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Advances in Intelligent Logistics System and Supply Chain Management
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Advances in Intelligent Logistics System and Supply Chain Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 7007

Special Issue Editors

Dr. Dimitrios Aidonis

E-Mail Website
Guest Editor
Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece
Interests: reverse logistics; supply chain management; optimization techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Charisios Achillas

E-Mail Website
Guest Editor
Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece
Interests: data analytics; optimization techniques; circular economy
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis Kostavelis

E-Mail Website
Guest Editor
Department of Supply Chain Management, International Hellenic University, 60100 Katerini, Greece
Interests: artificial intelligence; robotics; automation; emerging technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue aims to explore and showcase the latest developments, innovations, and advancements in the fields of intelligent logistics systems and supply chain management. Topics of interest include, but are not limited to, artificial intelligence applications, robotics, data analytics, automation, optimization techniques, and emerging technologies that contribute to enhancing the efficiency, resilience, and sustainability of logistics and supply chain operations. The scope of this Special Issue encompasses research articles, case studies, and reviews that provide valuable insights into the integration of intelligent systems to address challenges and foster improvements in logistics and supply chain management. 

Dr. Dimitrios Aidonis
Dr. Charisios Achillas
Dr. Ioannis Kostavelis
Guest Editors

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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • artificial intelligence applications
  • robotics
  • logistics
  • supply chain management

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Published Papers (6 papers)

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Research

18 pages, 1749 KiB  
Article
Resilient Responses to Global Supply Chain Disruptions: Focusing on the Stock Price of Global Logistics Companies
by Min-Seop Sim, Jeong-Min Lee, Yul-Seong Kim and Chang-Hee Lee
Appl. Sci. 2024, 14(23), 11256; https://doi.org/10.3390/app142311256 - 3 Dec 2024
Viewed by 548
Abstract
This study clarifies the impact of global supply chain risks on global logistics companies, with a focus on the potential implications for sustainable supply chain management. The study employs the vector auto-regression model to examine the relationship between the Global Supply Chain Pressure [...] Read more.
This study clarifies the impact of global supply chain risks on global logistics companies, with a focus on the potential implications for sustainable supply chain management. The study employs the vector auto-regression model to examine the relationship between the Global Supply Chain Pressure Index (GSCPI) and the stock prices of global logistics companies, yielding the following results. First, the GSCPI does not have a statistically significant effect on most global logistics firms, except for shipping companies, which tend to be negatively impacted by supply chain disruptions. The t-statistics of the GSCPI on air cargo, integrated logistics, and pipeline companies were below the threshold of 1.291, corresponding to a 90% confidence level, which indicates that these results were not statistically significant. Therefore, logistics companies should prioritize the development of resilient and sustainable supply chain strategies incorporating alternative energy sources, such as liquefied hydrogen, ammonia, green methanol, and liquefied natural gas, to enhance their ability to respond to unexpected situations. Second, contrary to other logistics sectors, shipping enterprises have been positively impacted by the GSCPI, suggesting that they may find new opportunities during periods of global instability. By adopting eco-friendly fuel alternatives and green technologies, shipping companies can capitalize on these opportunities and contribute to the global transition toward sustainable logistics practices. These findings suggest that global logistics companies, including pipeline, air cargo, and integrated logistics companies, should develop resilient global supply chain management strategies that incorporate supply chain platforms, nearshoring, and import diversification. This study offers important implications for entrepreneurs and policymakers, emphasizing the role of sustainable energy solutions in stabilizing global supply chains. Full article
(This article belongs to the Special Issue Advances in Intelligent Logistics System and Supply Chain Management)
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18 pages, 11002 KiB  
Article
Simulation and Optimization of an Intelligent Transport System Based on Freely Moving Automated Guided Vehicles
by Ladislav Rigó, Jana Fabianová, Ján Palinský and Iveta Dočkalíková
Appl. Sci. 2024, 14(17), 7937; https://doi.org/10.3390/app14177937 - 5 Sep 2024
Viewed by 825
Abstract
AGV-based intra-company transport systems are indispensable in the manufacturing industry of Industry 4.0. Designing the systems involves determining AGV movement paths that are predefined dynamically or adjusted based on real-time events. This study focuses on the simulation and optimization of an intelligent transport [...] Read more.
AGV-based intra-company transport systems are indispensable in the manufacturing industry of Industry 4.0. Designing the systems involves determining AGV movement paths that are predefined dynamically or adjusted based on real-time events. This study focuses on the simulation and optimization of an intelligent transport system. The aim is to create a system model with freely moving AGVs controlled based on the requirements of production facilities. The simulation model was designed in the Tecnomatix Plant Simulation environment. A fictional case study with a flexible manufacturing system was used. Specific methods have been developed for AGV operation, control, and dynamic product handling. The initial simulation model served as the basis for optimization. Model optimization, performed using a genetic algorithm, aimed to maximize production volume while minimizing the number of AGVs. Simulation results showed that AGV movements were dynamically adjusted based on real-time machine requests, and the optimal configuration of AGVs achieved a production volume that was significantly higher than the initial setup. This study demonstrates a new approach to modeling AGV traffic systems emphasizing real-time dynamic adjustments of AGV paths. The findings contribute to integrating intelligent transport systems into production processes, and this study provides valuable insights for future investigation in this area. Full article
(This article belongs to the Special Issue Advances in Intelligent Logistics System and Supply Chain Management)
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26 pages, 9902 KiB  
Article
Digital Maturity of Logistics Processes Assessed in the Areas of Technological Support for Performance Measurement, Employees, and Process Management
by Agnieszka A. Tubis, Adam Koliński and Honorata Poturaj
Appl. Sci. 2024, 14(17), 7893; https://doi.org/10.3390/app14177893 - 5 Sep 2024
Viewed by 1038
Abstract
(1) Background: Industry 4.0 and the COVID-19 pandemic have resulted in an acceleration of digital transformation, primarily in production systems and logistics. This raises the need to assess where a company is in its digital transformation today and what measures must be taken [...] Read more.
(1) Background: Industry 4.0 and the COVID-19 pandemic have resulted in an acceleration of digital transformation, primarily in production systems and logistics. This raises the need to assess where a company is in its digital transformation today and what measures must be taken to improve logistics processes. This article aims to present the results of a study assessing the digital maturity of logistics processes in a group of selected enterprises located in Poland. The research was conducted among companies that are business partners of the Poznan School of Logistics. (2) Methods: The DMM-OP digital process maturity assessment model was used in the study. Digital maturity was assessed on a five-point scale in four areas of company activity: process management, performance measurement, employee support, and technology. The research procedure included four stages. (3) Results: The results indicate that companies in the process management and performance measurement dimensions achieved the highest level of digital maturity. In commercial enterprises, the level of digital transformation is at the lowest level. Large enterprises achieved the best results, but there were also very good results in the group of small enterprises. (4) Conclusions: The results presented in the article can be used by industry and academia. The research was not statistical but can form the basis for benchmarking analyses. Full article
(This article belongs to the Special Issue Advances in Intelligent Logistics System and Supply Chain Management)
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23 pages, 3158 KiB  
Article
A Machine Learning Predictive Model for Ship Fuel Consumption
by Rhuan Fracalossi Melo, Nelio Moura de Figueiredo, Maisa Sales Gama Tobias and Paulo Afonso
Appl. Sci. 2024, 14(17), 7534; https://doi.org/10.3390/app14177534 - 26 Aug 2024
Viewed by 1617
Abstract
Water navigation is crucial for the movement of people and goods in many locations, including the Amazon region. It is essential for the flow of inputs and outputs, and for certain Amazon cities, boat access is the only option. Fuel consumption accounts for [...] Read more.
Water navigation is crucial for the movement of people and goods in many locations, including the Amazon region. It is essential for the flow of inputs and outputs, and for certain Amazon cities, boat access is the only option. Fuel consumption accounts for over 25% of a vessel’s total operational costs. Shipping companies are therefore seeking procedures and technologies to reduce energy consumption. This research aimed to develop a fuel consumption prediction model for vessels operating in the Amazon region. Machine learning techniques such as Decision Tree, Random Forest, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and CatBoost can be used for this purpose. The input variables were based on the main design characteristics of the vessels, such as length and draft. Through metrics like mean, median, and coefficient of determination (R2), six different algorithms were assessed. CatBoost was identified as the model with the best performance and suitability for the data. Indeed, it achieved an R2 value higher than 91% in predicting and optimizing fuel consumption for vessels operating in the Amazon and similar regions. Full article
(This article belongs to the Special Issue Advances in Intelligent Logistics System and Supply Chain Management)
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31 pages, 1193 KiB  
Article
Optimizing Supply Chain Efficiency Using Innovative Goal Programming and Advanced Metaheuristic Techniques
by Kaoutar Douaioui, Othmane Benmoussa and Mustapha Ahlaqqach
Appl. Sci. 2024, 14(16), 7151; https://doi.org/10.3390/app14167151 - 14 Aug 2024
Viewed by 1288
Abstract
This paper presents an optimization approach for supply chain management that incorporates goal programming (GP), dependent chance constraints (DCC), and the hunger games search algorithm (HGSA). The model acknowledges uncertainty by embedding uncertain parameters that promote resilience and efficiency. It focuses on minimizing [...] Read more.
This paper presents an optimization approach for supply chain management that incorporates goal programming (GP), dependent chance constraints (DCC), and the hunger games search algorithm (HGSA). The model acknowledges uncertainty by embedding uncertain parameters that promote resilience and efficiency. It focuses on minimizing costs while maximizing on-time deliveries and optimizing key decision variables such as production setups, quantities, inventory levels, and backorders. Extensive simulations and numerical results confirm the model’s effectiveness in providing robust solutions to dynamically changing supply chain problems when compared to conventional models. However, the integrated model introduces substantial computational complexity, which may pose challenges in large-scale real-world applications. Additionally, the model’s reliance on precise probabilistic and fuzzy parameters may limit its applicability in environments with insufficient or imprecise data. Despite these limitations, the proposed approach has the potential to significantly enhance supply chain resilience and efficiency, offering valuable insights for both academia and industry. Full article
(This article belongs to the Special Issue Advances in Intelligent Logistics System and Supply Chain Management)
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37 pages, 2200 KiB  
Article
Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
by Eleni Aretoulaki, Stavros T. Ponis and George Plakas
Appl. Sci. 2024, 14(15), 6464; https://doi.org/10.3390/app14156464 - 24 Jul 2024
Viewed by 929
Abstract
The pursuit of ameliorating humanitarian logistics (HL) through the integration of cutting-edge technologies has received significant attention in recent years. AIRDROP is a visionary platform conceived to offer a cohesive disaster management approach spanning from preparedness to recovery of a wide range of [...] Read more.
The pursuit of ameliorating humanitarian logistics (HL) through the integration of cutting-edge technologies has received significant attention in recent years. AIRDROP is a visionary platform conceived to offer a cohesive disaster management approach spanning from preparedness to recovery of a wide range of natural and human-made disasters. AIRDROP aims to be a scalable, modular and flexible solution, employing an array of drones of different sizes and payload capabilities, able to provide different HL services to first responders and operational decision-makers. This study aims to elicit, specify and validate the requirements for AIRDROP to ensure their applicability across a broad spectrum of disaster scenarios and the entire disaster management continuum. This research utilized a thorough literature review and expert consultations to systematically elicit and specify the AIRDROP requirements, ensuring they were grounded in both academic foundations and practical industry standards. The validation process involved a questionnaire survey administered to 26 participants from various professional backgrounds. The requirements were prioritized using the MoSCoW methodology, and significant differences among participant groups were identified through the Kruskal–Wallis H and Mann–Whitney U tests. Furthermore, two critical requirements emerged from open-ended responses. As a result, 276 out of the initially defined 335 requirements in total advanced to the design phase. It is worth noting that the dynamic nature of requirements in HL necessitates ongoing assessment and adaptation to keep AIRDROP at the forefront and aligned with evolving needs. Full article
(This article belongs to the Special Issue Advances in Intelligent Logistics System and Supply Chain Management)
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