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Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability
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Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Artificial Intelligence and Digital Systems Engineering".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 9195

Special Issue Editor

Dr. Roman Gumzej

E-Mail Website
Guest Editor
Faculty of Logistics, Univerza v Mariboru, Maribor, Slovenia
Interests: logistics information systems; cyber–physical systems; autonomous systems; safety and security

Special Issue Information

Dear Colleagues,

Intelligent Logistics Systems (ILS) represent a transformative paradigm in the field of supply chain management, leveraging cutting-edge technologies to optimize and automate various aspects of the logistics process. The integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and advanced analytics has empowered Intelligent Logistics Systems to dynamically respond to real-time challenges and complexities in the logistics domain. These systems are designed to streamline operations, reduce costs, minimize delays, and improve overall supply chain resilience.

Key applications include but are not limited to:

  1. Predictive Analytics for Demand Forecasting: ILS leverage advanced predictive analytics algorithms to analyze historical data, market trends, and external factors, enabling accurate demand forecasting. This results in optimized inventory management and improved resource allocation.
  2. Autonomous Vehicles and Drones: The deployment of autonomous vehicles and drones in logistics operations enhances transportation efficiency, reduces human error, and facilitates last-mile delivery. These technologies contribute to quicker and more cost-effective distribution networks.
  3. Smart Warehousing and Robotics: ILS utilize smart warehousing solutions, incorporating robotics and automation, to optimize inventory storage, retrieval, and packing processes. This ensures faster order fulfillment, reduces errors, and minimizes operational costs.
  4. Real-Time Tracking and Visibility: IoT-enabled sensors and RFID technology provide real-time tracking and visibility throughout the supply chain. This transparency enables better decision-making, improves accountability, and enhances overall supply chain responsiveness.
  5. Blockchain Technology: The implementation of the Blockchain ensures secure and transparent transactions across the supply chain. It enhances trust among stakeholders, reduces fraud, and improves traceability of products from manufacturing to delivery.
  6. Cognitive Computing: Cognitive computing algorithms analyze various factors such as production and storage locations, traffic conditions, weather, and historical data to optimize goods distribution. This minimizes transportation costs, reduces fuel consumption, and enhances overall logistical efficiency.

In conclusion, Intelligent Logistics Systems represent a significant leap forward in the quest for a more agile, responsive, and cost-effective supply chain. By leveraging the power of emerging technologies, these applications redefine traditional logistics models and pave the way for a future where adaptability and efficiency are paramount in navigating the complexities of the global marketplace.

This Special Issue provides an overview of the diverse applications of Intelligent Logistics Systems and their impact on enhancing efficiency and adaptability in the rapidly evolving global supply chain landscape.

Dr. Roman Gumzej
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • real-time computing
  • artificial intelligence
  • big data
  • blockchain
  • IoT
  • predictive analytics
  • smart business models
  • quality of service

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Published Papers (5 papers)

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Research

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25 pages, 1712 KiB  
Article
Improving the Information Systems of a Warehouse as a Critical Component of Logistics: The Case of Lithuanian Logistics Companies
by Kristina Vaičiūtė and Aušra Katinienė
Systems 2025, 13(3), 186; https://doi.org/10.3390/systems13030186 - 7 Mar 2025
Viewed by 576
Abstract
Rapid changes in the modern world and technological advances and processes are increasingly contributing to greater attention being given to emerging problems associated with obtaining big data, as well as modifying decision-making processes in diverse spheres. Special attention in logistics companies should be [...] Read more.
Rapid changes in the modern world and technological advances and processes are increasingly contributing to greater attention being given to emerging problems associated with obtaining big data, as well as modifying decision-making processes in diverse spheres. Special attention in logistics companies should be given to the warehouse as a critical component of logistics, in particular to such processes as big data processing and automation, as well as the improvement, development, and support of information systems. Enhancing logistics information systems provides companies with a competitive advantage, reduces the emergence of human error, accelerates processes, and ensures the collection and sharing of information and big data are used in a sustainable manner. The automation of warehouse processes results in better-established operational safety and overall service quality. The present paper reviews the importance of improving warehouse automation and logistics information systems. Its advantages are highlighted, and the results of the conducted research are provided to expose the problem areas of warehouse automation and encourage improvements in information systems in Lithuanian logistics companies wherein there is a need to transfer a large amount of information and increase service quality. Full article
(This article belongs to the Special Issue Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability)
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18 pages, 2296 KiB  
Article
Advancing Dynamic Emergency Route Optimization with a Composite Network Deep Reinforcement Learning Model
by Jin Zhang, Hao Xu, Ding Liu and Qi Yu
Systems 2025, 13(2), 127; https://doi.org/10.3390/systems13020127 - 17 Feb 2025
Viewed by 821
Abstract
Emergency logistics is essential for rapid and efficient disaster response, ensuring the timely availability and deployment of resources to affected areas. In the process of rescue work, the dynamic changes in rescue point information greatly increase the difficulty of rescue. This paper establishes [...] Read more.
Emergency logistics is essential for rapid and efficient disaster response, ensuring the timely availability and deployment of resources to affected areas. In the process of rescue work, the dynamic changes in rescue point information greatly increase the difficulty of rescue. This paper establishes a combined neural network model considering soft time-window penalty and applies deep reinforcement learning (DRL) to address the dynamic routing problem in emergency logistics. This method utilizes the actor–critic framework, combined with attention mechanisms, pointer networks, and long short-term memory neural networks, to determine effective disaster relief path, and it compares the obtained scheduling scheme with the results obtained from the DRL algorithm based on the single-network model and ant colony optimization (ACO) algorithm. Simulation experiments show that the proposed method reduces the solution accuracy by nearly 10% compared to the ACO algorithm, but it saves nearly 80% in solution time. Additionally, it slightly increases solution times but improves accuracy by nearly 20% over traditional DRL approaches, demonstrating a promising balance between performance efficiency and computational resource utilization in emergency logistics. Full article
(This article belongs to the Special Issue Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability)
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18 pages, 3453 KiB  
Article
Development of a Blockchain-Based Food Safety System for Shared Kitchens
by Hyejin Jang, Daye Lee and Byungun Yoon
Systems 2024, 12(11), 509; https://doi.org/10.3390/systems12110509 - 20 Nov 2024
Cited by 1 | Viewed by 1467
Abstract
With the recent growth of the sharing economy, businesses offering shared-kitchen services are expanding rapidly. Due to the communal nature of these kitchens, there is a heightened need for systematic food safety management. However, existing research on blockchain applications has largely overlooked shared [...] Read more.
With the recent growth of the sharing economy, businesses offering shared-kitchen services are expanding rapidly. Due to the communal nature of these kitchens, there is a heightened need for systematic food safety management. However, existing research on blockchain applications has largely overlooked shared kitchens, a complex setting with numerous stakeholders and sensitivity to real-time kitchen conditions. This study addresses this gap by proposing a blockchain-based food safety management system for shared kitchens. The system’s functional requirements were meticulously outlined based on guidelines from South Korea’s Ministry of Food and Drug Safety. Key participants were identified as system users, and use cases were crafted in alignment with their responsibilities and roles to ensure effective safety management. Additionally, the blockchain system’s mechanisms for enhancing safety in shared kitchens were substantiated through specific use cases and detailed data structures, addressing issues related to forgery, alteration, and management challenges. This study also offers practical insights that can facilitate more structured safety management in shared-kitchen environments. Full article
(This article belongs to the Special Issue Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability)
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15 pages, 487 KiB  
Article
Deep Learning-Based Freight Recommendation System for Freight Brokerage Platform
by Yeon-Soo Kim and Tai-Woo Chang
Systems 2024, 12(11), 477; https://doi.org/10.3390/systems12110477 - 7 Nov 2024
Cited by 1 | Viewed by 1160
Abstract
Platform-based businesses in the logistics market are evolving under the influence of digital transformation. Transforming the freight market into an environment where various types of freight can be traded across multiple markets and locations. Freight brokerage platforms have revolutionized the trading relationship between [...] Read more.
Platform-based businesses in the logistics market are evolving under the influence of digital transformation. Transforming the freight market into an environment where various types of freight can be traded across multiple markets and locations. Freight brokerage platforms have revolutionized the trading relationship between freight owners and vehicle owners. However, this type of system has also introduced inefficiencies, such as unestablished contracts, leading to unnecessary costs and delays. To address this issue, a freight recommendation system can assist users in finding what they are looking for while aiming to reduce failed contracts. With current advances in deep learning, complex patterns based on users’ past behaviors and preferences can be learned, enabling more accurate and personalized recommendations. This study proposes a deep learning-based freight recommendation system to provide personalized services and reduce failed contracts on freight brokerage platforms. The system is built by creating a freight transaction dataset, classifying freight categories through natural language processing and text mining techniques, and incorporating externally derived data on transportation distances. The deep learning model is trained using Autoencoder, Word2Vec, and Graph Neural Networks (GNN), with recommendation logic implemented to suggest suitable freight matches for vehicle owners. This system is expected to increase the market efficiency of the freight logistics industry and is a key step toward improving the long-term profit structure. Full article
(This article belongs to the Special Issue Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability)
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Review

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23 pages, 2005 KiB  
Review
Exploring the Role of Autonomous Trucks in Addressing Challenges within the Trucking Industry: A Comprehensive Review
by Ali Hasiri and Amirhassan Kermanshah
Systems 2024, 12(9), 320; https://doi.org/10.3390/systems12090320 - 23 Aug 2024
Cited by 1 | Viewed by 3982
Abstract
The trucking industry, a vital part of the economic structure, faces numerous challenges such as greenhouse gas emissions, labor-related issues, fluctuating fuel costs, and safety concerns. These challenges intensify as the industry expands to meet growing demand. The advent of artificial intelligence has [...] Read more.
The trucking industry, a vital part of the economic structure, faces numerous challenges such as greenhouse gas emissions, labor-related issues, fluctuating fuel costs, and safety concerns. These challenges intensify as the industry expands to meet growing demand. The advent of artificial intelligence has led to the development of autonomous trucks, which are seen as a promising solution to these ongoing issues. This study is the first comprehensive review of literature on autonomous trucks, organized by theme and research method. Studies are initially categorized based on the timeline of the issues investigated, divided into two main subcategories: foundational aspects of autonomous truck implementation and practical implementation and economic analysis of autonomous trucks. Research on the foundational aspects of autonomous trucks is further divided into four categories: (1) Acceptance surveys, (2) Identification of barriers, (3) Core technologies for autonomous trucks implementation, and (4) Predictions of adoption rates. Research on practical and economical aspects of autonomous trucks falls into three subcategories: (1) Infrastructure, (2) Systemic performance optimization, and (3) Cost estimation. To enhance the accuracy of this review, a more detailed classification was conducted on two specific subcategories: core technologies for autonomous truck implementation and systemic performance optimization. Additionally, the studies were also categorized based on their research methods and assumptions, which include accurate descriptions of autonomous technology, data collection methods, assumptions about the study environment, the fuel type of autonomous trucks, and approach to analysis: simultaneous or separate. This comprehensive review of the literature offers a roadmap for researchers, aiding them in identifying unique and novel research topics, thereby propelling the advancement of autonomous trucks as a viable solution to numerous challenges in the trucking industry. Full article
(This article belongs to the Special Issue Intelligent Logistics Systems Applications: Enhancing Efficiency and Adaptability)
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