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Facing the diverse needs of large-scale customers, based on available railway service resources and service capabilities, this paper aims to research the design method of railway freight service portfolio, select optimal service solutions and provide customers with comprehensive and customized freight services.

Based on the characteristics of railway freight services throughout the entire process, the service system is decomposed into independent units of service functions, and a railway freight service combination model is constructed with the goal of minimizing response time, service cost and service time. A model solving algorithm based on adaptive genetic algorithm is proposed.

Using the computational model, an empirical analysis was conducted on the entire process freight service plan for starch sold from Xi'an to Chengdu as an example. The results showed that the proposed optimization model and algorithm can effectively guide the design of freight plans and provide technical support for real-time response to customers' diversified entire process freight service needs.

With the continuous optimization and upgrading of railway freight source structure, customer demands are becoming increasingly diverse and personalized. Studying and designing a reasonable railway freight service plan throughout the entire process is of great significance for timely response to customer needs, improving service efficiency and reducing design costs.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

The paper aims to investigate the comfort-related performances of an innovative solar shading solution based on a new composite patented material that consists of a cement-based matrix coupled with a stretchable three-dimensional textile. The paper’s aim is, through a performance-based generative design approach, to develop a high-performance static shading system able to guarantee adequate daylit spaces, a connection with the outdoors and a glare-free environment in the view of a holistic and occupant-centric daylight assessment.

The paper describes the design and simulation process of a complex static shading system for digital manufacturing purposes. Initially, the optical material properties were characterized to calibrate radiance-based simulations. The developed models were then implemented in a multi-objective genetic optimization algorithm to improve the shading geometries, and their performance was assessed and compared with traditional external louvres and overhangs.

The system developed demonstrates, for a reference office space located in Milan (Italy), the potential of increasing useful daylight illuminance by 35% with a reduced glare of up to 70%–80% while providing better uniformity and connection with the outdoors as a result of a topological optimization of the shape and position of the openings.

The paper presents the innovative nature of a new composite material that, coupled with the proposed performance-based optimization process, enables the fabrication of optimized shading/cladding surfaces with complex geometries whose formability does not require ad hoc formworks, making the process fast and economic.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

The aim of this research is to assess the influence of the underlying service quality variable, usually related to university students' perception of the educational experience. Another aspect analysed in this work is the development of a procedure to determine which variables are more significant to assess students' satisfaction.

In order to achieve both goals, a twofold methodology was approached. In the first phase of research, an assessment of the service quality was performed with data gathered from 580 students in a process involving the adaptation of the SERVQUAL scale through a multi-objective optimization methodology. In the second phase of research, results obtained from students were compared with those obtained from the teaching staff at the university.

Results from the analysis revealed the most significant service quality dimensions from the students' viewpoint according to the scores that they provided. Comparison of the results with the teaching staff showed noticeable differences when assessing academic quality.

Significant conclusions can be drawn from the theoretical review of the empirical evidences obtained through this study helping with the practical design and implementation of quality strategies in higher education especially in regard to university education.

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.