s-shape and midpoint routing policies, respectively, outperforming traditional random and ABC storage assignments. These results not only highlight the significant potential of Artificial Intelligence (AI) in revolutionizing warehouse operations but also bridge the existing knowledge gap by showcasing a practical and impactful application of AI in SLAP. Our research advances the field of smart logistics, emphasizing the critical role of AI-driven intelligent storage location assignment in optimizing warehouse processes and enhancing the efficiency of the e-commerce supply chain.

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Smart Solution For Enhancing Storage Location Assignments In WMS Using Genetic Algorithm

Cited by 3 publications

References 14 publications

Current studies on storage location assignment problem

Current studies on storage location assignment problem

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

Enhancing Warehouse Efficiency With Time Series Clustering: A Hybrid Storage Location Assignment Strategy

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