Search Results (259)

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

Modern agricultural intelligent manufacturing faces critical challenges including low automation levels, safety hazards in high-temperature processing, and insufficient production data integration. Digital twin technology and 3D printing offer promising solutions through real-time virtual–physical synchronization and customized equipment manufacturing, respectively. However, existing research exhibits significant limitations: inadequate real-time synchronization mechanisms causing delayed response, poor environmental adaptability in unstructured agricultural settings, and limited human–machine collaboration capabilities. To address these deficiencies, this study develops a digital twin-driven intelligent sorting system for 3D-printed agricultural tools, integrating an Articulated Robot Arm, 16 industrial-grade 3D printers, and the Unity3D 2024.x platform to establish a complete “printing–sorting–warehousing” digitalized production loop. Unlike existing approaches, our system achieves millisecond-level bidirectional physical–virtual synchronization, implements an adaptive grasping algorithm combining force control and thermal sensing for safe high-temperature handling, employs improved RRT-Connect path planning with ellipsoidal constraint sampling, and features AR/VR/MR-based multimodal interaction. Validation testing in real agricultural production environments demonstrates a 98.7% grasping success rate, a 99% reduction in burn accidents, and a 191% sorting efficiency improvement compared to traditional methods, providing breakthrough solutions for sustainable agricultural development and smart farming ecosystem construction. Full article

This study explores the innovative application of Artificial Intelligence (AI) in transforming data engineering practices, with a specific focus on optimizing data modeling and data warehouse automation for Business Intelligence (BI) systems. The proposed framework automates the creation of Data Vault models directly from raw source tables by leveraging the advanced capabilities of Large Language Models (LLMs). The approach involves multiple iterations and uses a set of LLMs from various providers to improve accuracy and adaptability. These models identify relevant entities, relationships, and historical attributes by analyzing the metadata, schema structures, and contextual relationships embedded within the source data. To ensure the generated models are valid and reliable, the study introduces a rigorous validation methodology that combines syntactic, structural, and semantic evaluations into a single comprehensive validity coefficient. This metric provides a quantifiable measure of model quality, facilitating both automated evaluation and human understanding. Through iterative refinement and multi-model experimentation, the system significantly reduces manual modeling efforts, enhances consistency, and accelerates the data warehouse development lifecycle. This exploration serves as a foundational step toward understanding the broader implications of AI-driven automation in advancing the state of modern Big Data warehousing and analytics. Full article

This paper aims to explore the role artificial intelligence (AI) technologies play in optimizing energy consumption levels in urban logistical systems, including the strategic implications of such technologies on smart supply chain management (SCM) in Dubai. The mixed-methods study was adopted and applied, in which quantitative measures of the performance of 16 public–private organizations were merged with qualitative evidence provided through semi-structured interviews and document analysis. AI solutions that were assessed in the research included the use of predictive routing, dynamic fleet scheduling, IoT-base monitoring, and smart warehousing. Results indicate an overall decrease of 13.9% in fuel consumption, 17.3% in energy and 259.4 kg in monthly CO₂ emissions by the organization on average by adopting AI. These findings were proven by the simulation model, which estimated that the delivery efficiency would increase within an AI-driven scenario and be scalable in the future. Other important impediments were also outlined in the study, such as constraint of legacy systems, skills gap, and interoperability of data. Implications point to the necessity of the incorporation of digital governance, data protocol standardization, and AI-compatible city planning to improve the urban SCM of Dubai, through the terms of sustainability and resilience. In this study, a transferable structure is provided that can be utilized by cities that are interested in matching AI innovation and energy and logistics goals, in terms of policy objectives. Full article

This study’s purpose was to analyze how the configuration of a sustainable distribution network affects the effectiveness of logistics coordination mechanisms, specifically focusing on the role of 3PL (third-party logistics) providers. We examined 69 networks that used a 3PL provider. The study used a weighted regression approach, with coordination mechanisms scaled by importance using the Analytic Hierarchy Process (AHP). To enhance interpretability, the SHAP model from Explainable AI (XAI) was used to identify the most influential configuration factors, which included service recipient type, product characteristics, warehousing susceptibility, and assortment diversity. The findings indicate that while increasing network complexity enhances adaptability, it may simultaneously reduce the efficiency of certain coordination mechanisms. The study highlights warehousing susceptibility as a critical factor, with other variables having a weaker or statistically insignificant effect. The SHAP analysis provided additional practical insights beyond standard statistical thresholds. By integrating expert-based weighting (AHP) with XAI, we propose a hybrid analytical framework that helps 3PL operators select the most effective coordination tools, such as flow forecasting, for specific network and product types. Full article

Robotic manipulators in warehousing and logistics often face complex tasks that involve multiple steps, frequent task switching, and long-term dependencies. Inspired by the hierarchical structure of human motor control, this paper proposes a Hierarchical Reinforcement Learning (HRL) framework utilizing a multi-task goal-conditioned Decision Transformer (MTGC-DT). The high-level policy treats the Markov decision process as a sequence modeling task, allowing the agent to manage temporal dependencies. The low-level policy is made up of parameterized action primitives that handle physical execution. This design improves long-term reasoning and generalization. This method is evaluated on two common logistics manipulation tasks: sequential stacking and spatial sorting with sparse reward and low-quality dataset. The main contributions include introducing a HRL framework that integrates Decision Transformer (DT) with task and goal embeddings, along with a path-efficiency loss (PEL) correction and designing a parameterized, learnable primitive skill library for low-level control to enhance generalization and reusability. Experimental results demonstrate that the proposed Decision Transformer-based Hierarchical Reinforcement Learning (DT-HRL) achieves over a 10% higher success rate and over 8% average reward compared with the baseline, and a normalized score increase of over 2% in the ablation experiments. Full article

The order picking process in Goods-to-Person (G2P) systems involves a set of interdependent yet often separately addressed decisions, such as order allocation, sequencing, and rack handling. This study focuses on the joint optimization of order allocation, order sequencing, rack selection, and rack sequencing in a G2P robotic mobile fulfillment system with multiple picking stations. To model this complex problem, we develop a mathematical formulation and propose a two-phase heuristic algorithm that combines simulated annealing, genetic algorithms, and beam search for efficient solution. In addition, we explore and compare two order allocation strategies—order similarity and order association—across a range of operational scenarios. Extensive computational experiments and sensitivity analyses demonstrate the effectiveness of the proposed approach and provide insights into how strategic order allocation can significantly improve picking efficiency. Computational experiments on small-scale instances show that our algorithm achieves near-optimal solutions with up to 93.3% reduction in computation time compared to exact optimization for small cases. In large-scale scenarios, the order similarity strategy reduces rack movements by up to 44.8% and the order association strategy by up to 33.5% relative to a first-come, first-served baseline. Sensitivity analysis reveals that the association strategy performs best with fewer picking stations and lower rack capacity, whereas the similarity strategy is superior in systems with more stations or higher rack capacity. The findings offer practical guidance for the design and operation of intelligent warehousing systems. Full article

As consumer awareness grows and regulations regarding the quality and safety of perishable goods become stricter, careful management of environmental conditions throughout the supply chain is becoming essential. Among these factors, storage temperature plays a crucial role in preserving the physicochemical characteristics of products. Therefore, an effective approach to ensure quality and safety up to the final customer is to continuously monitor the temperature within warehouses, using specific location-mapping techniques and stocking optimization methods. This study proposes a dynamic optimization model for the storage location assignment problem, integrating both temperature and humidity constraints into the placement of stock-keeping units. The model operates under a multi-period, multi-product framework and leverages real-time sensor data to account for spatial temperature stratification and environmental variability within the warehouse, contributing to the reduction in the energy consumption. Two alternative optimization strategies are explored: one focused on minimizing thermal and humidity stress, and another targeting the reduction in average storage cycle time. A detailed what-if analysis is conducted across three scenarios, varying warehouse fill rates and incoming load volumes, in order to prove the effectiveness of the proposed model in a real-data context. The results show that the approach minimizing environmental stress consistently outperforms traditional methods in quality-related metrics, maintaining superior objective function values. Full article

Multi-criteria decision analysis (MCDA) in furniture design is challenged by increasing product complexity and component proliferation. This study introduces a novel framework that integrates entropy reduction—achieved through dimensional standardization and modularity—as a core factor in the MCDA methodologies. The framework addresses both individual furniture evaluation and product family optimization through systematic complexity reduction. The research employed a two-phase methodology. First, a comparative analysis evaluated two furniture variants (laminated particleboard versus oak wood) using the Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The divergent rankings produced by these methods revealed inherent evaluation ambiguities stemming from their distinct mathematical foundations, highlighting the need for additional decision criteria. Building on these findings, the study further examined ten furniture variants, identifying the potential to transform their individual components into universal components, applicable across various furniture variants (or configurations) in a furniture line. The proposed dimensional modifications enhance modularity and interoperability within product lines, simplifying design processes, production, warehousing logistics, product servicing, and liquidation at end of lifetime. The integration of entropy reduction as a quantifiable criterion within MCDA represents a significant methodological advancement. By prioritizing dimensional standardization and modularity, the framework reduces component variety while maintaining design flexibility. This approach offers furniture manufacturers a systematic method for balancing product diversity with operational efficiency, addressing a critical gap in current design evaluation practices. Full article

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manufacturers must ensure timely availability of these parts while managing inventory efficiently. An excess of inventory can increase warehousing costs, while stock shortages can lead to supply delays. Accurate demand forecasting is essential to balance these factors, considering the changing demand characteristics over time, such as long-term trends, seasonal fluctuations, and irregular variations. This paper introduces a novel method for time series forecasting that employs Ensemble Empirical Mode Decomposition (EEMD) and Dynamic Mode Decomposition (DMD) to analyze service part demand. EEMD decomposes historical order data into multiple modes, and DMD is used to predict transitions within these modes. The proposed method demonstrated an approximately 30% reduction in forecasting error compared to comparative methods, showcasing its effectiveness in accurately predicting service parts demand across various patterns. Full article

Globalization has greatly changed the way logistics firms function, improving speed, accuracy, and efficiency in everything from logistic management to warehousing. Robotics and automation technologies driven by artificial intelligence improve warehouse operations’ efficiency and adaptability, allowing warehouses to easily manage a variety of items, packaging kinds, and order profiles. Nevertheless, more research is still needed to fully comprehend how automation has affected logistics and how it has evolved. In addition, to date, no scholarly work has provided a thorough analysis of particular automated logistic process automation strategies used by Lithuanian businesses. Although many of the assessments that are currently available in this field offer valuable insights, they are frequently overly broad. In order to tackle this problem, we conducted a methodical study that attempts to offer a strong and pertinent basis, focusing on the automation of logistics processes that are used in supply chain management together with artificial intelligence. This study’s objective was to examine conditions for increasing logistics automation processes in Lithuanian logistic companies. The novelty of this article is the consideration of the main factors influencing the automation of logistics processes, which include the key drivers of AI-powered warehouse automation processes to evaluate the real level of automation. Full article

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article