Logistics Applications Research Articles

Ant Colony Optimization (ACO) for Traveling Salesman Problem: A Review

The traveling salesman problem (TSP) is a fundamental combinatorial optimization problem with applications in resource management, logistics, and communications. In order to address TSP and its differences, this paper discusses developments in Ant Colony Optimization (ACO), a biologically inspired algorithm. Inspired by the foraging activity of ants, ACO's decentralized and recursive methodology has proven successful in solving difficult routing problems. ACO's scalability, convergence speed, and solution quality have been greatly enhanced over time through innovations including hybridization with algorithms such as Firefly, genetic algorithms, parallel computing frameworks, and adaptation mechanisms. These developments have given the ACO the flexibility and efficiency to handle dynamic situations, such as real-time vehicle guidance and underwater navigation. Despite its progress, issues remain such as scalability in resource-limited contexts, processing overhead, and reliance on parameter modification. This work summarizes current developments in ACO, noting how revolutionary the TSP solution is, pointing out its drawbacks, and suggesting areas for further study. Leveraging emerging technologies like machine learning and quantum computing, ACO has huge potential to progressively address challenging real-world problems. This review provides a comprehensive framework for developing uses of ACOs and reaffirms their status as a key component of improvement research.

Innovators and transformers: envisioning a revolution in middle mile logistics with extended range cargo drones

PurposeThis paper investigates the integration of middle mile drones (MMD) into logistics operations, addressing two key questions: (1) What are the drivers, financial implications and upcoming innovations associated with integrating drones into MM logistics? and (2) What challenges need to be overcome for successful implementation of drones in MM logistics?Design/methodology/approachThe study combines expertise from an industry professional with over ten years of experience in drone operations and logistics applications, along with insights gained from discussions with 33 industry executives.FindingsThe research identifies several unique advantages of integrating drones into MM logistics, including their potential to improve operational efficiency in challenging environments. However, significant challenges related to scalability, evolving airframe designs and operational constraints remain. Early-stage use cases demonstrate the viability of MMD technologies in lower-risk logistics environments, but broader implementation requires overcoming the identified challenges.Research limitations/implicationsAs MMD logistics is a nascent field, the study is exploratory and based on early-stage use cases and expert discussions. The limited scope of practical implementations may restrict the generalizability of the findings. Future research should focus on larger-scale operations and empirical studies of MMD integration in diverse logistics contexts.Practical implicationsThe findings offer valuable insights for practitioners related to the costs, benefits and challenges of integrating drones into logistics operations and for policymakers related to societal implications, workforce development, privacy and safety concerns, and environmental impact.Originality/valueThis paper contributes to the evolving understanding of drone applications in MM logistics by presenting early use cases and identifying both challenges and opportunities for MMD technology and offers a foundation for future research and practice in this emerging domain.

Intellectual capital meets Industry 4.0: transforming logistics through bibliometric insights

This study conducts a comprehensive bibliometric analysis to explore the evolving relationship between intellectual capital and Industry 4.0 within the logistics sector. The research identifies trends, key contributors, and thematic developments in this interdisciplinary field using data from two leading scientific databases, Web of Science and Scopus. The primary objective is to understand the critical areas of intellectual capital—human, structural, and relational—and their application in Logistics 4.0, driven by technological innovations such as cyber-physical systems and digital transformation. The methodology employs established bibliometric techniques, including co-occurrence and thematic mapping, to evaluate research questions addressing publication trends, country contributions, source relevance, and author influence. Data from 2001–2024 for Web of Science and Scopus datasets reveal key insights: consistent publication growth, leading contributions by countries like China and the USA, and the emergence of themes such as sustainability, innovation, and digital transformation. Comparative analysis highlights differences in keyword trends and collaborative networks between databases, with Scopus displaying a notable time lag in topic evolution. Findings emphasise the pivotal role of intellectual capital in driving logistics innovation, underscoring its integration with Industry 4.0 technologies. Limitations include potential database biases and a data lag for 2024. This study is a foundational reference for scholars and practitioners who leverage intellectual capital for competitive advantage in the digitised logistics era.

Vision-Based Deep Reinforcement Learning of Unmanned Aerial Vehicle (UAV) Autonomous Navigation Using Privileged Information

The capability of UAVs for efficient autonomous navigation and obstacle avoidance in complex and unknown environments is critical for applications in agricultural irrigation, disaster relief and logistics. In this paper, we propose the DPRL (Distributed Privileged Reinforcement Learning) navigation algorithm, an end-to-end policy designed to address the challenge of high-speed autonomous UAV navigation under partially observable environmental conditions. Our approach combines deep reinforcement learning with privileged learning to overcome the impact of observation data corruption caused by partial observability. We leverage an asymmetric Actor–Critic architecture to provide the agent with privileged information during training, which enhances the model’s perceptual capabilities. Additionally, we present a multi-agent exploration strategy across diverse environments to accelerate experience collection, which in turn expedites model convergence. We conducted extensive simulations across various scenarios, benchmarking our DPRL algorithm against state-of-the-art navigation algorithms. The results consistently demonstrate the superior performance of our algorithm in terms of flight efficiency, robustness and overall success rate.

Improvement of Concrete Construction Work Performance Through Employment of Lean Logistics Principles

ABSTRACTIn logistics systems, the use of methods, approaches, and management procedures with the aim of optimizing all activities has increasingly been promoted. In the case studies, the logistics of the concrete construction work during the implementation of concrete frame structures of residential building projects is analyzed. The examination of the logistics of concrete construction work was carried out using a combination of monitoring, measurement, comparison, and modeling methods. In addition to obtaining real data on the mentioned logistics of concrete construction work, by applying the Just in Time method, as one of the methods of lean logistics, the optimal logistics of ready‐mix concrete transport to construction sites was simulated. In order to reveal the most significant wastages during concrete construction work, the essential parameters of real logistics were compared with the parameters of simulated optimal logistics of ready‐mix concrete transport. The goal of the research is to evaluate the intensity of the application of logistics and its elements in the implementation of construction and to point out the importance of the involvement of lean production methods and thus also lean logistics in the construction industry. Based on this study results from construction projects in Slovakia and also based on the experience of the authors of this work, it can be concluded that the use of logistic management in the supply of the main construction material in the production of monolithic concrete structures is poor, and this causes a lot of unnecessary waste, having an adverse effect not only on the construction time, but also on the management of individual construction resources. It has been proven that investment in planning and management of logistics can lead to increased overall efficiency of the construction and can bring profit in the form of system, synchronization of construction process resources.

Evaluation of the Planet constellation’s daily coverage for estimating the number of vessels at Daikoku Pier automobile terminals, Port of Yokohama, Japan

The automotive industry is a key industry in Japan; however, the worker shortage has affected transportation in the ports and harbors, including that of automobile shipping. Automobiles are shipped using roll-on/roll-off (Ro-Ro) ships. When many Ro-Ro ships dock at the same time, problems related to the shortage of workers and limited storage space for automobiles are exacerbated, decreasing transportation efficiency. The spatial resolution and temporal frequency of satellite imagery have improved with Planet Labs satellite constellations making daily observations of the Earth’s surface. Moreover, the field of remote sensing is seeing an increasing number of logistic applications. As such, this study aimed to evaluate the Planet constellation’s daily coverage for estimating the actual usage of seaport automobile terminals by counting the number of vessels in each image. Here, we focused on Daikoku Pier automobile terminals in the Port of Yokohama, Japan, from 2018 to 2023. Images were classified by visual interpretation into three categories: data available for analysis, data not available for analysis due to cloud, and lack of data. Over the 6-year period, 37.1% of the data were classified as data available for analysis; 21.2%, data not available for analysis due to cloud; and 41.8%, lack of data. The visual inspection survey was conducted twice, with an agreement rate of 90.5% between the two surveys. The number of vessels were then counted two times from “data available for analysis” class, and the discrepancies were corrected. This result was compared to the actual schedule information and the accuracy was 89.0%. The number of vessels docked at the same time tended to be lower in August than in other months. The days from the 25th to 31st tended to have a higher number of vessels than other days, regardless of the month. It seems difficult to use Planet constellation for daily site progress management because data was sometimes available 1 day per month. Planet constellation can be useful in estimating the actual usage through long-term monitoring. We expect that the results of this study will be helpful to those interested in using daily satellite imagery for improving work conditions and efficiencies.

A Multicriteria Approach for Ship Bunkering System

Abstract This research aims to enhance maritime fuel supply’s efficiency, safety, and sustainability by advocating for the standardization of bunkering systems in ports. Through the application of Multi-Criteria Decision Making (MCDM) and the Analytical Hierarchy Process (AHP), this study assesses three primary bunkering methods: Ship to Ship, Truck to Ship, and Onshore to Ship. The criteria for evaluation encompass safety, economic factors, operational efficiency, technical considerations, and additional relevant aspects. Stakeholder feedback underscores the significance of safety (0.281) and economic (0.274) criteria, eclipsing operational (0.274), technical (0.137), and other factors (0.033) in importance. The findings prominently advocate for the Onshore to Ship method as the superior option for standardization in principal ports, marked by a value of 0.480, with Truck to Ship and Ship to Ship methods trailing in preference. Sensitivity analysis reinforces the stability of this outcome, indicating that adjustments in criteria weightings scarcely affect the overall conclusion, thereby affirming the Onshore to Ship method’s predominance. This method, characterized by a pipeline connecting the jetty to ships, holds considerable potential for both research and practical applications in port logistics.

IoT-Enabled Cross-Platform Applications for Real-Time Logistics Monitoring

Internet of Things (IoT)The integration of Internet of Things (IoT) technology and cross-platform mobile applications has revolutionized real-time logistics monitoring, enabling unprecedented visibility, efficiency, and proactive management of supply chain operations. This article explores the key components of IoT-enabled logistics monitoring, including IoT sensors, data integration, real-time tracking, automated notifications, predictive maintenance, and proactive supply chain management. The benefits of implementing these solutions are highlighted, such as reduced risks, optimized routes, improved operational efficiency, and enhanced customer satisfaction. The article also discusses the challenges associated with IoT implementation, including data integration, connectivity, security, and privacy, and provides strategies for overcoming these obstacles. Through real-world case studies, the tangible impact of IoT-enabled cross-platform applications in logistics is demonstrated, showcasing the potential for significant improvements in cost savings, asset utilization, and overall supply chain performance. As the IoT landscape continues to evolve, the adoption of these technologies will become increasingly critical for logistics providers looking to maintain a competitive edge in the market.

Micro UAVs with Fixed Wings: Design, Technological Solutions, and Tests

Considering the advantages of using expanded polystyrene (EPS) reinforced with adhesive tape made of glass fibers, this paper presents a design and technological solution for a functional drone of class C1, meaning a maximum take-off mass of 900 g, and tests validating the use of EPS for small UAVs under flight conditions. The selected profile was MH-49, which had a maximum chord thickness of 10.5%. This profile demonstrated a much lower coefficient of pitching moment than that of the NACA 63215 profile, giving this flying-wing UAV superior governability. This airfoil implies a geometry with greater attenuation of the trailing edge, and the design favors the placement of stress concentrators towards the trailing edge. Due to the use of fiberglass tape reinforcement technology, it is possible to address this profile, implying improved aerodynamic performance. The use of EPS in the disposable UAV industry may bring significant benefits, contributing to the development of high-performance, versatile, and low-cost UAVs, suitable for a wide range of tactical and logistic applications. This study presents the design, the fabrication, and testing of this drone, highlighting the advantages and possible challenges of the proposed solution.

Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

Flexible Hyper-Distributed IoT–Edge–Cloud Platform for Real-Time Digital Twin Applications on 6G-Intended Testbeds for Logistics and Industry

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform’s capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform’s support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load.

Set Covering Routing Problems: A review and classification scheme

Cyclic routing problems are a well-researched category of combinatorial problems in Operations Research (OR). They involve finding an optimal cycle (route), starting from an initial point (e.g., depot), visiting some nodes (e.g., customer or demand points), and returning to the starting point while adhering to constraints and optimizing a specific objective. These problems are categorized into two main types: (i) Hamiltonian routing problems: All customer nodes must be visited (must be on a cycle), such as the Traveling Salesman Problem (TSP), seeking the shortest possible cycle visiting each node once. (ii) Non-Hamiltonian routing problems: At least one customer node is not on a route. This work mainly focuses on classifying one of the main sub-problems of non-Hamiltonian routing problems, known as Set Covering Routing Problems (SCRPs), in which customers may be directly visited or covered by another node on the cycle or route. The SCRPs also incorporate Steiner nodes (transition points) to cover the customers who are not directly visited, integrating covering and routing decisions. SCRPs have wide-ranging applications in logistics, transportation planning, healthcare, telecommunication network design, and disaster response, making them vital for theoretical exploration and practical solutions. This work reviews the studies on SCRPs, presenting a classification scheme and a generic mathematical model for these problems, enabling a unified analysis and comparison. Finally, the study gaps, future research directions, and the limitations and implications for practical applications are discussed.

The Impact of Green Logistics on the Sustainable Development of Enterprises

This study examines the importance of green logistics for the sustainable development performance of enterprises. It analyzes the connotation and application of green logistics, the significance of corporate sustainable development, and the relationship between the two, to clarify the decisive influence of green logistics in contemporary enterprises. At the same time, it points out the existing problems in the development process of green logistics from multiple perspectives, indicating that the further development of green logistics requires the efforts and cooperation of enterprises, governments, and multiple parties in the supply chain. Following this positioning, several conceptual solutions are proposed, such as the innovation and sustainable utilization of green technology and green management in logistics, and the government's role as a third party in regulating the application of green logistics. The research results have theoretical significance, highlighting the impact of green logistics on corporate sustainable development and the important role of the government in sustainable development. The practical significance includes improving corporate sustainable development performance indicators in the technical and management dimensions, in order to further improve the organization's sustainable operations and behavior.

Multistage stochastic programming with a random number of stages: Applications in hurricane disaster relief logistics planning

We consider a logistics planning problem of prepositioning relief commodities in preparation for an impending hurricane landfall. We model the problem as a multi-period network flow problem where the objective is to minimize the total expected logistics cost of operating the network to meet the demand for relief commodities. We assume that the hurricane’s attributes evolve over time according to a Markov chain model, and the demand quantity at each demand point is calculated based on the hurricane’s attributes (intensity and location) at the terminal stage, which corresponds to the hurricane’s landfall. We introduce a fully adaptive multi-stage stochastic programming (MSP) model that allows the decision-maker to adapt their logistics decisions over time according to the evolution of the hurricane’s attributes. In addition, we develop a novel extension of the standard MSP model to address the challenge of having a random number of stages in the planning horizon due to the uncertain landfall time of the hurricane. We benchmark the performance of the adaptive decision policy given by the MSP models with alternative decision policies, including a static policy, a rolling-horizon policy, a wait-and-see policy, and a decision-tree-based policy, all based on two-stage stochastic programming models. Our numerical results and sensitivity analyses provide key insights into the value of MSP in the hurricane disaster relief logistics planning problem.

Robust motion control for an underactuated wheeled bipedal robot utilizing sliding mode strategy

In recent years, wheeled bipedal robots (WBRs) have become one of the frontier fields of robotic research, attracting immense interest from scholars globally. They can not only move rapidly on flat ground through wheels but also possess satisfactory adaptability to uneven terrain due to the introduction of legs. With concise structure and strong agility, WBRs have broad prospects of application in logistics, routing inspection, home service, and so on. However, the inherent underactuation of such robots presents a significant challenge in terms of balance control, particularly in the face of uncertainties and external disturbances. To address this problem, this paper presents a sliding mode control strategy for WBRs, which guarantees robust balance performance even under the influence of various external disturbances. Specifically, the dynamic equations of WBRs are first rearranged in cascaded form, facilitating targeted control design. After that, the wheel torque and leg supporting force are carefully devised to ensure that the sliding surfaces converge to zero within a fixed time. Rigorous Lyapunov-based analysis has shown that the desired equilibrium point is asymptotically stable. Finally, extensive hardware experiments are undertaken, providing convincing evidence of the superior balance control performance achieved by the proposed method.

The Ant Colony Optimization Algorithm Applied in Transport Logistics

The Vehicle Routing Problem belongs to graph optimization and its goal is to find shortest routes visiting a given set of customers with additional constraints present. The article presents the ant colony optimization metaheuristic which solves vehicle routing problems and its real-life application in transport logistics (finding routes for delivery companies). The metaheuristic generated high-quality solutions (superior to compared methods). Our tool is flexible and enables us to solve various variants of routing problems so it is well suited to specific needs of transportation companies.

Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review

Objective - Artificial Intelligence (AI) has become a pivotal technology in transforming logistics performance. This paper aims to comprehensively understand how AI-enabled solutions improve efficiency, accuracy, and responsiveness in logistics operations. The study focuses on synthesizing current research to explore AI applications across various logistics domains, such as predictive analytics, autonomous vehicles, and smart warehousing. Methodology/Technique – A systematic review approach was used to gather and analyze existing literature on AI applications in logistics. The review covered studies published in recent years, highlighting the advancements and impact of AI on logistics processes. The methodology included selecting relevant sources, categorizing AI applications, and assessing their effects on different logistics functions. Finding – The findings reveal that AI adoption substantially improves logistics operations, including enhanced operational performance, cost reduction, and increased customer satisfaction. Specific AI applications, such as predictive analytics for demand forecasting, autonomous vehicles for transportation, and smart warehousing for inventory management, were identified as key contributors to these improvements. However, challenges such as data privacy concerns and integration complexities were also noted. Novelty – This study's novelty lies in its comprehensive analysis of AI applications across various logistics domains, offering a holistic view of AI's role in optimizing logistics performance. This paper highlights the benefits of AI adoption and addresses the associated challenges, providing insights into future research directions and practical implications for leveraging AI in logistics. Type of Paper: Review JEL Classification: C61, C62, D83. Keywords: Artificial Intelligence; Logistics; Performance Improvement; Predictive Analytics; Autonomous Vehicles; Smart Warehousing Reference to this paper should be referred to as follows: Fatorachian, H. (2024). Leveraging Artificial Intelligence for Optimizing Logistics Performance: A Comprehensive Review, GATR-Global J. Bus. Soc. Sci. Review, 12(3), 146–160. https://doi.org/10.35609/gjbssr.2024.12.3(5) _____________________________________

Alternative Fuels in Sustainable Logistics—Applications, Challenges, and Solutions

Logistics is becoming more cost competitive while customers and regulatory bodies pressure businesses to disclose their carbon footprints, creating interest in alternative fuels as a decarbonization strategy. This paper provides a thematic review of the role of alternative fuels in sustainable air, land, and sea logistics, their challenges, and potential mitigations. Through an extensive literature survey, we determined that biofuels, synthetic kerosene, natural gas, ammonia, alcohols, hydrogen, and electricity are the primary alternative fuels of interest in terms of environmental sustainability and techno-economic feasibility. In air logistics, synthetic kerosene from hydrogenated esters and fatty acids is the most promising route due to its high technical maturity, although it is limited by biomass sourcing. Electrical vehicles are favorable in road logistics due to cheaper green power and efficient vehicle designs, although they are constrained by recharging infrastructure deployment. In sea logistics, liquified natural gas is advantageous owing to its supply chain maturity, but it is limited by methane slip control and storage requirements. Overall, our examination indicates that alternative fuels will play a pivotal role in the logistics networks of the future.

Scaling-up 5G adoption in smart ports: barriers and enablers

ABSTRACT As the fifth generation of mobile communication technology, 5G is predicted to be a significant focus for investment by the logistics industry in the coming years. One area where it is expected to have a significant impact is ports, enabling further digitization and the development of Smart Ports. The aim of this paper is to investigate the barriers and enablers to 5G adoption, focusing on the UK port industry. Data has been collected from nine senior representatives from the UK port sector as well as two technology providers. Potential use cases are identified, along with barriers and enablers to their adoption. These including building a business case with appropriate use cases, the trade-off between legacy systems and 5G capability, technology maturity, skill development within the workforce, and the role of government in supporting innovation. This work contributes by giving insights into the challenges when introducing 5G within the port environment. Research of 5G applications in logistics currently focuses more on the technological applications rather than the business implications from adoption.

Graph Theory Applications in Optimizing Emergency Response Logistics

This study explores the utilization of graph theory and optimization methods to improve the effectiveness of emergency response logistics. The study devises and evaluates multiple algorithms with the objective of enhancing the efficiency of emergency routes and resource distributions, showcasing advancements compared to conventional approaches. The findings demonstrate substantial decreases in response times, operational expenses, and improvements in resource utilization rates. This study provides reliable and powerful resources for emergency managers to enhance their ability to strategize and carry out response operations with more efficiency. Furthermore, the models introduced in this work are versatile and may be utilized in different emergency situations to guarantee the most efficient allocation of resources and response times. The results emphasize the practical significance of graph theory in emergency logistics, establishing a solid basis for additional advancement and practical implementation.