An event-based model and hybrid genetic search algorithm for an inland multi-size container transportation problem

ABSTRACT: In view of food safety regulation in logistics transportation, the participation of we-media in governance has been scarcely considered in the existing literature. To analyze the dynamic evolution and influence factors of strategies made by government, carrier and we-media, a tripartite evolutionary game model was constructed using Matlab for analysis and verification. The results manifest that the illegal operation behavior of carriers and the ineffective government regulation in the food safety transportation process are jointly influenced by the different strategic behaviors of the members, and the strategic choices of the participants have an impact on their own and each other's future decisions; to solve the problems in food safety transportation, the compliance transportation of carriers serves as the basis, the active participation of we-media as the key, and the strict government regulation as the guarantee. The conclusions of this study not only enrich the theoretical connotation of food safety transportation regulation but also provides innovative multi-party governance strategies in practice, expecting to promote the healthy development of the food safety transportation market. Keywords: Food safety regulation, Carrier, Government, We-media

Corrigendum to “A new effective heuristic for the Prisoner Transportation Problem”

Because they have become an integral part of our daily lives, large quantities of hazardous materials are produced and transported each year. In most industrial societies, life without hazardous materials has become almost unimaginable. Hazardous materials are defined as substances that, during transportation, have the potential to pose adverse effects or risks to public health, safety, or property due to their quantity or form. In this context, hazardous materials or products include explosives, gases, flammable and oxidizing substances, toxic and infectious materials, as well as radioactive, corrosive substances and their associated hazardous wastes. The safe transportation of hazardous materials is considered a comprehensive and multidimensional issue, influenced by various legal and physical factors, as well as the numerous risks that vehicles may encounter during transit. Increasing environmental awareness of the potential impacts of hazardous material accidents on public health has significantly heightened both academic and institutional interest in this field. This study proposes a risk-averse solution to the hazardous material transportation problem through a model developed by integrating the Tabu Search algorithm with a game theory–based approach. Within the model, the dispatcher aims to minimize the expected loss under the worst possible conditions in the event of a disruption in any link of the distribution network. In this framework, the expected cost determined through Nash equilibrium is evaluated as an effective and practical analytical tool for strategic decision-making in the selection of safe routes for hazardous material transportation.

Forecasting of travel demand has become increasingly important in the context of evolving mobility patterns and structural disruptions, including economic fluctuations and public health crises. Classical time series models, although well established in travel-demand analysis, are often limited in their ability to capture non-linear dependencies or adapt to abrupt regime shifts. This study develops and evaluates forecasting techniques drawn from both traditional statistical modeling and machine learning approaches. Their predictive performance and adaptability are benchmarked for U.S. outbound air travel demand across eight global destination regions, Europe, the Caribbean, Asia, South America, Central America, Oceania, the Middle East, and Africa, respectively. Using historical outbound passenger data, six forecasting models are constructed and assessed through multiple forecasting accuracy measures, including the Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Empirical results demonstrate that machine-learning-based models, particularly those incorporating adaptive learning components, consistently outperform conventional approaches in modeling structural changes in travel demand data. The study further contributes a generalizable methodological framework that enhances robustness under uncertainty and offers broad applicability to forecasting problems in transportation, tourism, and related domains.

Data-driven Hybrid Neural Ensembles-evolutionary Framework for Dynamic Multi-objective Green Transportation Problem

ABSTRACT Although several past studies have addressed sustainable transportation issues within the supply chain, many critical real-world factors such as time-dependent vehicle speeds, actual route distances, and load-dependent engine efficiency have not been sufficiently addressed. In order to address these limitations, present study proposes a sustainable transportation network that simultaneously optimizes economic, environmental, and social objectives. A capacitated vehicle routing and scheduling problem is formulated, incorporating heterogeneous vehicles, hard time windows, and multiple time periods. An improved Ant Colony Optimization algorithm is developed with 2-optimal local search for enhanced route quality and convergence. Pareto-optimal solutions are ranked using the technique for order preference by similarity to ideal solution. Partial rank correlation coefficient and regression analysis are conducted to interpret variable influence, offering insights into input-output relationships. A real-world case study from a rural logistics company demonstrates the practical applicability and effectiveness of the proposed framework in solving complex sustainable transportation problems.

A novel neutrosophic fuzzy programming model for multiobjective transportation problems with survival costs

The relocation of Indonesia’s Capital City (IKN) to East Kalimantan presents challenges in managing the linkage between land use and transportation systems. Changes in land use and the increase in motor vehicle ownership may potentially lead to transportation and environmental problems. This study aims to analyze the influence of transportation factors and regional dynamics on the number of motor vehicles in East Kalimantan. This research employs a quantitative approach using panel data regression based on secondary data from 2018–2023 at the regency/city level. The independent variables include road length, total and population growth, economic growth, and the area of built-up and non-built-up land, while the dependent variable is the number of motor vehicles. Model selection was conducted through panel data regression testing, and the results indicate that the Random Effect Model (REM) is the most appropriate model. Partially, only the built-up area (LCT) has a significant effect on the number of motor vehicles, while the other variables are not statistically significant. However, the simultaneous test (F-test) shows that all independent variables collectively have a significant effect on the number of motor vehicles, with a coefficient of determination (R² = 0.4473). These findings highlight that the growth of built-up areas plays a crucial role in increasing motor vehicle numbers in East Kalimantan. Therefore, the integration of spatial planning and transportation systems is essential to support sustainable IKN development.

Background The Transportation Problem (TP) is a detailed model in operations study with applications in logistics, supply chain management, and resource allocation. The classical IBFS methods including North-West Corner, Least Cost and Vogel’s Approximation have competitive computational efficiency, but they are very sensitive to the structure of the problem and usually lead to a solution that is far from the global optimum. Classic enhancement strategies like the Generalized Distribution (MODI) and Stepping-Stone (SS) approaches have low computational complexity but may fall into a local optimum quickly, which makes them ineffective in large-scale or unbalanced problems. Methods We propose the first generic hybrid algorithm, called Ester Hybrid Improvement for Transportation Problem (EHITP), which was developed with the aim of mitigating the shortcomings of traditional IBFS-based methods. To overcome the local minima problem, the proposed EHITP framework combines adaptive perturbation procedures and guided neighborhood search methodologies to broaden the solution space. Results Initial experiments on benchmark and synthetically created datasets show that EHITP obtains a much less total transportation cost relative to the classical IBFS and improved MODI/SS methods. These features lead to a more robust method, stable solutions over iterations, and convergence across a wider range of problem sizes and structures. Conclusions The findings show EHITP serves as a more reliable, scalable, and expense-effective solution to transportation issues. The balance this algorithm achieves between the quality of the solution it produces, and its computational efficiency makes it a potential candidate for real life applications in topics such as distribution chain and economic resource allocation.

The multi-objective multi-item transportation problem is a challenging issue in the context of supply chain management which deals with optimizing several conflicting objectives, considering the allocation of different products departing from many source nodes to multiple demand destinations. In this paper we propose a systematic mathematical approach based on linear programming to solve this challenging optimization problem. Based on these assumptions the study designs a multi-objective linear programming (MOLP) model with cost, delivery time and environment as the main objectives. The model is developed under clear-cut restrictions that consider supply avai lability, demand requirements, vehicle capacity and multi-product allocation rules. A practical example is considered with real operational data of a regional distribution network for optimal transportation planning and WinQSB software is used to find the best routes. Results show that the proposed model can effectively compromise conflicting multi-objectives, reducing total cost by 18.5%, delivery time by 12.3% and CO2 emissions by 15.2%. The research uses the weighted sum-constraint method for Pareto optimization based decisiontrade-offs, and results into a full tradeoffs analysis and possible transportation planning solutions to decision-making people.

The classical linear programming transportation problem of minimizing the cost of transportation between production and consumption points has many applications, one of which is the problem of efficient fire control. The article considers a modified formulation of this problem. It includes main and auxiliary batteries, each of which can fire a limited number of shots at targets with a given efficiency. Auxiliary batteries can fire only at predetermined targets. The goal is to distribute targets between batteries in such a way that the total efficiency of destruction is maximized. The decomposition method is used to solve the proposed problem. The original problem of large dimension is divided into many simpler one-dimensional and two-dimensional subproblems. At the first stage, the initial pseudo-solution is found as a set of solutions to these subproblems. If it is admissible for the original problem, then it is also optimal. Otherwise, an iterative process of sequentially coordinating the solutions of the subproblems is launched by cyclically recalculating the coefficients of the objective function in two dimensional problems. This process guarantees a monotonic approximation to the optimal solution. The article examines in detail possible cases arising during the algorithm operation, including a special degenerate case, for the resolution of which it is proposed to introduce additional constraints. The possibility of replacing inequality constraints with equality constraints for the main batteries within the framework of the decomposition approach without loss of generality is theoretically substantiated. The efficiency of the proposed algorithm is confirmed by the results of computational experiments. Approximation of the dependence of the running time on the problem dimension demonstrates the polynomial complexity of the method. The obtained results open up prospects for applying this approach to other non classical formulations of transport-type optimization problems.

The complex relationship between poverty, health-seeking behavior, and access to health services is critical to understanding health inequalities. This study aimed to deepen our understanding of this field by examining the relationships between poverty, health-seeking behavior, and access to health services with a mixed-methods approach. Between December 2023 and March 2024, a mixed-methods design (QUAN-QUAL) was employed using a convergent (sequential) approach. The quantitative phase adopted a cross-sectional research approach, while the qualitative part embraced a phenomenological approach. The quantitative stage involved 384 participants, and the qualitative stage involved 11 individuals. Quantitative data were collected using the Health Seeking Behavior Scale and the Healthcare Demand Procrastination Scale. In contrast, qualitative data were gathered through in-depth individual interviews using a semi-structured interview guide. As household income levels increase, health-seeking behavior (r=0.141, p<0.001) and healthcare demand procrastination (r=0.143, p<0.001; β=0.132, p=0.003) significantly increase. Health-seeking behavior rises among individuals who perceive their general health status as good (F=7.111, p<0.001; β=0.143, p=0.004). In contrast, low income levels (t=3.797, p<0.001; β=-0.147, p=0.004) and low education levels (F=4.791, p<0.001; β=-0.145, p=0.004) are significantly associated with decreases in health-seeking behavior. Procrastination of healthcare demand is more common among workers (β=0.176, p<0.001). On the other hand, higher income levels and the distribution of health expenditures appear to reduce this tendency (β=-0.121, p=0.015; β=-0.116, p=0.021). The synthesis of interviews revealed two main themes: utilization of health services, and access to health services and inequalities. Within the theme of "challenges in accessing health services," the qualitative thematic analysis highlighted key difficulties such as transportation problems, long waiting times, and insufficient personnel. Findings indicate that socioeconomic factors such as income level and education were determinants of health-seeking behavior and that healthcare demands procrastination behavior. Among the challenges encountered in accessing health services, transportation issues, long waiting times, and inadequate staff were prominent.

River-land multi-modal bulk cargo transportation problem with containerization

Traffic congestion is a persistent urban transportation problem that imposes substantial economic losses through travel delays, increased ‎fuel consumption, and reduced productivity. This study examines the relationship between traffic volume and economic loss resulting ‎from traffic delays along President Jose P. Laurel Highway, a major arterial corridor in Lipa City, Batangas. Using one week of peak-‎hour field observations, data were collected on vehicle volume, average delay per vehicle, and additional fuel consumption. Economic ‎losses were estimated using standard transportation economics approaches, including the Value of Time (VOT) method and fuel cost ‎valuation. Pearson correlation and simple linear regression analyses were employed to quantify the relationship between traffic volume ‎and congestion-related economic losses. The results reveal a strong and statistically significant positive relationship between traffic volume and economic loss, with delay duration identified as the primary contributor to congestion costs. Comparative analysis further ‎shows that PM peak periods generate higher marginal economic losses than AM peak periods, reflecting intensified end-of-day travel ‎demand. These findings demonstrate that traffic congestion along President Jose P. Laurel Highway is not merely an operational concern but a significant economic burden. The study provides empirical evidence to support targeted traffic management measures, infrastructure improvements, and demand management strategies aimed at reducing congestion-related economic losses in Lipa City‎.

This paper presents an innovative approach to train timetable generation using Monte Carlo tree search (MCTS) integrated with a deep reinforcement learning technique. The generation and adjustment of train timetables for high-speed railways represent a complex optimisation problem with numerous rule-based constraints that traditional mathematical methods struggle to solve efficiently. Therefore, the train timetable generation problem is modelled as a discrete spatiotemporal Markov decision process, and a comprehensive MCTS-based algorithm is developed to effectively balance exploration and exploitation through a structured tree search mechanism. The result of the comparative analysis demonstrates that MCTS-based algorithms significantly outperform state-of-the-art reinforcement learning algorithms, including double deep Q-network (DDQN) and proximal policy optimisation (PPO), achieving optimal solutions 6.5 times faster with superior training stability. To validate the scalability and real-world applicability, a large-scale case study involving 120 pairs of trains on the Beijing-Shanghai High-Speed Rail corridor over an 18-hour period successfully resolved all 45,600 initial conflicts. The optimised timetables yield significant operational improvements, including a 16.4% reduction in average delay time, 22.8% improvement in track utilisation efficiency and 9.7% reduction in energy consumption. This research contributes to the advancement of intelligent railway operations optimisation and demonstrates the potential of MCTS-based approaches to transform complex transportation problems.

This study has highlighted women's participation and the challenges they face in higher education in the Chittagong district. The rate of women's education at the traditional level has been analyzed. This study has highlighted how women can participate in higher education in the Chittagong district. Data has been collected from (332) female students of various colleges and universities in Chittagong district through questionnaires. Through this data collection, it is evident that most women have pursued higher education for career and social status. However, in some cases, they do not receive financial and psychological support, which decreases the rate of education. Another issue mentioned in this study is that women in higher education are facing a lack of accommodation, mental stress, insecurity in educational institutions, family obstacles, and transportation problems. However, women in higher education (97%) have agreed with the following issues. Introducing scholarships for female students in higher education, eliminating social prejudices, providing safe hostels and transportation, providing special facilities for poor and marginalized women, and increasing the number of female teachers. This study highlights the current context of women's education in the Chittagong district. The information from this study will provide significant benefits to schools, colleges, universities, and the country's education administration and policymakers.

Human immunodeficiency virus (HIV) infection can impact the physical health and psychological well-being of those who are infected and requires effective long-term management to achieve viral suppression. A patient's ability to seek care is largely influenced by social determinants of health (SDOH) and the provider's ability to help manage social needs. This study aims to elucidate how SDOH impacts the provider's practice and well-being. We also assessed how provider burnout, which is significantly associated with feeling emotional exhaustion, plays a role in the patient-provider relationship. This cross-sectional quantitativestudy was conducted at the Division of Infectious Diseases of the University of Kansas Medical Center, Kansas City, USA, with 86 providers who care for people living with HIV across the United States who completed an electronic survey about their perceptions of SDOH, barriers to social needs screening, and their personal experience of burnout. Providers identified several unmet social needs, such as financial instability, transportation, and appointment logistics, as major barriers for the patients to engage in HIV care. Data were analyzed using descriptive statistics and chi-square tests to evaluate associations between provider-related variables and provider burnout. Our results revealed that lack of access to social needs,such as financial instability, transportation, and appointment logistics, and insufficient time were barriers preventing clinicians from inquiring about social needs.We had a total of 86 respondents of healthcare providers: 95% were physicians and 5% were advanced practice practitioners (APPs). Moreover, 35% were located in the Midwest, 28% in the Northeast, 24% in the South, and 13% in the West regions of the United States. Survey respondents indicated that the following SDOH impacted their patients: 60% financial instability, 33% food insecurity, 33% face housing instability, and 49% transportation problems. Sixty-two percent (62%) of the providers reported feeling burned out. Burnout was most frequently reported among providers from the Midwest and the West. Among providers who felt burned out, 26% of those admitted receiving less support from staff and 57% feeling emotional exhaustion. There was a significant association between emotional exhaustion and those who felt burnout. Forty-eight percent (48%) of respondents indicated that they always or often encounter a lack of resources, and 35% identified the lack of time to discuss SDOH as a prominent barrier. These findings suggest the importance of improving provider well-being and optimizing practices to effectively bridge the community with social services to ensure that people living with HIV remain engaged in care and attain the goal of viral suppression.

Soft Computing-Enabled Optimization of Multi-Choice Stochastic Transportation Problem Involving Exponential and Logistic Distributions

Sematang Borang Street is a city road frequently used by Palembang residents to reach their homes. Over time, the number of residents in various housing complexes and settlements continues to increase. The flow of vehicles passing through Sematang Borang Street also increases. This increase has the potential to cause transportation problems, such as congestion, decreased road performance, and reduced road user comfort. Therefore, this researcher conducted an analysis to determine the relationship between traffic flow characteristics, such as volume, speed, and capacity, on Sematang Borang Street, in order to address potential future problems. In this study, the author addresses the analysis of traffic characteristics on Sematang Borang Street in Palembang City. The survey was conducted on a weekday using a one-day enumeration method, including vehicle volume and speed surveys. The calculation and analysis revealed a traffic volume of 1,678.7 passenger car units/hour, with a capacity of 1,414.96 passenger car units/hour. The current value of free flow speed is 32.825 km/hour.