Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT’s capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT’s role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT’s capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI’s potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for “brainstorming” in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT’s effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.

This study proposes a structured conceptual model for analyzing stakeholder complexity within Hungary’s newly implemented Deposit Refund System (DRS), using the Quality Function Deployment (QFD) framework. A House of Quality (HoQ) matrix was developed to map the relationships between the functional features of the system and the specific requirements of key stakeholders, including government, system operators, manufacturers, retailers, and customers. A qualitative focus group method was applied to gather expert input and evaluate system features based on stakeholder experience and the evolving Hungarian context. Rather than aiming for statistical generalization, the study focuses on illustrating how structured modeling can reveal stakeholder trade-offs and support strategic alignment in complex waste collection systems. Findings highlight areas of convergence and divergence among stakeholders and demonstrate the utility of the QFD-based approach in designing adaptable, stakeholder-informed DRS models. The results are particularly relevant for countries in the early stages of DRS implementation or those seeking to redesign existing systems to better integrate technical feasibility and stakeholder priorities.

Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique—directional epsilon neighborhood clustering (DENC)—is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management.

This study introduces an adaptive parameterization framework for the Bacterial Memetic Algorithm (BMA), tailored to storage location assignment problems (SLAP) in warehouse environments. The investigation focused on a warehouse layout handling fast-moving consumer goods (FMCG) within a picker-to-parts system. Building on the previous findings that highlighted the limitations of static parameterization and the advantages of dynamic control in evolutionary algorithms, the research was extended by formulating problem-dependent rules that guide self-adaptive behaviour during execution. Specific algorithmic response patterns were identified along performance metrics - such as the improvement rates of local and global search modules and the evolution of the objective function - to define adaptive control rules. These rules, in the next phase of the research, will be integrated into the algorithm to dynamically adjust its parameterization during execution. This dynamic parameter control enables more efficient execution and faster convergence, contributing to flexible and effective warehouse operations. Although the rule set was developed for a specific layout, the methodology is applicable to other combinatorial optimization problems as well. The current work provides a foundation for future research on machine learning-enhanced parameter prediction and further refinement of adaptive strategies in evolutionary warehouse optimization.

Corrosion is a leading cause of pipeline failures, responsible for up to a third of reported incidents. Given the extensive length and limited accessibility of pipelines, operators rely on frequent in-line inspections (ILIs) to detect and quantify corrosion defects. Feature matching between successive ILIs is therefore utilized to align and compare this data, enabling the identification of corrosion evolution and behavior. However, measurement uncertainties, nonuniform corrosion growth, and spatial interactions between adjacent defects pose significant challenges in achieving accurate matching. This study proposes a two-phase feature matching model designed to address these limitations. The first phase performs Iterative Closest Point (ICP) alignment with a context-aware nearest neighbor selection strategy based on Directional Epsilon Neighborhood Clustering (DENC) to isolate stable feature pairs and minimize ambiguous associations in densely clustered defect regions. The second phase applies a novel proximity–overlap-informed correspondence optimization using linear programming to identify matches and outliers by jointly considering feature positioning and geometric attributes. The model’s effectiveness is evaluated on a 1116 m subsea pipeline segment involving two consecutive inspections reporting 1305 and 1491 features, respectively. Compared to three state-of-the-art models, the proposed approach achieves a recall, precision, and F1 score of 99.2%, demonstrating substantial improvements in accuracy, stability, and robustness to inspection and corrosion-related uncertainties. These results confirm the model’s ability to address critical limitations in existing approaches and underscore its potential to enhance pipeline integrity assessments.

This paper tackles the logistics dilemma of how to meet customer expectations while at the same time respecting the internal processes and financial interests of the company and ensuring long-term sustainability. In this paper, integrated Quality Function Deployment (QFD) and Balanced Scorecard (BSC) techniques developed a method for the structured planning of logistics strategies. BSC, combined with QFD, gives the opportunity not only to “translate” the voice of the customer but also to focus on the company’s interests from four perspectives. For example, for products, we evaluated the interactions between different expectations, and the focus was on the disputes that arise during the expectations. The result of this paper is that Extended QFD provides a new method to formulate the various requirements. This method is suitable for creating a sustainable logistics strategy.

Routing optimization nowadays is an important and popular topic, it plays a key role in the transport and logistics industry. In this paper we present a software tool for solving the Traveling Salesman Problem and related non-fuzzy and fuzzy optimization problems. For this purpose the Discrete Bacterial Memetic Evolutionary Algorithm (DBMEA) was implemented in this software which is a proven efficient method for handling the examined type of optimization problems.

In this paper, we address the challenge of creating candidate sets for large-scale Traveling Salesman Problem (TSP) instances, where choosing a subset of edges is crucial for efficiency. Traditional methods for improving tours, such as local searches and heuristics, depend greatly on the quality of these candidate sets but often struggle in large-scale situations due to insufficient edge coverage or high time complexity. We present a new heuristic based on fuzzy clustering, designed to produce high-quality candidate sets with nearly linear time complexity. Thoroughly tested on benchmark instances, including VLSI and Euclidean types with up to 316,000 nodes, our method consistently outperforms traditional and current leading techniques for large TSPs. Our heuristic’s tours encompass nearly all edges of optimal or best-known solutions, and its candidate sets are significantly smaller than those produced with the POPMUSIC heuristic. This results in faster execution of subsequent improvement methods, such as Helsgaun’s Lin–Kernighan heuristic and evolutionary algorithms. This substantial enhancement in computation time and solution quality establishes our method as a promising approach for effectively solving large-scale TSP instances.

The hierarchy of semantic networks can also be observed in the functioning of economic systems. There are uncertainties in semantic networks, meaning that the classification of different attributes is not always clear. The same uncertainty is also present, for example, in the design of logistics strategies as a sub-strategy of the economy, which can lead to inconsistencies between the parameters of the system. It is important for a system to be resilient to both internal and external influences, and it is, therefore, necessary to develop a hierarchy of system parameters based on the semantic network's method and to examine the relationship between parameters in order to achieve resilience and long-term sustainability.

Cognitive biases often occur even in the decision- making process of highly qualified company managers due to the drive for efficiency and time pressure in operations. At the same time, there are also long-term strategic decisions where time pressure is no longer a factor, and yet cognitive bias appears, which has to be considered properly. In strategic issues, decision- makers tend to see their wishes and desires rather than the objective reality. The proposed system of fuzzy indicators based on technical and objective data supports decision-making between logistics strategies by mitigating cognitive biases, which is extremely important in the logistics field, where the decisions have to be made partly based on subjective, vague, or uncertain parameters.

The goal of this article is to examine traffic education and its examination system, using a new approach based on the House of Quality method. While every country has its own legal rules and requirements regarding how traffic education and examinations are conducted, there is a direct relationship between traffic education, its examination system and road safety. Therefore the quality of such a complicated process is of great interest for both stakeholders: the authorities and the citizens. These stakeholders both have their own objectives regarding the system, consequently increasing its complexity. This article investigates, as its case study, the system in Hungary. The House of Quality method has been expanded to provide a unique approach to examine the goals and objectives of both stakeholders, revealing similarities and differences and their interrelationships. Secondary data on the effectiveness of the traffic education and examination system are also analysed. Based on the HOQ model representations of the goals and objectives of the stakeholders regarding the traffic education and exam system, it can be established that the stakeholder points of view are closer to each other in the case of the test system than that of the education system. However, there are unsolved contradictions between the stakeholders that have to be handled, as opinions regarding the quality of the service and the criteria of the stakeholders’ satisfaction are very diverse.

Simulation of non-stationary random vibrations has motivated Packaging vibration testing for decades. Often, an event-detection algorithm decomposes Road vehicle vibrations when analyzing the recorded series. However, heuristics and subjective justifications are often in the papers, whereby the foremost concern is the validation of the non-stationarity of simulated signals. Furthermore, if a changepoint detection is inherent to the procedure, it is recommended to calibrate the detector. The current paper concerns the Receiver operating characteristics (ROC) of two novel algorithms and provides contextual support by Segment length distributions (SLD).

On-road driving studies are essential for comprehending real-world driver behavior. This study investigates the use of eye-tracking (ET) technology in research on driver behavior and attention during Controlled Driving Studies (CDS). One significant challenge in these studies is accurately detecting when drivers divert their attention from crucial driving tasks. To tackle this issue, we present an improved method for analyzing raw gaze data, using a new algorithm for identifying ID tags called Binarized Area of Interest Tracking (BAIT). This technique improves the detection of incidents where the driver’s eyes are off the road through binarizing frames under different conditions and iteratively recognizing markers. It represents a significant improvement over traditional methods. The study shows that BAIT performs better than other software in identifying a driver’s focus on the windscreen and dashboard with higher accuracy. This study highlights the potential of our method to enhance the analysis of driver attention in real-world conditions, paving the way for future developments for application in naturalistic driving studies.

In-Vehicle Information Systems (IVIS) have evolved with the integration of advanced technologies like touchscreens, enhancing vehicle functionality and infotainment features. However, the development of sustainable and user-centric dashboard interfaces embracing the Bring-Your-Own-Device (BYOD) concept remains limited. This research aims to explore the usability, advantages, and disadvantages of the BYOD concept within the context of IVIS. Specifically, it investigated the control of the onboard air conditioning system and selected Advanced Driver Assistance System (ADAS) functions. To accomplish this, a complex simulation environment using Unity, Blender, and C# was developed. Eye-tracking technology was utilized to record participants' gaze patterns and attention allocation during experimental tasks. Following the simulation, participants provided subjective usability assessments of the system through questionnaires. The integration of a mobile phone with a suitable user interface as part of the BYOD concept generally led to enhanced usability and reduced distraction. This study underscores the potential benefits of integrating the BYOD concept into IVIS, emphasizing improved usability, sustainability, and user-friendliness. These findings hold significance for advancing the design of user-centric, sustainable interfaces in automotive technology.

Cognitive biases often appear in the decision-making process of highly qualified managers of companies because of the drive for efficiency and the time pressure in operation. There are also long-term strategic decisions where there is no longer time pressure, and yet cognitive bias appears, for example during the selection between the Push and Pull systems in logistics. The description of the actual situation has to be quantified because communication between human-machine systems, as defined in cognitive info-communication, is only viable if cognitive biases in decision-making can be considered properly. We propose fuzzy approach and measures that can assess whether the production uses a Push or Pull logistics strategy for a specific product or for the entire company.

One of the most significant corporate challenges today is to meet customer expectations.In order for customer satisfaction to be achieved, it is necessary to review the entire corporate system and related processes and coordinate the various corporate strategies.In the recent past, it was widely regarded as sufficient by managers to develop the right marketing strategy in order to sell a product.However, there is currently a discussion as to whether a marketing logistics strategy as a well-designed logistics environment is needed to sell the product and thereby gain customer satisfaction.In this article, we present the Quality Function Deployment (QFD) technique, an effective tool for transforming consumer needs into technical, quality characteristics.The method of QFD technique can also be successfully applied in the field of logistics.Utilizing it ensures the possibility of examining the impact of the sub-areas and processes of marketing and logistics services on the basis of customer needs.In addition, visual control can be used to illustrate that whilst two products require the same logistics strategy, lead times already cause significant differences in the interaction of logistics processes and technological parameters.The analysis also highlights the shortcomings of the logistics environment, thereby supporting the decision-making of the company management in both marketing and logistics strategy planning.

This paper investigates the usability of touch screens used in mass production road vehicles. Our goal is to provide a detailed comparison of conventional physical buttons and capacitive touch screens taking the human factor into account. The pilot test focuses on a specific Non-driving Related Task (NDRT): the control of the on-board climate system using a touch screen panel versus rotating knobs and push buttons. Psychological parameters, functionality, usability and, the ergonomics of In-Vehicle Information Systems (IVIS) were evaluated using a specific questionnaire, a system usability scale (SUS), workload assessment (NASA-TLX), and a physiological sensor system. The measurements are based on a wearable eye-tracker that provides fixation points of the driver’s gaze in order to detect distraction. The closed road used for the naturalistic driving study was provided by the ZalaZONE Test Track, Zalaegerszeg, Hungary. Objective and subjective results of the pilot study indicate that the control of touch screen panels causes higher visual, manual, and cognitive distraction than the use of physical buttons. The statistical analysis demonstrated that conventional techniques need to be complemented in order to better represent human behavior differences.

Research on the non-stationary nature of road vehicle vibrations (RVV) led to advances in simulating such processes. Contemporary methods introduced for the analysis of RVV primarily aimed at partitioning the signal in the time- or time − frequency domain, providing differing segments of a signal. However, a degree of dissimilarity, or conversely similarity, is still challenging to find. Hereunder we argue that in some cases, merely a statement of dissimilarity between neighbouring segments within a signal might be well-enough, though from a broader perspective, the assessment of the similarity of discrete Fourier transforms (DFT) may be the next practical step forward. For this reason, the current paper presents the hierarchical clustering of elements of the short-time Fourier transform (STFT) plane from an RVV measurement; secondly, it introduces a clustering validation metric to arrive at an optimum distance metric and a threshold to use in binary hierarchical clusters.