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Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

The article explores a recursive algorithm for determining Bhattacharyya parameters, which is a measure of the degree of channel polarization for telecommunications with polar coding. A method is given for determining the positions of information and fixed bits in a polar code. The principles of constructing a polar encoder and a polar decoder successive cancellation are considered. The Field-Programmable Gate Array implementation of the successive cancellation list decoder is considered. Experimental studies of the construction of a polar code using Gaussian approximation have been carried out. The influence of the design signal-to-noise ratio on the bit error rate of telecommunications with polar codes has been studied. The use of multi-position modulation in telecommunications with polar codes has been studied. It is expected that the results obtained will be useful in hardware and software implementation of 5G telecommunications with polar coding.

The densification of the vehicle fleet leads to numerous challenges in road safety management. Wireless Sensor Networks (WSN) helps to manage vehicle-vehicle-infrastructure interactivity, in particular to predict dangers and avoid accidents, detect and penalize traffic violations. This requires a massive deployment of communicating nodes embedded in vehicles and in road infrastructure. The reliability of the systems built on these nodes lies in the accuracy of the ranging. In order to improve the factors that impact accuracy, various ranging protocols have been developed, but their implementations most often involve the use of expensive and specialized transceivers, which incorporate a dedicated IEEE 802.15.4a ranging layer. Our approach implements the basic and advanced functions integrated into low-cost transceivers and microcontrollers. As result, in two or three links according to the case, our protocol performs simultaneously the ranging and the data exchange, against a specific ranging session with a minimum of three exchanges for the other protocols. Moreover, each node knows the ranging data and has its clock frequency adapted to the controlled process. So, we meet the challenges of large-scale low-cost node deployment, while ensuring high reliability and performance of ranging.

Two main elements are essential in terms of road traffic safety. The first element is accident prevention and the second is the minimization of accident severity once a crash has occurred. Concrete safety barriers have very good anti-collision performance against roadside obstacles, relatively modest construction and maintenance costs, and low dynamic deflection and therefore are widely used in tunnels. Thanks to their characteristic redirective profile these barriers can redirect errant vehicles into their original lane after collisions. However insufficient research has been done for increasing the performance of concrete barriers purposely designed for tunnel installations. This research presents the new “NDBA Tunnel” concrete safety barrier designed and constructed by the Italian Road Operator ANAS indicated to be installed in road tunnel sections for safety improvements. In Europe, road safety barriers must be designed in compliance with the European Standard EN 1317. Therefore, the barrier “NDBA Tunnel” was subject to the TB11 and TB81 full-scale crash tests according to the European EN 1317 regulation. The results prove the barrier's ability to absorb impact loads of light and heavy vehicles with a working width W2. Therefore, the NDBA concrete barrier can be installed on existing or new tunnels at a distance less than or equal to 70 cm from the facing of the tunnel wall.

This paper addresses the vulnerability of vehicular ad hoc networks (VANETs) to malicious attacks, specifically focusing on position falsification attacks. Detecting misbehaving vehicles in VANETs is challenging due to the dynamic nature of the network topology and vehicle mobility. The paper considers five types (constant attack, constant offset attack, random attack, random offset attack, and eventually stop attack) of position falsification attacks with varying traffic and attack densities, considered the most severe attacks in VANETs. To improve the detection of these attacks, a novel travel distance feature and an enhanced two-stage detection approach are proposed for classifying position falsification attacks in VANETs. The approach involves deploying the misbehavior detection system within roadside units (RSUs) by offloading computational work from vehicles (onboard units, or OBUs) to RSUs. The performance of the proposed approach was evaluated against different classifiers, including a wide range of paradigms (KNN, Decision Tree, and Random Forest), using the VeReMi dataset. Experimental results indicate that the proposed method based on Random Forest achieved an accuracy of 99.9% and an F1-Score of 99.9%, which are better not only than those achieved by KNN and Decision Tree but also than the most recent approaches in the literature survey.

The demand for scalable, open and granular networks has enabled mobile networks such as 5G to adopt new concepts including NFV (Network Function Virtualization). NFV separates the dependency of network functions from the hardware component, allowing them to be deployed flexibly and dynamically across network slices. In this way, operators can deliver personalized services and optimize the use of network resources, contributing to greater operational efficiency and an enhanced end-user experience. However, there are several issues to be addressed, such as the security of VNFs and the way in which service provider customers instantiate NSSIs (Network Slice Subnet Instance). In this article, we present a new approach based on blockchain 2.0, which guarantees the immutability of VNFs and templates, as well as highly secure instantiation with access management and support for replay attacks.

Understanding the main research directions in transport is crucial to provide useful and relevant insights. The analysis of Horizon 2020, the largest research and innovation framework, has been already realized in a few publications but rarely for the field of transport. Thus, this article is devoted to fill this gap by introducing a novel application of topic modelling techniques, specifically the Latent Dirichlet Allocation (LDA), in the Horizon 2020 framework for transport projects. The method is using the Mallet software with pre-examined code optimizations. As the first step, a corpus is created by collecting 310 project abstracts; afterward, the texts of abstracts are prepared for the LDA analysis by introducing stop words, optimization criteria, the number of words per topics, and the number of topics. The study successfully uncovers the following five main underlying topics: road and traffic safety, aviation and aircraft, mobility and urban transport, maritime industry and shipping, open and real-time data in transport. Besides that, the main trends in transport are identified based on the frequency of words and their occurrence in the corpus. The applied approach maximizes the added value of the Horizon 2020 initiatives by revealing insights that may be overlooked using traditional analysis methods.

The digitalisation of the supply chain has presented substantial opportunities for companies to enhance resource allocation, pricing strategies, and overall operational efficiency. In this context, researchers play a crucial role in developing novel methodologies and algorithms that can confer a competitive edge to stakeholders. This study employed operational research methods to investigate the specific requirements of researchers in this domain. The results highlight the challenges, motivations, and practical needs experienced by researchers. Additionally, data-related issues and databases were examined to identify areas of improvement. The findings indicate that enhancing researchers' engagement in data-driven solutions for supply chain problems primarily hinges on addressing issues related to data quality, data accessibility, and regular dataset updates. By addressing all the aspects defined in this study, organisations can enhance the practical implementation of findings and drive advancements in supply chain management.

Railway projects are incredibly costly due to their complex nature. This study performs risk analysis and management of Pakistan railways ML-1 project expansion and upgradation that will be undertaken at US$6.8 billion under the China-Pakistan Economic Corridor (CPEC). The risks were identified based on an extensive literature review and their relevance to the CPEC. We employed fuzzy set theory (integrated with the risk severity index) to obtain the most severe risks. The novelty of this study is the technique employed to analyse and rank the risk, also there are no previous studies conducted that emphasized the construction risk of railway projects in Pakistan. The study is useful for all stakeholders of the project, as it can plan for them to tackle these risks and implement ML-1 successfully.

Aviation is one of the most global industries, and if we can model and predict a country’s air transportation flow and indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This study proposes a new modeling, and prediction method that employs both fractional calculus and Multi Deep Assessment Methodology (MDAM) techniques. For the application, air passengers carried, air freight, available seat kilometers, number of flights, destination points, international travelers, international destination points, and international flight data between 2011 and 2019 for eight countries with the busiest airports were chosen. As a result, the highest modeling error was discovered to be Germany’s air transport freight factor expressed as a percentage of 1,59E-02. The percentage of predictions with errors less than 10% was 90.278. We also compared the performance of two different MDAM methodologies. The novel MDAM wd methodology proposed in this paper has a higher accuracy in aviation factors prediction and modeling.

The objective of this article is to provide an in-depth exploration of the complex task of container storage at seaports, a problem characterized as one of the challenging NP (Non-Deterministic Polynomial time) problems. Seaports are faced with the dilemma of accommodating a finite number of containers due to the constrained surface area available, making the management of container storage operations a formidable task.

Potholes detection is an essential aspect of road safety and road infrastructure maintenance. Potholes, which are typically caused by a combination of heavy traffic and weather, are depressions or holes in the road surface that can cause damage to specific parts of a vehicle. Autonomous vehicles, in particular, must be capable of detecting and avoiding them. Hitting a deep or sharp-edged pothole at high speed can lead to loss of control or even an accident. This makes pothole detection all the more important. The accuracy of pothole detection systems installed in autonomous vehicles may be significantly impaired by adverse weather and bad light conditions. Therefore, the classification accuracy of selected well-known computer vision models for pothole detection under these specific conditions has been investigated. The results were then compared with state-of-the-art methods. Our findings showed that we outperformed many of them when used under adverse weather and low light situations. This paper presents valuable insights into the precision of various computer vision models for potholes detection. It may aid in selecting the optimal model for a specific application.

The air pollution of our planet is rising, and the contribution of road transport to global pollution has a serious impact on this phenomenon. Previous papers have analysed and recommended measures to reduce road transport’s negative environmental impact and carbon footprint. However, some restrictions are impossible (or very costly) to meet, even in developed countries. Unfortunately, presenting the impact of transport on air pollution levels as a whole can only give a general picture. This paper provides a more detailed analysis and attempts to assess the impact of one of the most important elements shaping modern transport, that is, vehicle engine types. Thus, the main objective of the study is to analyse and evaluate the different types of engines in vehicles from the point of view of technical, environmental, and economic aspects in European countries and to verify whether Europe is ready to implement the European Green Deal. The results indicate significant technological developments must occur in electric vehicles to become environmentally better than combustion engine-based cars. Additionally, in the case of some developing countries, owning a means of transport is still perceived as a symbol of a certain status, which is why it is still an important material asset. Thus, in rich countries, material status and environmental awareness (e.g. choice of public transport) will help to achieve climate neutrality, while poorer countries (even developed ones) may have severe problems in meeting EU requirements. Overall, while answering some questions, this article also poses new ones. Decision-makers often face challenging aspects. This article is intended to give them a basic knowledge to pursue an environmental policy that is viable and feasible for all countries.

Nowadays, supply chain and road networks are full of complexity. Companies can differentiate themselves by offering superior customer service. Therefore, pharmaceutical distributors need to optimize route distribution for efficient and effective delivery to pharmacies, enhancing their competitiveness. In the field of operations research (OR), efficient route optimization is a significant function of the Vehicle Routing Problem (VRP) where the goal is to find the optimal distance travelled. The VRP can be solved using a range of algorithms, comprising exact methods, heuristics and meta-heuristics. This paper gives the basic concepts of some algorithms and these algorithms are compared based on the results. This study also extends its focus to the variation known as the Vehicle Routing Problem with Time Windows (VRPTW), that closely mirrors real-world scenarios, allowing for a more practical assessment of pharmaceutical distribution challenges and optimization solutions.

The paper addresses the issue of evaluating the attractiveness of new airport connections, one of which is a global transfer hub and the other is a regional European international airport. The attractiveness of new connections expressed, for example, by predicting the demand for new flight routes has long been studied, mainly using gravity models. The aim of the presented paper is to check whether an approach based on gravity models could be replaced with other approaches, in this case by multi-criteria evaluation methods. Questions of substitution of approaches based on gravity models, compiled for demand prediction, are relevant in cases where there are not enough input data for existing gravity models. The absence of input data occurs especially in cases where either there is no historical data on the operation of flights, or historical data do not have current significance, e.g. the operation of the flight has been interrupted for a longer time. The results of a study carried out for Ostrava International Airport located in the Czech Republic, show that gravity models for which relevant data are not available can be fully replaced by multi-criteria evaluation methods.

This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.

* A risk situation is a type of uncertainty situation where events are likely to occur. In other words, risk is an estimated probability, and uncertainty is something that cannot be quantified. The greater the uncertainty when making a decision, the greater the degree of risk.

The Trans-Caspian International Transport Route (TITR) is one of the main transport corridors between China and Europe. It passes through the territory of Kazakhstan, as well as the countries of the South Caucasus, Turkey, and Iran, and contributes to the full realisation of transit and export opportunities of these countries. Each country participating in the development of TITR is implementing projects to increase the capacity of its transit infrastructure, but the potential of this route has not yet been fully realised. The reason for this is both high tariffs for sea and rail transport and insufficiently short transport times, which are dependent on the parameters and methods of using the infrastructure. This paper is devoted to the study of the TITR railway infrastructure located in Kazakhstan. The developed simulation model allows estimating the impact of infrastructure parameters of the railway track on the movement time of a container train from the PRC border to the ports of Aktau and Kuryk on the Caspian Sea coast.

This paper presents a study of a 32×10 Gbps Radio over Fiber and Wavelength Division Multiplexing (RoF-WDM) full-duplex system that uses Phase Modulators and a Dual-Port Mach-Zehnder Modulator (PMs-DPMZM) for bidirectional data transfer. The system employs Millimeter-Wave (MMW) signaling over optical fiber and focuses on selecting a technology that provides high transmission capacity per wavelength, improved spectral efficiency, and resistance against optical transmission impairments. The proposed method was validated using simulation results to confirm the efficiency of the proposed system in generating a 40 GHz signal and efficiently detecting and modulating the RF signals. The results demonstrate that the system exhibits strong resistance against dispersion, non-linear effects, and noise, delivering satisfactory performance for distances of up to 220 km. By analyzing the input power, the paper establishes a relationship between input power and signal quality, revealing that an optimal power of 0 dBm leads to an improved Quality Factor (QF) and reduced transmission errors. Furthermore, the evaluation of received optical power indicates the power level required to maintain an acceptable error rate, approximately -20.9690 dBm for downstream data transfer and -20.7245 dBm for upstream data transfer at the BER limit. The simulation performance also demonstrates the transmission efficiency achieved through a high Polarization Mode Dispersion (PMD) coefficient of up to 0.8. The analytical calculations conducted in this work provide valuable insights for optimizing and enhancing the performance of RoF-WDM networks.

Due to rapid population growth, traffic congestion has become one of the major issues in urban areas. The utilization of technology may help to address this issue. This paper proposes a new Multi-head Self-attention Vision Transformer (MSViT) based macroscopic approach, for road traffic congestion classification. To evaluate this approach, we use the UCSD (University of California San Diego) dataset that includes different weather conditions (clear, overcast and rainy) and different traffic scenarios (light, medium and heavy). The classification accuracy reached a high level of 99.76% with this dataset and 99.37% when night-mode frames are added to it. The proposed MSViT based method outperforms the state-of-the-art macroscopic and microscopic methods that have been evaluated using the same UCSD dataset, which makes it an efficient solution for traffic congestion prediction.

Road traffic in metropolitan cities is increasing at enormous rates resulting in congestion. Vehicles rendering emergency services like ambulances, fire engines, law enforcement vehicles, etc., act as lifelines and should be looked into with the highest priorities on the road. Such emergency vehicles (EmVs / EVs) are seen stuck many times in traffic, especially during peak hours of the day. The vehicles that block the EmVs on the road are unaware of the arrival of the same. Hence this work proposes a system that uses a central server that receives the location of the EmV and shares it with the civilian vehicles around. This is achieved through pinpointed accuracy systems like the Indian Regional Navigation Satellite System (IRNSS), Global Positioning System (GPS), and Global System for Mobile Communication (GSM), etc., The objective here is to help the EmVs reach their target location earlier thus saving lives.

The development of movable bridges is a modern technological advancement that requires the integration of multi-disciplinary concepts such as control, automation, and design. In order to demonstrate how a collision avoidance system works, this research offers the design of an automated double-leaf bascule bridge with a pulley-rope moving mechanism and Pratt truss. The bridge is created where a settled railway or roadway intersection of a navigable stream cannot attain a steep profile. The design took into account the length, height, and width of the bridge as well as automatic ship identification and scaffold leaf movement activity for ship crossing. For the safe passage of cars on the bridge and for stopping vehicles during bridge movement, the system includes a collision avoidance control system and an automated road barrier system. After sensor adjustment, the model bridge’s performance under test produced results that were adequate, with a success rate of 100%.

In the realm of gas turbine engine technology, turbine blades are persistently subjected to extreme thermal conditions due to prolonged exposure to high-temperature gases. Given this operating environment, devising effective cooling strategies is paramount for ensuring the turbine’s safety and longevity. Despite the critical nature of this issue, existing literature scarcely addresses cooling channels that incorporate factors like variable cross-sectional attributes and rotational effects. In this study, Computational Fluid Dynamics (CFD) and numerical methodologies were employed to analyze the flow mechanism and performance of three distinct cooling channels characterized by variable cross-sectional features. The results indicate a marginal thermal performance improvement of up to 3% for U-shaped cooling channels, specifically at a Reynolds number of 20,000, in comparison to Net cooling channels. Conversely, jet impingement channels outperform U-shaped channels by an impressive margin of over 11%. Additionally, at a Reynolds number of 60,000, jet impingement cooling channels manifest a significant upsurge of nearly 25% in the ratio between average Nusselt number, and the reference Nusselt number when compared with U-shaped and Net cooling channels.

The adoption of the Voice over Internet Protocol (VoIP) system is growing due to several factors, including its meagre rate and the numerous contours that can be joined with VoIP systems. However, the wasteful utilisation of the computer network is an inevitable problem that limits the rapid growth of VoIP systems. The essential explanation behind this wasteful utilisation of the computer network bandwidth (BW) is the considerable preamble length of the VoIP packet. In this study, we invent a technique that addresses the considerable preamble length of the VoIP packet. The designed technique is known as the manikin voice frame (MVF). The primary idea of the MVF technique is to utilise the VoIP packet preamble tuples that are not essential to the voice calls, particularly client-to-client calls (voice calls between only two users). Specifically, these tuples will be utilised for reserving the data of the VoIP packet. In certain instances, this will make the VoIP packet data manikin or even make it empty. The performance assessment of the introduced MVF technique demonstrated that the utilisation of the computer network BW has enhanced by 33%. Along these lines, the MVF technique indicates potential progress in resolving the inefficient usage of the computer network BW.

This paper aims to provide a PESTLE (Political, Economic, Social, Technological, Legal and Environmental) analysis of current trends and changes in the urban goods transport market. The goal is to assess the macro-environmental factors that are affecting innovative solutions for last-mile delivery, and to use this analysis to support decision making and planning for horizontal schemes of collaboration and policy interventions that may be necessary for introducing these solutions successfully and becoming more competitive in practice. By carefully prioritizing these factors, relevant stakeholders can make informed decisions and take effective actions to improve the competitiveness of their operations in the urban goods transport industry.

Background: Thailand’s logistics costs significantly exceed global averages. This study examines the novel application of a triple-helix model in factory logistics improvement projects in Northern Thailand from 2022 to 2024, addressing industrial logistics inefficiencies. Methods: The project involved 30 factories across various sectors, employing a case study methodology to analyze improvement initiatives in transportation, warehouse/inventory management, and logistics administration. Results: Transportation management interventions yielded up to 25% cost savings and improved delivery performance. Warehouse and inventory management upgrades led to inventory cost reductions of up to 55%. Logistics administrative improvements resulted in up to 20% cost savings and enhanced planning capabilities. Conclusions: The project demonstrates the effectiveness of the triple-helix model in facilitating knowledge transfer and practical improvements in industrial logistics. These findings are valuable for Thai industries, policymakers, and logistics managers globally, offering a blueprint for similar initiatives in developing economies. The success of this approach provides practical insights for optimizing logistics operations, potentially benefiting supply chain stakeholders, economic planners, and researchers seeking to enhance logistics efficiency and reduce costs in various industrial contexts.

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products’ availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena.

Background: Milkrun-based material supply plays an important role in the automotive industry, as it is a material supply concept where high efficiency can be achieved. When implementing milkrun-based material supply, the milkrun supply of the production plant often has to be integrated with an existing warehouse material handling system, which frequently leads to a less efficient solution. Methods: In this paper, the author investigates the impact of a hub-based milkrun supply, where the collection processes in the component’s warehouse and the distribution processes in the assembly plant are connected to a hub, which is responsible for the sequencing of component demands. After a systematic literature review, the paper introduces a novel mathematical model, which makes it possible to describe the conventional milkrun-based solutions, the hub-based milkrun solutions, and to compare them in terms of the length of transportation routes, transportation time, total service time, and virtual emission points of view. Results: The scenario analysis demonstrates that the hub-based solution can lead to an efficiency improvement of about 13% in total service time, 23% savings in transportation time, and 45% savings in transportation time in the component’s warehouse. Conclusions: The article’s findings suggest that implementing a hub-based milkrun system in automotive material supply can significantly enhance efficiency. The described approach could lead to more streamlined operations in production plants by optimizing the integration of milkrun systems.

Background: Traditional procurement and price negotiation methods in public hospitals in Thailand involve group purchasing agreements and bulk orders, posing challenges to improving inventory management efficiency. The vendor-managed inventory (VMI) model is a promising alternative for enhancing hospital performance, especially during crises. This study aimed to assess the potential cost savings in implementing VMI in a large general hospital in Thailand. Methods: A simulation modeling approach was used to compare the current inventory system with three VMI models: VMI1, focused on improving inventory turnover rate (ITR); VMI2, emphasized frequent replenishment with a 1-month supply; and VMI3, eliminated safety stock. Results: The results demonstrated significant cost savings, with potential reductions in total inventory management expenses. Specifically, VMI1 improved ITR from 6.31 to 7.76, reducing average inventory by 36% and cutting management costs by 40%. VMI2, with an ITR of 12.80, reduced inventory by 44% and saved 47% in management costs, while VMI3 achieved a 70% reduction in inventory and a 69% saving in management costs. Conclusions: This study highlights the VMI’s transformative potential in hospital inventory management, demonstrating significant cost savings. However, in the public sector, the feasibility of procurement regulations requires further exploration.

Background: Supply chain management (SCM) plays a fundamental role in the progress and success of organizations and has continuously evolved to better adapt to today’s complex business environments. Consequently, the issue of supplier evaluation and selection (SES), which is one of the most critical decisions in SCM, has gained special significance and has been examined from various perspectives. The concept of green and resilient (gresilient) SCM has emerged in response to recent concerns about environmentally friendly production and operations, as well as organizations’ ability to cope with crises and disasters. In the rapidly growing construction industry, applying gresilient principles can ensure green operations and help overcome future challenges. Methods: This study focuses on gresilient SES in a real-world construction case study, proposing a streamlined FUCOM (S-FUCOM) approach. The proposed method streamlines traditional FUCOM processes to solve decision-making problems in deterministic and uncertain environments. Several numerical examples are provided to illustrate its applicability. Results: the case study results identify air emissions, environmental management systems, and restorative capacity as the most critical gresilient SES criteria. Conclusions: The third supplier emerged as the top performer based on decision-making indicators. Finally, a sensitivity analysis was conducted across 20 scenarios, demonstrating that S-FUCOM is robust and provides stable results.

Background: We focus on the inland rail forwarding of import containers from a marine terminal. Specifically, we present a discrete-event simulation study related to container-loading operations by train, evaluating different train departure policies within a predetermined schedule based on the capacity of the rail network. The aim is threefold: manage more containers shipped by train, improve terminal operational efficiency, and increase the rail share. Methods: The proposed procedures take full advantage of the digitization and visualization of data currently present in the terminal with the aim of improving the performance indices of interest, thereby increasing the terminal‘s operational efficiency. We evaluate the medium- and long-term impact of alternative strategies on container dwell times and the possible increase in the number of containers shipped by train. Results: The computational tests are performed with data from a terminal in the port of Genoa (Italy). The results show the relationships between train departure management policies and train departure distributions. The number of departing trains, cancelled trains, and trains departing with load percentages below 70% is studied. Average loads per train and estimated delays are also analyzed. Conclusions: It is noted that the results, which can be obtained with data from any terminal, are of great importance for optimizing operational management, offering practical solutions to improve efficiency and reduce container downtime.

Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance.

From the perspectives of users, operators, and the community, decision making for public transportation is regarded as one of the most crucial characteristics to assess quality, ease and preferable. Studies on the decision-making for public transportation have been published. Comprehensive and quantitative investigations are lacking, nevertheless. Additionally, the Scopus database contains very few assessments of public transport integration. The current evaluation was carried out to provide scholars and practitioners with the most recent updates of papers on public transport and decision-making over the last 18 years (between 2005 and 2023), to allay their worries and address their inquiries about better public transport systems. The purpose of this bibliometric analysis and the overview is to encourage future research and collaboration by illuminating the idea of transport decision making studies, highlighting the paucity of relevant works, and providing a summary of the most productive nations, journals, and institutions in the field. A bibliometric analysis of the publications on public transportation and decision-making obtained from the Scopus database was carried out using the VOSviewer software. This allowed researchers to trace the historical and annual records of publications related to decision making covering the top countries, institutions, journals, most frequently used keywords, authors, citation network analysis, and most cited publications. The analysis’s findings identify the indications and interesting issues spots as well as the publications’ remarkable annual rise in decision-making on public transportation systems.

The paper proposes a new method for finding the optimal design of complementary split ring resonator cells for a microstrip antenna, which makes it possible to purposefully optimize the parameters of resonator cells, which significantly improve the electrical characteristics of the antenna. This antenna is designed for cellular communication 5G. The proposed method combines a regression model using a deep network based on fully connected neural layers with a search based on the conditional optimization algorithm COBYLA to find the optimal design parameters of resonator cells. This approach fully automates the process of creating microstrip antennas with resonator cells based on metamaterials and makes it possible to find the parameters of complementary split ring resonator cells that provide the optimal operating mode as a whole and gives the maximum level of antenna radiation while maintaining the bandwidth. The small size of complementary split ring resonator cells opens up the possibility of using this peculiar configuration to create compact and efficient microstrip antennas.

The concept of Aviation Technical Support as a Service (ATSaaS) has emerged as a new and innovative approach in the aviation industry, aiming to provide comprehensive technical support services to small airlines. The paper explores the development and implementation of this model, combining state-of-the-art digitalization methods and advanced technologies to address the unique challenges faced by small airlines. The research focuses on the customization and scalability of the service model, considering the diverse needs and requirements of small airlines in terms of aircraft maintenance and technical operations. By leveraging advanced digitalization methods, such as artificial intelligence, machine learning, and data analytics, the model aims to optimize decision-making processes, enhance operational efficiency, and improve overall safety and reliability. The article highlights the research novelty in terms of integrating advanced digitalization methods, developing a customizable and scalable service model, and optimizing performance metrics. It explores the practical significance of the ATSaaS model by providing guidance for small airlines to access high-quality technical support services, make informed decisions, and achieve business growth and profitability. Through a comprehensive literature review, the article examines the existing models of aviation technical support services and identifies the gaps that the ATSaaS model aims to address. The study also emphasizes the importance of collaboration between the service provider and the airline, promoting a collaborative ecosystem that fosters effective communication, information sharing, and mutual benefits.

In this study a problematic of logistics service provider selection is discussed. Traditional decision-making techniques are functionally limited and must be adjusted. Modern approach must be based on contemporary tools and information. The aim of this study is to develop a modern, industry-oriented decision-making model for selection of strategic logistics service providers. Main tasks of this study include: to perform critical analysis and comparison of multiple-criteria decision-making methods; to adapt decision-making framework for specific needs of selection of third-party logistics service providers; to collect and analyze industry data related to procurement projects; to develop modern decision-making model for selection of logistics service providers; to perform practical application of developed decision-making model based on collected industry data. The key novelties of this study are developed methodology for collection of industry data for decision-making process, developed hybrid decision-making model for selection of third-party logistics service provider, as well as application of the model. As the result, there is developed decision-making model for selection of 3PL service providers that includes 6 steps.

In the evolving domain of urban mobility systems, the integration of technology with user-centric strategies is pivotal. This research stands on the foundational concept of Mobility-as-a-Service, a user-centric intelligent mobility management distribution system that seeks to prioritize human needs over mere technological infrastructure. The study delves deep into the wealth of data available through mobile sensing technologies, highlighting the unprecedented understanding it offers into human mobility patterns, thus facilitating personalized route recommendations.

Drivers play a significant role in causing serious accidents, which underscores the need for further investigating the human element in order to improve road safety. Given the predominance of the information processing approach in driver’s behavior research field, an important psychological construct, Mental Workload (MWL), has been introduced to study the behavior of drivers. The objective of this paper is to investigate the impact of increased MWL on driver behavior and specifically the changes in driver’s Reaction Time (RT) under increased MWL. The experiment conducted in the driving simulator of the Hellenic Institute of Transport which is part of the Centre for Research and Technology Hellas, with the participation of 56 subjects from all age groups. For the simulation of the increased MWL conditions during driving, a secondary task was employed. To this end, the MIT AgeLab Delayed Digit Recall Task in the 1-back version was adapted for the needs of the present research. The driving scenario included 4 unexpected events, which further increase driver’s MWL. Driving performance was observed and relative parameters were measured as RT on the unexpected events, accidents occurred, and maneuvers performed. Appropriate statistical analysis was performed to examine the difference in the drivers’ RT in the unexpected events. Results demonstrated that higher MWL increased drivers’ RT in the majority of the participants. Furthermore, results also indicated a number of participants that probably employed adaptive control behaviors to counterbalance the increased MWL. Overall, variance on MWL proved to play an important role on driver performance, and thus further research on its consequences on driving performance, and the factors that influence its variance during driving, is imperative.

The subject of this paper is the AvatarTraffic simulator – a computer system capable of modelling in real time environments such as subway stations or airport halls populated with tens or hundreds of moving figures, which, in addition to pedestrian traffic typical for this type of objects, can perform predefined sequences of events and actions formulated as a simulation scenario. Thanks to the integration with a real monitoring system, the simulator, in addition to providing data streams (including video) generated by the virtual scene, is also able to dynamically respond to actions taken by the system’s staff. Using the Unity simulation engine as the implementation platform, a number of practical problems had to be solved during the development, two of which are the subject of this article: a) supervising and correcting the work of AI algorithms used in Unity to simulate the pedestrian movement of avatars, and b) a textual description of the scenario of events taking place on the stage in a way editable for experts planning tests of the monitoring system. Some more challenging cases of people movement are discussed (including creating queues and passing through doors) and the paper presents original algorithms correcting the work of the Unity’s built-in methods in the situations when the coordinated behaviour of people groups is required. Because of the specifics of the simulator environment the scenario needed to be expressed in a JSON text file, and the article presents the implemented mechanisms of its compilation directly to the C# runtime environment and discusses the original command language which was created to model sequences of events and actions making up the scenario.

Methods for improving the characteristics of the reliability message transmission in multiservice telecommunication networks in the provision of multimedia services are analyzed. The noise immunity reception, effective methods modulation, paths transmission systems, indicators of information, frequency and energy efficiency of the system, methods of processing and receiving messages are considered. The problems of optimizing signal-code constructions according to the criterion of energy efficiency of a telecommunications system are studied. A new approach to the construction of methods for calculating the indicators of noise immunity of receiving messages is proposed, taking into account signal-code structures. The developed calculation method takes into account effective types of modulation, demodulator synthesis algorithms, signal processing methods, bandwidth, signal-to-noise ratio and energy efficiency indicators. On the basis of the calculation method, important analytical expressions were obtained that evaluate the characteristics of the noise immunity reception, taking into account the indicators energy efficiency, throughput per sample, the conditions for the indicators of the signal-to-noise ratio and the probability erroneous reception discrete signals.

The utilization of simulation modelling in supply chain management has proven to be a valuable tool for logistics service providers, enabling them to optimize their operations and meet customer demands efficiently. AnyLogic, with its diverse capabilities in discrete event simulation, system dynamics, and agent-based simulation, stands out as a powerful software solution for tackling complex supply chain challenges. This paper significantly contributes by demonstrating the practical implementation of AnyLogic in the modelling and simulation of agro-food product distribution network, particularly vegetable oils from the Cevital group. Additionally, the study integrates an optimization experiment to ascertain the optimal number of vehicles required to meet a specific customer demand while ensuring a high level of fleet utilization. The obtained results are very encouraging and demonstrate the feasibility and effectiveness of our approach.

Urban logistics plays a crucial role in modern society by covering all the flows of goods and services in the transportation world. This study aims to compare different delivery distribution scenarios using the aggregation of stops by grouping entities, for instance, the postal code approach (PCA) and the volume-based approach (VBA), to indicate the most effective one in simplifying urban logistic operations. These two scenarios illustrate two aggregation criteria: geographical, which groups stops with those closest ones, and non-geographical, which tends to cluster stops with similar stops. Used stops came from a real-world dataset acquired from urban logistics operator in the East of Rome. This study uses an optimizing algorithm called Traveling Salesman Problem (TSP) and Google Matrix API to calculate the shortest path and travel time. A comparison of those two approaches has been made to illustrate the similarities and differences in CO2 emissions, travel length, travel time, and unloading time. Although PCA is influenced by demand level, results show that PCA leads to a shorter travel time, shorter travel length, and less emissions produced. Furthermore, VBA is a more heterogenous distribution while PCA contains more homogeneity. The outcome could have the potential for companies and researchers interested in urban logistics due to the proposal of a new way of making distributions, real-world data usage, and comparing different scenarios.

Correct signage belongs to critical factors affecting road traffic safety. During a naturalistic driving study in Poland major mistakes associated with signage were noticed and analysed: road works were announced and speed limits were reduced from 120 km/h to 70−80 km/h, but no road construction could be found. Measurements of drivers’ speed showed their complete disregard of the limitations: average speed of light vehicles remained at 113 km/h and lorries at 88 km/h. Assessment of the encountered 683 road signs revealed 12.5% of them to be inadequate (10.9% falsely informed drivers). Evaluation of contrast ratio of road markings (their replacement was the claimed reason for road work) showed low values both before and after re-placement (Weber contrast 0.6). It is hypothesised that erroneous road signage and its contradiction with the visible road features are contributing to the permanent disobedience of traffic rules in Poland and are decreasing road safety.

The multi-objective optimization for voyage planning, which aims to balance time, fuel utilization, and all other relevant service costs, is noticeably absent in the scientific literature. The reason behind this gap is closely linked to the involvement of a large number of participants with conflicting interests in the process. This complexity explains why modelling an optimization tool for maritime logistics services appears challenging. Although new technologies such as blockchains and smart contracts have helped reduce the number of participants and address some complexities, they cannot fully resolve the fragmentation observed in maritime transport.

Background: Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate uncertainty during the development and execution of appointment schedules. Further, the situation is complicated by disruptions in the arrival of external trucks (ETs) during transport, which results in congestion at the port due to unbalanced arrivals. In the wake of Industry 4.0, simulation can be used to test and investigate the present CT configurations for possible improvements. Methods: This paper presents a simulation optimization (SO) and simulation-based optimization (SBO) iteration framework which adopts a dual transactions approach to minimize the gate operation costs and establish the relationship between productivity and service time while considering congestion in the yard area. It integrates the use of both the developed discrete event simulation (DES) and a mixed integer programming (MIP) model from the literature to iteratively generate an improved schedule. The key performance indicators considered include the truck turnaround time (TTT) and the average time the trucks spend at each yard block (YB). The proposed approach was verified using input parameters from the literature. Results: The findings from the SO experiments indicate that, at most, two gates were required to be opened at each time window (TW), yielding an average minimum operating cost of USD 335.31. Meanwhile, results from the SBO iteration experiment indicate an inverse relationship between productivity factor (PF) values and yard crane (YC) service time. Conclusions: Overall, the findings provided an informed understanding of the need for dynamic scheduling of available resources in the yard to cut down on the gate operating costs. Further, the presented two methodologies can be incorporated with Industry 4.0 technologies to design digital twins for use in conventional CT by planners at an operational level as a decision-support tool.

Background: Various innovations have been proposed to improve the efficiency and sustainability of last-mile delivery in urban and rural environments. Notable examples of innovative delivery solutions are parcel lockers, cargo bicycles, crowdsourced delivery, and so on. Methods: This study contributes to the ongoing research by developing a large-scale digital model of England and Wales to evaluate a new generation of solutions for last-mile delivery challenges being faced in both rural areas and cities. The two innovations chosen for comparison in this study are (i) carrier consolidation and (ii) alternative delivery locations (i.e., delivery to the workplace instead of the home). As well as the effect on any individual locations, the digital model evaluates both the benefits for England and Wales as a whole. Furthermore, the influence of the market share on the results, as well as the effect of changing the number of depots, is assessed. Results: By delivering to the customer’s workplace instead of the home, the vehicle kilometres travelled (VKT) reduce slightly (less than 10%). Carrier consolidation shows significant potential in reducing the overall VKT (up to 53%). When looking at individual areas in isolation, the consolidation option reduces the VKT or changes it up and down all within tolerance. Naturally, the first option causes a significant shift in delivery activity across England and Wales. Areas of central London would see in-excess of a 10-fold increase in the number of parcels delivered, whereas the demand for parcels in rural areas is further, and significantly reduced. Conclusions: This study highlights the importance of large-scale and detailed digital models that not only calculate the overall benefits of an innovation but also their effect on each individual area.

Background: This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a “what-if” analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning.

Background: An alternative to unsustainable urban developments, rural living is on the rise, but it already has its challenges. To that end, rural logistics and transportation (RLT) calls for a comprehensive analysis of its context, especially in a climate-changed and socially turbulent world. Unlike urban logistics, there is limited focus on RLT in academic literature. However, rural areas’ lack of transportation and limited logistics operations negatively affect rural residents’ daily lives, especially socially disadvantaged groups such as older people, children, women, and low-income households. Methods: This study first identifies the key literature on RLT and sustainability using a systematic literature review. Then, it synthesizes from the extant literature the challenges in RLT and proposed solutions to understand how to improve accessibility and address some barriers to implementation, all through the perspective of quadruple bottom line (QBL) sustainability pillars. Results: The lack of opportunities presented to rural residents due to limited RLT leads to inequality between rural and urban populations, requiring academic attention. Moreover, despite the growing emphasis on sustainability in academic literature, there is a noticeable lack of attention to sustainability in RLT. Conclusions: This study leads policymakers toward a better understanding of rural communities’ complexities, directs practitioners to adopt the QBL perspective in decision-making, and aims to stipulate innovative RLT topics for further research for academicians.

Background: The corn supply chain is vital for food security and economic stability regionally and globally. This study integrates sustainable supply chain management with location optimization to address trade-offs from climate change, economic viability, and environmental impact while assuming the constant social obligation inherent in the supply chain structure. Methods: This study employs a mixed-integer programming (MIP) framework to optimize facility locations in North Dakota, including corn production zones as suppliers and ethanol plants as consumers. Primary objectives include cost minimization and greenhouse gas reduction, enabling the prioritization of economic or environmental goals as per organizational strategies and regulations. This approach ultimately maximizes resource utilization by ensuring efficient production and distribution practices. Results: The case study results highlight the optimal selection of 20 out of 30 corn production zones to meet statewide ethanol plant demand efficiently. Using compressed natural gas (CNG) instead of diesel could potentially save USD 2 million annually and cut carbon emissions by up to 1148 thousand tons per year, demonstrating meaningful progress toward economic and environmental sustainability within the supply network. Conclusions: The presented work offers a systematic methodology for designing sustainable supply chains for various agricultural products, aligning with the broader goal of promoting sustainability and resilience for efficient agricultural production and distribution systems.

Background: Sustainability in food supply chains is desired for production, logistics, and waste management. However, food supply chains (SCs) have complex systems that differ from other SCs. Managing such complexities is challenging for small and medium-sized enterprises (SMEs) due to their heightened constraints, such as limited resources and inadequate awareness. Despite the challenges, there is fragmented research, understanding, and approaches to assist SMEs. SC designs need to be adapted so that SMEs can navigate the challenges and avert high wastage. The main objective of this research was to identify the practices influencing sustainable SC designs in a developing country and create a simulation model to illustrate the potential benefits and challenges of promoting sustainability in the banana SC in Malawi. Methods: Mixed-methods research was utilised, employing a literature review, participant interviews, observations, and survey data collected from 353 participants from three districts in Malawi to gain insight into the banana SC problem, establish objectives, and develop a simulation model complemented by design science research. Results: The research identified awareness, collaboration, efficiency, governance, knowledge sharing, and resilience as sustainability practices in the case study banana SC, thus forming a sustainability model. Simulation results showed improvements in key performance indicators like shelf-life, lead-time, quality, throughput, and waste through SC operation reorganisation. Conclusions: Sustainable models must be tailored to the specific challenges inherent in developing food supply chains in developing nations. The development of the models has significant managerial implications, notably enhancing strategic planning, operational efficiency, risk management, alignment of sustainability goals, performance monitoring, stakeholder engagement, and resource optimisation.