Logistics Applications Research Articles

Analysis on the Application of Artificial Intelligence in the Field of Logistics

This paper discusses the wide application of artificial intelligence in the logistics industry, from intelligent distribution to intelligent transportation, through route optimization, automated warehouse management, demand forecasting, inventory management and other means, greatly improve logistics efficiency and reduce costs. Intelligent transport enhances traffic management, ensures road safety and optimizes resource utilization with road condition monitoring, autonomous driving and freight optimization technologies. In the face of challenges such as data security and privacy protection, artificial intelligence continues to promote the transformation of the logistics industry to a smarter and more efficient form. In the future, artificial intelligence will be further integrated into the logistics field, and through data-driven intelligent logistics management and applications in international logistics, more refined supply chain management, better resource scheduling and higher customer service standards will be achieved.

Business models on the basis of digital infrastructure data

The project Ready for Smart City Robots (R4R) is developing strategies for the detailed capture of infrastructural environmental parameters. These are intended to be utilized for autonomous mobility and logistics applications on pedestrian and bicycle paths. The project evaluates the quality and completeness of data for Smart City concepts, using examples from (autonomous) micro-mobility in logistics and transportation. The results encompass the following areas: firstly, the identification of necessary environmental information for environmentally friendly, automated last-mile logistics, along with a continuous data collection method for up-to-date information. Secondly, the examination of various integration scenarios for autonomous delivery services and an autonomous bicycle rental system in rural areas. This includes the development of application scenarios and the scientific support of the prototype construction of the necessary technical prerequisites for the data/sensor boxes. The following outlines the business model potentials for data collection, autonomous micro-mobility, sharing services, and Smart City applications.

Exploring the Application of Blockchain Technology in Logistics and Transportation System

This paper explores the application of blockchain technology in improving the management and efficiency of logistics and transportation systems. By leveraging blockchain technology, real-time recording, traceability, data sharing, security, and trustworthiness of logistics information can be achieved, thereby enhancing the efficiency and safety of logistics operations. The paper discusses the theoretical foundations of blockchain technology and logistics and transportation systems, highlighting key concepts such as decentralization, distributed ledger, consensus mechanisms, smart contracts, operations research, supply chain management, and transportation economics. Additionally, the literature review presents an overview of existing studies on logistics and transport systems and blockchain technology, illustrating various applications and benefits, such as sustainable transport frameworks, transportation and logistics management models, smart logistics systems, and supply chain traceability platforms. In conclusion, the exploration of blockchain technology's application in logistics and transportation systems offers comprehensive support for improving logistics efficiency, reliability, and safety, promising more efficient and reliable transportation services for the logistics industry in the future.

Two-Stage Delivery System for Last Mile Logistics in Rural Areas: Truck–Drone Approach

In rural areas of China, the challenges of efficient and cost-effective distribution are exacerbated by underdeveloped infrastructure and low population density, with last mile logistics distribution posing a significant obstacle. To address the gap in drone application for last mile logistics in rural areas, a truck–drone distribution model was developed based on the specific conditions of rural regions. The improved fuzzy C-means algorithm (FCM) and genetic simulated annealing algorithm (GASA) were employed to tackle real−world cases in rural areas. The focus of the truck–drone system is to optimize the rural logistics distribution process, reduce delivery time, and minimize costs while considering factors such as maximum mileage of trucks and drones as well as customer priority. Compared to traditional methods, this system has demonstrated notable improvements in distribution efficiency and cost reduction, offering valuable insights for practical drone applications in last mile rural logistics.

Indoor Positioning of AGVs Based on Multi-Sensor Data Fusion Such as LiDAR

In recent years, with the rapid growth in technology and demand for industrial robots, Automated Guided Vehicles (AGVs) have found widespread application in industrial workshops and smart logistics, emerging as a global hot research topic. Due to the volatile and complex working environments, the positioning technology of AGV robots is of paramount importance. To address the challenges associated with AGV robot positioning, such as significant accumulated errors in wheel odometer and Inertial Measurement Unit (IMU), susceptibility of Ultra Wide Band (UWB) positioning accuracy to Non Line of Sight (NLOS) errors, as well as the distortion points and drift in point clouds collected by LiDAR during robot motion, a novel positioning method is proposed. Initially, Weighted Extended Kalman Filter (W-EKF) is employed for the loosely coupled integration of wheel odometer and Ultra Wide Band (UWB) data, transformed into W-EKF pose factors. Subsequently, appropriate addition of W-EKF factors is made during the tight coupling of pre-integrated Inertial Measurement Unit (IMU) with 3D-LiDAR to counteract the distortion points, drift, and accumulated errors generated by LiDAR, thereby enhancing positioning accuracy. After experimentation, the algorithm achieved a final positioning error of only 6.9cm, representing an approximately 80% improvement in positioning accuracy compared to the loosely coupled integration of the two sensors.

AN ALGORITHM FOR SOLVING A TWO-STAGE CONTINUOUS-DISCRETE LOCATION PROBLEM FOR MEDICAL LOGISTICS OPTIMIZATION

The research paper focuses on logistics optimization, a critical component in supply chain management across various sectors, including healthcare. Efficient coordination of medical logistics is essential for maintaining public health and welfare, particularly during global emergencies where quick and effective distribution of medicine is crucial. This study aims to create and analyze a model and algorithm for a two-stage continuous-discrete location problem within medical logistics applications. We present a mathematical model tailored for a two-stage continuous-discrete location problem in medical logistics, considering the unique aspects of this field. The solution algorithm combines genetic methods with the optimal partition of sets theory. Additionally, we demonstrate the algorithm's effectiveness through a software application, using it to solve a representative model problem.

Real-time schedule adjustments for conflict-free vehicle routing

Conflict-Free Vehicle Routing Problems (CFVRPs) arise in manufacturing, transportation and logistics applications where Automated Guided Vehicles (AGVs) are utilized to move pallets and containers. A peculiar feature of these problems is that collision avoidance among vehicles must be considered explicitly. To make things more complex, the uncertainty affecting both travel times and machine ready times often results in vehicle delays or anticipations that require real-time modifications to the fleet nominal plan. In this paper, the determination of such modifications (schedule adjustment problem in CFVRPs) is modeled as a sequential decision problem for which we develop a tailored fast exact algorithm suitable for any objective function that is non-decreasing in the arrival times. Computational results show that optimal solutions can be found within at most 3.3 milliseconds for instances with up to 300 vehicles with improvements of various performance measures up to 74% compared to state-of-the-art solution algorithms.

SMART LOGISTICS SOLUTIONS FOR REDUCING FOOD WASTE: A CASE OF D NIPAH CATERING

The food and beverage (F&B) business is faced with a huge challenge in the form of food waste, which has a negative impact on profitability, sustainability, and environmental conservation. In recent years, smart logistical solutions have emerged as a potentially successful strategy for addressing the critical problem of food waste in the F&B industry. This article presents an overview of the application of smart logistics in the context of reducing food waste. The paper has a specific focus on the integration of technologies related to the Internet of Things (IoT) and production planning strategies. It investigated the concept of smart logistics and its benefits to improve decision-making, enhance visibility, and optimise the procedures involved in supply chain management. The study also underlined the role played by IoT in providing real-time monitoring, data collecting, and analysis, all of which can help in identifying and reducing concerns related to food waste. Qualitative methodologies involving the 5-whys analysis were used in this study to identify the root cause of the problem. Additionally, the present study highlights the significance of efficient production planning in reducing the amount of goods that are produced in excess, improving inventory management, and ensuring that production is in line with consumer demand. The findings also highlight the significance of combining the IoT technology and production planning in small and medium-sized enterprises (SMEs) to improve the efficiency and efficacy of their efforts to reduce food waste.

Endurance results of a refuels fleet test in a real application based on directly comparable truck test pairs

AbstractSynthetic fuels from a renewable base are an essential part of a greenhouse gas-neutral mobility, especially in transport sector. While scaling production of e-fuels (fuels based on electrolysis hydrogen) is ongoing, HVO called paraffinic diesel fuels are already available. Their production is based on the hydrogenation of waste and residues and they are established as diesel substitute in several applications. With regard to the approval of these fuels in German regulation, the question repeatedly arises as to whether they can be used easily and what effects can be achieved. This article describes an application in a real logistics application, in which both the everyday use and the concrete comparability to a refueling with conventional gas station diesel were ensured. The use of several parallel active and different truck pairs has shown that the use of HVO in existing vehicles has achieved the desired CO2 reduction. A detailed analysis of the engine oil also showed that no undesirable effects could be observed here either. From the perspective of this project, HVO fuels are ready for use for a significant greenhouse gas reduction in logistics.

Excellent interfacial compatibility of phase change capsules/polyurethane foam with enhanced mechanical and thermal insulation properties for thermal energy storage

The incorporation of phase change microcapsule (microPCMs) substantially augmented the temperature regulation capacity of foams, endowing it with outstanding application potential in numerous industrial domains. However, interfacial compatibility issues between the capsule and the substrate always exist and affect foam performance. In this study, a milli-meter macrocapsule (macroPCMs) containing microPCMs was prepared using calcium alginate gel as the second layer wall to physically improve the interfacial compatibility. And poly (N-hydroxymethyl acrylamide) was in situ polymerized in the gel layer and provided active alcohol hydroxyl groups to form chemical grafting with the polyurethane raw materials, which further chemically improved the interfacial compatibility. These measures created a significantly improved foam microstructure and a greatly increased foaming capacity, which thus endow the composite foam with excellent thermal insulation performance and mechanical strength. Compared with conventional microPCMs-based foam, the foaming capacity of the macroPCMs-based foam increased by 40 folds, the temperature regulation duration increased by 7 folds, and the mechanical performance increased by 3 folds. Moreover, the insulated incubator fabricated with the macroPCMs-based foam exhibited two-fold greater thermal insulation efficiency than conventional models containing internal ice incubator, which validated the potential of employing macroPCMs-based polyurethane foam in cold chain logistics applications.

Software and Architecture Orchestration for Process Control in Industry 4.0 Enabled by Cyber-Physical Systems Technologies

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Metaheuristic algorithms and their applications in performance optimization of cyber-physical systems having applications in logistics

Metaheuristic algorithms and their applications in performance optimization of cyber-physical systems having applications in logistics

Research on Intelligent Logistics Application based on Blockchain Technology

With the rapid development of information technology, intelligent logistics has become the development trend of modern logistics industry. As an innovative technology, b aerznm nzn lockchain technology has the characteristics of decentralization, data tamperability and high security, which provides new opportunities for the development of intelligent logistics. This paper first introduces the basic principles and characteristics of blockchain technology, and then analyzes the application scenarios of blockchain technology in intelligent logistics, including logistics tracking, supply chain management and so on. Then, it discusses the key technologies of building intelligent logistics platform based on blockchain technology, such as consensus mechanism. Finally, the advantages and challenges of intelligent logistics application based on blockchain technology are summarized, and the future development trend is prospected.

Drone Applications in Logistics and Supply Chain Management: A Systematic Review Using Latent Dirichlet Allocation

Drone Applications in Logistics and Supply Chain Management: A Systematic Review Using Latent Dirichlet Allocation

Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment

AbstractThe loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path.

Routing and scheduling of platform supply vessels in offshore oil and gas logistics

In this work, we focus on an operational logistics problem that arises in the offshore oil and gas exploration and production industry. In particular, we aim to design cost-effective routes and schedules for platform supply vessels, which are routinely employed to deliver necessary supplies to the platforms as well as to collect from those platforms used materials that need to be transported back to the onshore base for maintenance, reuse, or discarding. To address the rich-featured routing problem that arises in this offshore logistics application, we introduce a novel mixed-integer linear programming formulation and propose a specialized branch-and-cut algorithm to solve such a model. Furthermore, in order to evaluate our proposed algorithm’s performance, we conduct an extensive computational study using representative benchmark instances inspired by real operational data. The computational results show that our algorithm solved the majority of those instances to optimality, demonstrating its potential for practical use in offshore oil and gas logistics operations.

Heuristics Integrated Deep Reinforcement Learning for Online 3D Bin Packing

Online 3D Bin Packing Problem (3D-BPP) has a wide range of industrial applications and there is an emerging research interest in learning optimal bin packing policy and deploying it for real logistics applications. From the heuristic methods to the deep reinforcement learning (DRL) methods, the previous works have proposed many solutions to solve the online 3D-BPP. However, none of them have studied what and how heuristics can be modelled into DRL to build a more effective and practical bin packing pipeline. In this work, we thoroughly investigate what heuristics can be used in online 3D-BPP and how to effectively integrate the heuristics with the DRL. First, we design 3 different heuristics based on the physical rules of the real world and the experiences of the human packers, including the Physics-Heuristics, the Packing-Heuristics and the Unpacking-Heuristics. Second, we model the 3 types of heuristics into the DRL framework and propose a novel heuristic DRL method to solve the online 3D-BPP. Extensive experimental results show that our method achieves state-of-the-art bin packing performance and the resulting real-world system is able to reliably finish the bin packing task in real logistics scenarios. Supplementary video is available at https://www.youtube.com/watch?v=x8GpmEELq18. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The rapid growth of e-commerce has significantly increased the burden of human packers in logistic warehouses, where the workers need to pick the products from a conveyor and pack them into bins (i.e. the online 3D bin packing). Thus it is of great importance to develop intelligent robotic systems to replace human labor, which is a long-standing topic in the field of control and automation science. This paper makes a substantial contribution to the related field by studying the online 3D bin packing in terms of both the theory and practice. On the one hand, the simulated experiments suggest that the presented algorithm significantly improves the space utilization of bin packing. On the other hand, the robotic system developed based on the proposed method can favourably finish the bin packing task in real logistics scenarios, demonstrating the practical use of our approach. Consequently, the approach proposed in this paper is totally applicable in logistic warehouses and is promising to drastically improve the working efficiency of the product packing in real warehouses. In the future, we will extend the presented approach to pack irregular-shaped objects and then facilitate more logistics applications.

Requirements for Human-Machine-Interaction Applications in Production and Logistics within Industry 5.0 – A Case Study Approach

This research paper examines the evolution of human-machine interfaces (HMIs) in the manufacturing and logistics sectors in the context of Industry 5.0 (I5.0). I5.0 introduces a human-centered paradigm, and as digitalization transforms processes in these domains, human-machine interaction is undergoing a notable shift. Yet, there is a lack of I5.0 HMI requirements and design guidelines. Through a literature review, the research identifies and investigates general requirements for HMIs in I5.0. Findings include ethical principles, ergonomics, adaptability, human-centered design, intuitive interaction, and effective collaboration. It also uses a case study approach to highlight potential strategies and discuss challenges. The results provide valuable insights into considerations for designing effective HMIs for I5.0 applications, enhancing the user experience, improving productivity, and facilitating successful human-machine interactions.

Deep Reinforcement Learning for Solving Vehicle Routing Problems With Backhauls.

The vehicle routing problem with backhauls (VRPBs) is a challenging problem commonly studied in computer science and operations research. Featured by linehaul (or delivery) and backhaul (or pickup) customers, the VRPB has broad applications in real-world logistics. In this article, we propose a neural heuristic based on deep reinforcement learning (DRL) to solve the traditional and improved VRPB variants, with an encoder-decoder structured policy network trained to sequentially construct the routes for vehicles. Specifically, we first describe the VRPB based on a graph and cast the solution construction as a Markov decision process (MDP). Then, to identify the relationship among the nodes (i.e., linehaul and backhaul customers, and the depot), we design a two-stage attention-based encoder, including a self-attention and a heterogeneous attention for each stage, which could yield more informative representations of the nodes so as to deliver high-quality solutions. The evaluation on the two VRPB variants reveals that, our neural heuristic performs favorably against both the conventional and neural heuristic baselines on randomly generated instances and benchmark instances. Moreover, the trained policy network exhibits a desirable capability of generalization to various problem sizes and distributions.

A Label-correcting Algorithm for Constrained One-to-Many K-shortest Path Problem with Replenishment

The constrained one to many K-shortest path problem with replenishment (CKSPR) is an important variant of the classical shortest path problem with wide-range applications in transportation and logistics. In CKSPR, the objective is to find K number of shortest paths between an origin and multiple destinations, considering limitations on vehicle driving range and the availability of refueling resources along the paths. We propose a solution to the CKSPR using a mixed integer linear program (MILP); however, it is NP-complete and solving large-scale networks is computationally challenging. To tackle this, we introduce an efficient algorithm building upon Bellman’s classic dynamic programming approach. Our algorithm focuses on identifying K-shortest paths while considering driving range constraints, allowing vehicles to recharge en route and extend their reach. Numerical experiments demonstrate that the algorithm outperforms the MILP solvers with time limit, providing scalable solutions for large-scale networks. The analysis highlights the significant impact of large number of vertices on the problem difficulty. The source code is available at https://gitlab.com/AmirDavatgari/ckspr.