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Focusing on the insufficient passenger demand during off-peak periods in urban rail transit, a new train operation mode with flexible train composition considering trains couple/decouple at intermediate stations is proposed. An optimized two-phase nonlinear model is formulated in order to design a periodic train schedule, where train headways remain constant over a specific period. This model takes into account the flexible train composition. The objective function aims to minimize total passenger waiting time and train operating costs while considering constraints such as maximum line passing capacity, passenger flow demand, and the number of train units in the composition. Linearization techniques are applied to convert nonlinear constraints into linear equivalents, and the CPLEX solver is employed to solve the model. An illustrative real-case example is presented to validate the effectiveness of the proposed model. The results demonstrate that the operation mode with flexible train composition during off-peak periods offers significant advantages over traditional train composition modes, including single train routes, long-short train routes, and multiple train compositions. This mode enables better alignment between passenger demand and capacity, reduces operating costs, and enhances passenger service levels. The proposed operation mode is particularly suitable for urban rail transit lines with low passenger demand but requiring high passenger service, such as suburban lines or common lines during off-peak periods. [ABSTRACT FROM AUTHOR]

The training schedule is a factor that influences sports performance optimization. In a sport like soccer, there is often significant disparity in training schedules among different teams within the same club, without considering whether this may affect players' performance. The aim of this study was to describe differences in nutrient intake and body composition in elite youth soccer players from the Spanish league with different training schedules (morning and evening). A cross-sectional study was conducted to determine differences in anthropometric variables and dietary assessment in a sample of Spanish young soccer players. A total of 41 players participated in this study. After comparing the groups according to their training schedule, no differences were observed in body composition between both groups; the evening-night training group showed higher consumption of lipids and saturated fats. In summary, more experimental studies are needed to determine the effects on various health and performance parameters of different training schedules in young population. [ABSTRACT FROM AUTHOR]

In metropolises with large populations, some metro lines tend to have particularly high train load rates during peak hours, causing passengers to be stranded for several times on the platforms of some stations, triggering undesirable consequences of platform congestion and a lower level of service for some passengers. Therefore, coordinated passenger inflow control becomes an important means to alleviate station congestion and balance the fairness of passenger service level between stations on an oversaturated metro line. This paper considers the influence of passengers' train selection behavior on inflow control under the conditions of a long–short train route pattern, puts forward the train schedule–based inflow control step division principle, with the objective function of minimizing the total stranded value of the line, and establishes a line-based coordinated inflow control model under the constraints of passenger demand, platform capacity, and train capacity. To solve the model, a simulation-based algorithm embedded with improved genetic algorithm is developed. A case study on China's Shanghai Metro Line 6 is conducted to demonstrate the performance and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Under the condition of urban rail transit uncertainty of passenger demand and the high frequency of departure intervals, this study presents an innovative real-time urban rail transit (URT) train service scheduling control framework. In the context of a bidirectional urban rail transit line, a high-fidelity urban rail transit simulation environment was constructed. Within this environment, an advantage actor–critic (A2C) reinforcement learning approach was utilized to train a suitable strategy aimed at minimizing both passenger waiting costs and transit authority operational expenses. Subject to specific constraints, the strategy is designed to generate real-time train schedule based on the representation of traffic state using station congestion levels and train positions. Experimental results on Lines 3 and S7 of Nanjing Metro demonstrated the agent's effectiveness in achieving high-performance schedules across various scenarios. This research integrates deep reinforcement learning into the optimization of dynamic traffic systems, showing great potential for enhancing the efficiency and resilience of urban transport systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Transportation is an important component in the logistics and production processes. To accurately match rapidly growing demand and limited transport capacity, the goal of minimising costs while ensuring high service quality under existing infrastructure has received significant attention. This paper presents an integrated optimisation approach for the short-term operational management under daily fluctuating demand, with a focus on two key strategic decisions: train timetabling and coupling. In particular, an integrated two-stage stochastic model and a combined heuristic local search algorithm with the branch-and-bound method are developed to (1) obtain the optimal demand assignment to the rail network, (2) investigate trains' coupling plans to avoid waste of resources when demand is low, and (3) add candidate trains to generate new feasible timetables when demand surges. To verify the solving method, a lower bound algorithm is introduced. Using a hypothetical small-scale and a real-world China high-speed rail network as numerical experiments, different demand scales and critical parameters are tested to obtain optimised timetables. The results show that good solutions are achieved in several seconds, making it possible to adjust trains' schedules efficiently and effectively according to the variable demand in short-term operational management. [ABSTRACT FROM AUTHOR]

Background: The Accreditation Council for Graduate Medical Education has tasked residency programs to prioritize resident wellness, reduce trainee stress, and prevent burnout. Grief and bereavement can significantly impact residents' wellness during difficult clinical training schedules. There are no best practices on how to support residents during this time. Methods: In a split academic county emergency medicine (EM) residency, this pilot study documents a resident‐driven change to scheduling practices for bereavement leave. An advisory group of residents, chief residents, and program directors informally polled peer institutions to develop bereavement leave guidelines. Considerations were made to balance resident wellness, education, and patient care in developing a bereavement scheduling policy. Results: The bereavement policy was adopted in January 2023, aiming to "support the resident during a difficult time and reduce concerns around shift coverage" following the death of a family member without impacting sick call. The number of covered days depended on the relationship of the resident to the deceased. Residents covering bereavement days for their peers were financially compensated. During the first 7 months following implementation, five residents utilized the policy. These residents noted this to be the most positive impact on the residency during the past year. Based on resident feedback, the scope was expanded to include grave medical illness of a family member as an implementation criterion. Conclusions: This article outlines the creation, implementation, and benefits of a bereavement scheduling policy within an EM residency. Describing this approach will provide guidance for other residencies to adopt similar wellness‐focused strategies. [ABSTRACT FROM AUTHOR]

Effective work scheduling for clinical training is essential for medical education, yet it remains challenging. Creating a clinical training schedule is a difficult task, due to the complexity of curriculum requirements, hospital demands, and student well-being. This study proposes the Collaborative Control Protocol with Artificial Intelligence for Medical Student Work Scheduling (CCP-AI-MWS) to optimize clinical training schedules. The CCP-AI-MWS integrates the Collaborative Requirement Planning principle with Artificial Intelligence (AI). Two experiments have been conducted comparing CCP-AI-MWS with current practice. Results show that the newly developed protocol outperforms the current method. CCP-AI-MWS achieves a more equitable distribution of assignments, better accommodates student preferences, and reduces unnecessary workload, thus mitigating student burnout and improving satisfaction. Moreover, the CCP-AI-MWS exhibits adaptability to unexpected situations and minimizes disruptions to the current schedule. The findings present the potential of CCP-AI-MWS to transform scheduling practices in medical education, offering an efficient solution that could benefit medical schools worldwide. [ABSTRACT FROM AUTHOR]

In some automated container terminals, railway lines have been implemented into the port, saving container transfer time. However, the equipment scheduling level of the railway yard needs to be improved for managers. In the equipment scheduling of loading and unloading containers for railway trains, the operation modes "full unloading and full loading" and "synchronous loading and unloading" are often adopted. Due to the long length of the railway yard and the line of one train, there are two ways to arrange loading and unloading tasks for automated rail-mounted gantry cranes (ARMGs): one is to pre-assign tasks for ARMGs, and the other is to not pre-assign tasks for ARMGs. To investigate the efficacy of these different operation modes and methods of assigning tasks, this study formulated three mixed-integer linear programming (MILP) models with the goal of minimizing the ARMG task completion time. An adaptive large neighborhood search algorithm was used to tackle the scheduling problem. The scheduling effects of different operation modes and methods for assignment tasks were compared in terms of their calculation time and the completion time of ARMG tasks. Notably, the findings reveal that, with an increase in the number of tasks, the "pre-assign" task arrangement had a limited effect on the completion time of the ARMG tasks, made the calculation time shorter, and reduced the complexity of the problem. From the perspective of the completion time of ARMG tasks, the time under the "synchronous loading and unloading" operation mode was less than that of the "full unloading and full loading" operation mode. Therefore, it is recommended that the managers of the railway yard in an automated container terminal adopt the "synchronous loading and unloading" operation mode but determine the task assignment method according to decision time requirements. In addition, when the number of tasks is large, to decrease the time to complete ARMG tasks, the manager can adopt the "non-pre-assign" task distribution method. [ABSTRACT FROM AUTHOR]

This article uses Lefebvre’s rhythmanalysis to critically explore and make-sense-of the (co-)productive schemes of ‘coaching rhythm(s)’ at Alvour Football Club (a pseudonym). The first author spent seven months at a semi-professional football club by living with the group’s weekly schedule of training, meetings, and matches. The precise research methods included (written) field-notes, individual semi-structured interviews, and visual data. The findings introduce and explicate how the group’s rhythmic flow of the week was subject to the perpetual manipulation and imposition of the coaching staff, while also being politically negotiated, and simultaneously resisted by athletes. Specifically, the analysis uses the interplay between ‘eurhythmia’, ‘polyrhythmia’, and ‘arrythmia’ to understand the subtle and nuanced routines of coaching, and how individuals become sensitive to their production. In addition to introducing the value of understanding rhythm(s) in coaching, it is hoped the rhythmanalysis offered in this paper contributes an original interpretation of the taken-for-granted everydayness of coaching, which can challenge many of the assumed mechanical overtones that currently exist. Doing so, the paper offers a new reading of coaching as manipulating rhythm(s). [ABSTRACT FROM AUTHOR]

Cross-line operation increases accessibility to the railway network. However, its long-distance operation is quite difficult due to the transport resource limitations. To provide good transportation plans for operators and more direct services for cross-line passengers, we propose a new method of changing operation zones of cross-line trains, which can be summarized as the integrated train line planning and timetabling problem. A model is established to minimize deviation of same-line trains from ideal timetables and maximize cross-line origin-destination direct services. A Lagrangian relaxation approach and an improved priority-rule heuristic algorithm are developed to solve large-scale problems and obtain feasible solutions. Reality-based instances are conducted on the Beijing-Shanghai high-speed railway and its connecting lines to verify the performance of the model and solution approach. The results show the benefit of changing train operation zones, and the model and approach can help to generate a good balance between operating profit and passenger demand. [ABSTRACT FROM AUTHOR]

In the era of big data, deep neurals network models represented by LSTM have the ability to process massive data, and have excellent performance in the fields of language processing, speech recognition and time series data prediction. However, with the increase of model complexity, the training cost increases significantly. The existing LSTM training systems use acceleration methods, such as operator fusion and multi-stream, but neglect the parallelism of the internal calculation of a single training operator, which leads a low utilization rate of computing resources and a long traning time. Therefore, this paper designs a training acceleration system called TurboLSTM based on fine-grained model partitioning method and multi-stream parallel scheduling strategy. A new underlying training operator built on NVIDIA GPU and domestic Ascend NPU heterogeneous hardware realizes reasonable utilization of computing resources for tasks. Compared with the existing training systems, TurboLSTM on NVIDIA GPU has about 23% speed improvement of a single operator and about 17% speed improvement of the overall training time of a model, while TurboLSTM on Ascend NPU has about 15% speed improvement of a single operator, and the significant increase in the utilization of computing resources is observed. This shows that the acceleration method is efficient and has good generalization ability. [ABSTRACT FROM AUTHOR]

Virtual coupling of railway trains is an emerging technology that has the potential to significantly increase railway operational efficiency by reducing the train following distance from absolute braking distances to relative braking distances. Current research in this topic is mainly focused on passenger trains and uses distance-based headways. This paper studied virtual coupling for heavy haul freight trains and demonstrated that the distance headway scheme was challenging and sometimes impractical for heavy haul trains to achieve virtual coupling. A time-based headway scheme was then proposed to set the follower train to be a certain time behind the schedule of the leader train rather than a distance headway. The time-based headway required the follower train to reproduce the leader train's operational status at the same track location. This also allowed the follower train to copy any optimized train driving strategies from the leader train. Demonstrative simulations were carried out without the consideration of communication errors and train localization errors. The results show that a conventional distance headway simulation had maximum distance and speed errors of 716 m (36%, reference 2 km) and 24 km/h (66%, reference 36 km/h), respectively. A time-based headway simulation reduced the maximum distance and speed errors to 0.07 m (0%, reference 2 km) and 0.1 km/h (9%, reference 1.18 km/h), respectively. [ABSTRACT FROM AUTHOR]

This paper incorporates the shortest passenger travel paths into the first train timetabling problem in urban rail transit networks. We first propose a path-based first train timetabling problem, which is formulated as a mixed-integer nonlinear programming model to reduce the travel time for passengers. To solve this model, we present a tailored branch-and-bound combined with the timetable-based Dijkstra algorithm. To further enhance the efficiency of the exact algorithm, we design a rolling time heuristic algorithm as a substitute for the branch-and-bound algorithm. Then, we proved that the rolling time heuristic combined with the timetable-based Dijkstra algorithm achieves 97.86% and 99.39% reductions in CPU time while sacrificing only 0.007% and 0.057% of optimality gap by two numerical experiments. Finally, the results based on two test networks with different structures demonstrate that the path-based first train timetabling model effectively improves passenger travel efficiency. Specifically, the travel times are reduced by 744 mins and 761 mins, while the transfer waiting times are reduced by 37.87% and 58.91% compared to the existing timetables. Moreover, the results also demonstrate that loop line networks will increase the difficulty of timetabling and lead to many passengers taking detours. To further enhance the efficiency of commuting for passengers who take the first train, URT operators may consider relaxing the limitations on the departure time of the depot stations. • A path-based first train timetabling model to minimize the total travel time in urban rail transit networks. • A tailored branch-and-bound combined with a timetable-based Dijkstra algorithm. • A rolling time heuristic algorithm to solve large-scale networks cases. [ABSTRACT FROM AUTHOR]

Summary: Real‐time estimation of crowd counting in underground metro systems, constrained by limited space, is crucial for managing heightened pedestrian volumes and responding promptly to emergencies. To address this challenge, we propose a passenger state transition‐based model, called STRmt, designed for the seamless and continuous monitoring of real‐time crowd movement within service areas of stations and trains, leveraging auto fare collection systems (AFC) as a comprehensive sensor network. Our innovation lies in modeling the dynamic movement of passengers within a metro system over time as a state transition process aligned with the train schedule. To achieve this, we introduce a spatio‐temporal deep learning framework, denoted as STnet, designed to dynamically predict these state transitions. The performance of our method is rigorously assessed through extensive experiments conducted spanning 2 years in Shenzhen, China, utilizing AFC data, train schedule data, and weather data. The results demonstrate that the proposed method surpasses baseline methods, achieving an estimation precision of 0.92. [ABSTRACT FROM AUTHOR]

Featured Application: Monitoring salivary biomarkers demonstrates a potential application to assess the internal training load and recovery status of elite futsal players during congested competition periods, especially total protein and salivary immunoglobulin A. Tracking biomarker changes provides a practical tool for coaching staff to guide adjustments to training loads, nutrition, and recovery protocols to mitigate risks and optimize athlete performance and health. A congested match and training schedule could alter internal load, and this could be reflected in biomarkers of stress and immunity in elite futsal players. The aim of this study was to analyze the effects of a congested match and training schedule on internal load and levels of total protein, total oxidant status (TOS), total antioxidant capacity (TAC), oxidative stress index, and the concentrations of salivary immunoglobulin A (SIgA) in 17 professional players from the same Portuguese elite futsal club (age: 23.07 ± 6.76 years old; height: 1.75 ± 0.06 m; body mass: 75.47 ± 7.47 kg; experience in playing in elite: 5.38 ± 2.03 years) who performed 5 matches and 16 training sessions in a period of 27 days. The salivary content of total protein, TOS, TAC, oxidative stress index, and SIgA were calculated before and after the training sessions and the unofficial matches under study. Saliva sampling was conducted 10 min before each match or training session and 40 min after (post-match and post-training). The MANOVA of repeated measures showed a significant difference for total protein and SIgA (p < 0.01). Total protein (sphericity = 0.007; statistical power = 0.818) and SIgA (sphericity = 0.018; statistical power = 0.693) are highly correlated with the time factor. The main findings revealed several key points: (a) There was a significant increase in total protein, SIgA, and TAC during acute load (pre- vs. post-session) in both training and match contexts. Specifically, total protein and SIgA displayed notable increments in both training and match settings, while TAC exhibited significant increases exclusively during matches. (b) No changes in TOS and oxidative stress index were observed during acute load in either training or match contexts. (c) A positive trend was noted between the chronic load during a congested week of the precompetitive season and the decrease in total protein and SIgA levels. (d) Additionally, a positive correlation between internal training loads and oxidative/antioxidant responses was found, as expressed by the oxidative stress index, without significant differences (p-value > 0.05) in acute and chronic loads during congested matches and training schedules. [ABSTRACT FROM AUTHOR]

The determination of transport schemes represents a critical facet of operational management in express freight logistics and constitutes a significant scientific challenge within the domain of freight transportation. Globally, there is an escalating focus on adopting low-carbon transit strategies, and it has been established that multimodal transport methods can significantly diminish carbon emissions associated with freight transportation. Moreover, the scheduled arrival and departure times for railway and aviation are pivotal in shaping the selection of transport schemes. Regrettably, these critical considerations are frequently neglected in the prevailing body of research. This paper endeavors to bridge this research gap by advocating for a lowcarbon transport scheme for express freight through the utilization of an integrated transportation system. It undertakes a collaborative optimization study concerning transportation modes and routes. This document proposes an optimization framework for the selection of express freight transport schemes that incorporates considerations for carbon emissions and fixed timetables. A three-layer service network is developed, integrating the fixed schedules of railway and aviation to formulate a space-time shortest path problem aimed at minimizing the generalized transportation cost. Three models are formulated to reduce the transportation costs and carbon emissions of logistics enterprises, taking into account diverse carbon emission policies. Subsequently, a two-stage heuristic algorithm is introduced to address this complex problem. The findings reveal that by accounting for customers' transportation time constraints and judiciously selecting transportation modes and routes, it is feasible to effectively minimize transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

The rescheduling of train timetables is a crucial task in the scheduling of railway transport, known as the Train Timetable Rescheduling (TTR) problem. In order to increase passenger satisfaction with transfer and reduce the impact of delayed trains on transfer succession and railway operation, a high-speed railway multiobjective optimization model for train timetable rescheduling is constructed with station operating time, transfer station track utilization rate, and train operating time as constraints, and maximizing passenger satisfaction, minimizing total train delay time, and minimizing the number of failed passengers in transfer as optimization objectives. A multiobjective solution algorithm based on hierarchical sequence theory was designed, and finally, the model was solved using Gurobi. Taking the example of the railway network comprising of Beijing-Shanghai high-speed railway and Xuzhou-Lanzhou high-speed railway, two adjustment strategies have been formulated to validate the model. The results show that the train timetable rescheduling model considering transfer passenger satisfaction can significantly reduce the delay time of transfer passengers and the possibility of transfer failure while maintaining high transfer passenger satisfaction, which provides a new idea for real-time train operation scheduling considering transfer succession. [ABSTRACT FROM AUTHOR]

Maritime transportation plays a pivotal role in the global merchandise trade. To improve maritime safety and protect the environment, every state must effectively control ships flying its flag, which is called flag state control (FSC). However, the existing FSC system is so inefficient that it cannot perform its intended function. In this study, we adopt an optimization method to tackle this problem by constructing an integer programming (IP) model to solve the FSC officer routing and scheduling problem, which aims to maximize the total weight of inspected ships with limited budget and human resources. Then we prove that the IP model can be reformulated into a partially relaxed IP model with the guarantee of the result optimality. Finally, we perform a case study using the Hong Kong port as an example. The results show that our model can be solved to optimality within one second at different scales of the problem, with the ship number ranging from 20 to 1000. Furthermore, our study can be extended by considering the arrangement of working timetables with finer granularity and the fatigue level of personnel. [ABSTRACT FROM AUTHOR]

With the expansion of the metropolitan area, the application of express/local mode is gradually increasing. In contrast to the normal mode, the express/local mode has advantages in reducing energy consumption and saving total travel time by having express trains skipping some stops. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. As the consumed energy of a train is highly dependent on the rolling stock weight and the on‐board passengers' weight. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. An improved grey wolf algorithm is designed by improving convergence factor and incorporating differential evolution to solve the proposed problem. The real data on Guangzhou Metro Line 18 is adopted for numerical studies. The results show that the optimized timetable increases the regenerative energy utilization rate by 21.37% and reduces the total energy consumption by 5.02% compared to the operational timetable. This paper aims to minimize the total energy consumption of express and local train throughout the day by optimizing the train operation strategy in the same power supply section and increasing the overlap time between train traction acceleration and train regenerative braking to obtain the optimal energy‐efficient timetable. An integer programming model is proposed with on‐board passengers considered accurately, in which the dwell times, departure headway, and total turnaround time of express and local trains are determined. [ABSTRACT FROM AUTHOR]

In this study, the shift scheduling problem of the personnel who maintain the 324 trains operating on the lines of the Ankara Metro, which carry approximately 10 million passengers per month, is discussed. In the problem, daily personnel needs and personnel qualifications are evaluated together according to the fault prediction. Firstly, a total of 1721 fault data for 181 days is analyzed, and the prediction is made according to 8 prediction methods. Then, a 30-day fault prediction is made with the prediction method, which has the least error rate. According to the number of faults obtained as a result of the prediction, the daily personnel requirement is determined. Then, according to the type of fault, the personnel are classified as electronics, electromechanics, and mechanics. It is aimed at distributing the personnel evenly to the shifts. The goal programming model is used to solve the problem, allowing us to add more than one goal to the model. When the solution results of the problem are compared with the current schedule, it is seen that the monthly total working hours are distributed equally, and the shifts are distributed fairly, and a necessary balance is achieved in the shifts according to the personnel qualifications. It is thought that this study, in which personnel qualifications and fault prediction are evaluated together, will contribute to the literature on railway maintenance personnel scheduling. [ABSTRACT FROM AUTHOR]

Production planning in maintenance facilities that rely on the availability of certified operators is a challenging task. This is primarily attributed to the need for the integration of workforce training with the operations schedule, in addition to the uncertain repair time of some components. We confine our attention to the repair and overhaul of advanced technological devices that are composed of several components. In this context, this study proposes a novel approach to embedding uncertain repair times of faulty components and develops a multi-stage stochastic programming model for integrated production and workforce planning in such facilities under independent random repair time. Our numerical experiments conducted on a range of problem instances expose the significant value of incorporating repair time uncertainty in this problem. [ABSTRACT FROM AUTHOR]

Ensuring the reliability of railway transportation is heavily dependent on the quality of its infrastructure. In this regard, renewal and maintenance of the railway track infrastructure, referred to as trackwork, play a vital role. However, trackwork execution requires temporary capacity restrictions for train traffic. Therefore, harmonising the train and maintenance schedules is critical but challenging to accomplish when one is frequently changing. This paper explores and models the nature of trackwork schedule instability at the tactical level of the scheduling process. We analyse data from one year of trackwork rolling horizon plans, focusing on weekly changes at eight key trackwork locations across Sweden's railway network. Our study considers various factors that may affect schedule stability, such as track type, location, time of day, train traffic intensity, and the type of prevailing traffic. We find that schedule instability increases as the rolling horizon plan approaches its end. The regression analysis reveals that the most significant predictors of changes in trackwork schedules include previous changes, track type (single vs. double), work location (at station vs. between stations), and the timing of trackwork (daytime vs. nighttime and month). These provide insights to trackwork planners in making informed and proactive decisions about trackwork timeslot allocation. [ABSTRACT FROM AUTHOR]

Logistics distribution is one of the main sources of carbon dioxide emissions at present, and there are also such distribution problems in the shipbuilding process. With the increasing attention paid to environmental problems, how to effectively reduce the energy consumption of block transportation and improve the utilization rate of resources in the factory is the key problem that China's shipbuilding industry needs to solve at present. This article considers the time windows for block transportation tasks, as well as the self-loading constraints of different types of flat cars, and establishes an optimization model that minimizes the empty transport time and energy consumption of the flat cars as the optimization objective. Then, an Improved Genetic Whale Optimization Algorithm is designed, which combines the cross and mutation ideas of genetic algorithms and proposes a whale individual position updating mechanism under a mixed strategy. Furthermore, the performance and computational efficiency of the algorithm are verified through comparative analysis with other classical optimization algorithms on standard test examples. Finally, the shipyard's block transportation example proves that the energy-saving ship block transportation scheduling method can effectively improve the efficiency of shipbuilding enterprise's block transportation and reduce the energy consumption in the block transportation process. It proves the engineering practicality of the green dispatching method proposed in this paper, which can further provide a decision-making method for shipyard managers. [ABSTRACT FROM AUTHOR]

In container sea–rail combined transport, the railway yard in an automated container terminal (RYACT) is the link in the whole logistics transportation process, and its operation and scheduling efficiency directly affect the efficiency of logistics. To improve the equipment scheduling efficiency of an RYACT, this study examines the "RYACT–train" cooperative optimization problem in the mode of "unloading before loading" for train containers. A mixed-integer programming model with the objective of minimizing the maximum completion time of automated rail-mounted gantry crane (ARMG) tasks is established. An adaptive large neighborhood search (ALNS) algorithm and random search algorithm (RSA) are designed to solve the abovementioned problem, and the feasibility of the model and algorithm is verified by experiments. At the same time, the target value and calculation time of the model and algorithms are compared. The experimental results show that the model and the proposed algorithms are feasible and can effectively solve the "RYACT–train" cooperative optimization problem. The model only obtains the optimal solution of the "RYACT–train" cooperative scheduling problem with no more than 50 tasks within a limited time, and the ALNS algorithm can solve examples of various scales within a reasonable amount of time. The target value of the ALNS solution is smaller than that of the RSA solution. [ABSTRACT FROM AUTHOR]

High freight and passenger-carrying capacity, providing a fast and safe journey, and its tourism potential, railway transportation remains preferable among transportation types. Many personnel perform different tasks such as operation, administration, maintenance, security, and cleaning on the railways to provide an excellent service to the passengers. These personnel are among the most critical parts of the railway system in infrastructure, passenger, and freight transport. This study aims to create a schedule by using mathematical methods for the personnel working in the railway system. The goal programming method, which enables to achieve more than one goal simultaneously, was used to solve the problem. It is planned to create the most appropriate and efficient work plan by analyzing the work, task distribution, and shift schedules of the personnel working on the high-speed railway (HSR) Ankara-Istanbul line. The 28 day team schedule, which aims to distribute the duties of the staff equally, includes the distribution of the teams and the creation of tours for a total of sixteen daily trips. 225 crew working between Ankara-Istanbul and Istanbul-Ankara were formed. We gathered a fair schedule for the crew, and we increased the satisfaction level of the personnel. While all this is being done, the business requests were met. [ABSTRACT FROM AUTHOR]

Customized bus systems can improve service quality and efficiency by taking advantage of the speed regulation and capacity variation of autonomous bus systems. To address the challenges of customized bus systems design, this study proposes a collaborative optimization model of vehicle scheduling and speed regulation for the autonomous customized bus system. This model minimizes enterprise operation and passenger travel costs by considering mileage constraints, the time window of passenger travel constraints, and demand pairs coverage constraints. Based on the theoretical properties discovered in the joint design model, the problem is decomposed into a set partitioning master problem and route-selection subproblem. An algorithm combining column generation approach and genetic algorithm is proposed to solve the joint design model. To accelerate the calculation in searching for the shortest route for subproblems, hierarchical filtering strategy is used to reduce the cost of finding feasible routes and improve computational efficiency. The case study reveals that the proposed algorithm exhibits better convergence and stability according to the Wilcoxon test. In a numerical experiment with 100 demand pairs, the joint model can be solved by the algorithm proposed in the study optimally with 19% of the computation time required by the exact algorithms. The departure frequency was reduced by 22%, and the operating cost was reduced by 42% compared with those of customized bus lines serviced separately by human-driven buses. Sensitivity analyses were performed on various operating parameters, such as modular bus capacity and speed boundary, across three different scale cases. The results demonstrate that reasonable parameter settings are beneficial for reducing operating costs and vacant seats. • A collaborative optimization model of bus scheduling and speed regulation is proposed. • An algorithm combining column generation and genetic algorithm is proposed. • Hierarchical filtering strategy is used in the subproblem of the proposed algorithm. • An autonomous customized bus system can reduce corporate costs compared to human-driven buses. [ABSTRACT FROM AUTHOR]

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network's optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies. [ABSTRACT FROM AUTHOR]

Recent advancements in machine learning (ML) applications have set the stage for the development of autonomous construction project scheduling systems. This study presents a blueprint to demonstrate how construction project schedules can be generated automatically by employing machine learning (ML) and building information modeling (BIM). The proposed solution should utilize building information modeling (BIM) international foundation class (IFC) 3D files of previous projects to train the ML model. The training schedules (the dependent variable) are intended to be prepared by an experienced scheduler, and the 3D BIM files should be used as the source of the scheduled activities. Using the ML model can enhance the generalization of model application to different construction projects. Furthermore, the cost and required resources for each activity could be generated. Accordingly, unlike other solutions, the proposed solution could sequence activities based on an ML model instead of manually developed constraint matrices. The proposed solution is intended to generate the duration, cost, and required resources for each activity. [ABSTRACT FROM AUTHOR]

Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations. [ABSTRACT FROM AUTHOR]

The train operations of large stations are critical in determining the efficiency of the railway network. Large high‐speed railway stations often have more than two adjacent stations running in multiple directions and must address highly complex train operation patterns. To develop a more efficient operation plan than the existing route‐based representations for modeling train conflicts, a more systematic track‐based resource network (TRN) representation for a railway station that simulates fixed equipment is used in this paper. Based on this high‐fidelity TRN, an improved operation scheduling model (OSM) is introduced for the integrated scheduling of train operations, including shunting operations. Besides, two approaches based on Lagrangian relaxation (LR) and the Alternating Direction Method of Multipliers (ADMM) are described in detail to address the operation scheduling problem (OSP) for stations, and an efficient heuristic rule‐based algorithm is also designed to generate lower bound solutions. To improve the algorithm efficiency, we designed a parallelization strategy for the proposed approaches. Based on real‐world case studies, the computing results demonstrate that the parallelized ADMM‐based approach can always find high‐quality feasible solutions for complex stations and generate conflict‐free station operation plans with significantly improved efficiency. [ABSTRACT FROM AUTHOR]

With the implementation of cross-line operation in high-speed railway system, accessibility of cross-line passengers on the railway network has substantially improved. However, due to limitations of capacity, it is hard to schedule a conflict-free timetable based on a train line plan with many cross-line trains. In order to generate a feasible and optimal transportation plan, a novel methodology is introduced. The approach can simultaneously optimize both the line plans of cross-line trains and train timetable, aiming at having a trade-off between operating profit and direct service. Based on an event-activity network framework, a mixed integer programming model is established. Considering service quality would decline after optimizing line plan and train schedules, the objective of the model is set to minimize deviations from ideal schedules for main-line trains while maximize direct service frequency for cross-line passengers. To solve large-scale scenarios efficiently, an enhanced heuristic genetic algorithm is developed. Two smaller-scale cases are devised to validate the efficiency of the model and approach. Finally, the model and the algorithm are applied to a real-world scenario involving the Beijing-Shanghai High-speed Railway and its connecting lines. Also, comparative experiments, including a scenario without cross-line optimization, are conducted to evaluate the advantage of the proposed approach. The result shows the approach can help to quickly find a feasible solution and have a good balance between operating profit and passenger demand. [ABSTRACT FROM AUTHOR]

This paper considers the scheduling of electric vehicles in a public transit system. Our main innovation is that we take into account that charging stations have limited capacity, while also considering partial charging. To solve the problem, we expand a connection-based network in order to track the state of charge of vehicles and model recharging actions. We then formulate the electric vehicle scheduling problem as a path-based binary program, whose linear relaxation we solve using column generation. We find integer feasible solutions using two heuristics: price-and-branch and a diving heuristic, including acceleration strategies. We test the approach using data from the concession Gooi en Vechtstreek in the Netherlands, containing up to 816 trips. The diving heuristic outperforms the other heuristic and solves the entire concession within seven hours of computation time with an optimality gap of less than 3%. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0253. [ABSTRACT FROM AUTHOR]

Unplanned disruptions, such as vehicle breakdowns, in a public transportation system can lead to severe delays and even service interruptions, preventing the successful implementation of subsequent plans and the overall stability of transit services. A common solution to address such issues is implementing a bus bridging service using an experience-based response strategy, involving the deployment of spare buses to continue affected services. However, with this approach, it becomes impractical and challenging to generate a feasible and rational rescheduling scheme for the remaining transit services when spare buses are insufficient or widespread disruptions occur. In response to this challenge, we propose an innovative model that integrates service capability and regularity, aiming to minimize rescheduling costs through timetable adjustments and scheduling reassignments. We apply dynamic programming to comprehensively consider the hysteresis effects of disruptions and achieve a long-term optimal rescheduling scheme. To efficiently solve the proposed model, the large neighborhood search algorithm is improved by incorporating operational rules. Finally, several experiments are conducted under an actual transit operation scenario in Shenzhen. The results demonstrate that our method significantly reduces trip cancellations and, simultaneously, diminishes the increase in the departure interval resulting from the adjusted schedule by 23.27%. [ABSTRACT FROM AUTHOR]

In recent years, the global surge of High-speed Railway (HSR) revolutionized ground transportation, providing secure, comfortable, and punctual services. The next-gen HSR, fueled by emerging services like video surveillance, emergency communication, and real-time scheduling, demands advanced capabilities in real-time perception, automated driving, and digitized services, which accelerate the integration and application of Artificial Intelligence (AI) in the HSR system. This paper first provides a brief overview of AI, covering its origin, evolution, and breakthrough applications. A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system: mechanical manufacturing and electrical control, communication and signal control, and transportation management. The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components, forecast of railroad maintenance, optimization of energy consumption in railroads and trains, communication security, communication dependability, channel modeling and estimation, passenger scheduling, traffic flow forecasting, high-speed railway smart platform. Finally, challenges associated with the application of AI are discussed, offering insights for future research directions. [ABSTRACT FROM AUTHOR]

With the continuous prosperity of maritime transportation on a global scale and the resulting escalation in port trade volume, tugboats assume a pivotal role as essential auxiliary tools influencing the ingress and egress of vessels into and out of ports. As a result, the optimization of port tug scheduling becomes of paramount importance, as it contributes to the heightened efficiency of ship movements, cost savings in port operations, and the promotion of sustainable development within the realm of maritime transportation. However, a majority of current tugboat scheduling models tend to focus solely on the maximum operational time. Alternatively, the formulated objective functions often deviate from real-world scenarios. Furthermore, prevailing scheduling methods exhibit shortcomings, including inadequate solution accuracy and incompatibility with integer programming. Consequently, this paper introduces a novel multi-objective tugboat scheduling model to align more effectively with practical considerations. We propose a novel optimization algorithm, the Improved Grey Wolf Optimization (IGWO), for solving the tugboat scheduling model. The algorithm enhances convergence performance by optimizing convergence parameters and individual updates, making it particularly suited for solving integer programming problems. The experimental session designs several scale instances according to the reality of the port, carries out simulation experiments comparing several groups of intelligent algorithms, verifies the effectiveness of IGWO, and verifies it in the comprehensive port area of Huanghua Port to get the optimal scheduling scheme of this port area, and finally gives management suggestions to reduce the cost of tugboat operation through sensitivity analysis. [ABSTRACT FROM AUTHOR]

Railway capacity estimation problem is typically defined as estimating the maximum number of trains that can be operated in a railway section within a given time interval. However, trains with different speeds, routes, and stopping patterns in a railway network will likely compete for the limited capacity of network nodes and sections. As these trains may provide different services, it is ambiguous to simply indicate the network capacity by a scalar number of trains. To comprehensively estimate and interpret the railway capacity considering the capacity competition between heterogeneous trains, we propose a multiobjective perspective for the capacity estimation problem to enrich the capacity theory while handling the competition among trains with different routes and stopping patterns. Based on a time-space network timetable saturation model, we extend the multiobjective capacity estimation approach to the detailed timetable level by optimizing the saturated timetable under capacity estimation objectives with respect to different routes and stopping patterns. With the ε-constraint method, we can obtain the Pareto front of saturated timetables, i.e., a set of nondominated optimized timetables that no more candidate train can be additionally scheduled. The result is a more comprehensive capacity representation than a single absolute scalar number. A case study is conducted on a combined high-speed and intercity network of Zhengzhou Railway group in China. An extensive set of Pareto-optimal saturated timetables describing the effects on the capacity of the railway network is obtained. The results can help infrastructure managers select saturated timetables as the capacity utilization reference by considering the trade-off between time indexes from passengers' and operators' perspectives. [ABSTRACT FROM AUTHOR]

This paper presents a solution to address the challenges of unexpected events in the operation of metro trains, which can lead to increased delays and safety risks. An improved Q-learning algorithm is proposed to reschedule train timetables via incorporating train detention and different section running times as actions. To enhance computational efficiency and convergence rate, a simulated annealing dynamic factor is introduced to improve action selection strategies. Additionally, importance sampling is employed to evaluate different policies effectively. A case study of Shenzhen Metro is conducted to demonstrate the effectiveness of the proposed method. The results show that the method achieves convergence, fast computation speed, and real-time adjustment capabilities. Compared to traditional methods such as no adjustment, manual adjustment, and FIFO (First-In-First-Out), the proposed method significantly reduces the average total train delay by 54% and leads to more uniform train headways. The proposed method utilizes a limited number of variables for practical state descriptions, making it well suited for real-world applications. It also exhibits good scalability and transferability to other metro systems. [ABSTRACT FROM AUTHOR]

Speech learning can be influenced by a variety of factors. A growing body of literature suggests a significant influence of sleep on speech learning, i.e., those trained in the evening outperform those trained in the morning most probably due to consolidation of learning that happens during the sleep for the evening group. Since, learning, in general, may be a process that spans multiple sessions, in the current exploratory study, we aimed at investigating the effect of a multi-session training paradigm on the learning performance of the morning vs evening group. We compared young adults who were trained in the morning (8–10 am; n = 16) with those who were trained in the evening (6–8 pm; n = 16) on a Hindi dental-retroflex pseudoword-picture association training paradigm. Overall, we found that the evening group learned to a larger extent both for the identification (on trained items) and discrimination (on untrained items) tasks. The current findings, even with a multi-session paradigm, are consistent with the previous findings that support enhanced performance by training in the evening. These findings may have clinical implications toward scheduling of speech therapy. [ABSTRACT FROM AUTHOR]

In contrast to the wheel‐track trains, where the motor characteristics are considered a constant value, the motor characteristics of the medium‐speed maglev (MSM) trains are the dependent variable of the position. This article studies the integration of traction power control and train control for the MSM to minimize energy consumption. First, an innovative integrated energy‐efficient optimization model for MSM train control is constructed, considering the characteristics of the linear motors. Then, a multi‐level dynamic programming (DP) approach, which includes the train operation simulation with the linear motor, is developed to solve the optimization problem. Furthermore, a speed‐up strategy for the DP approach is proposed by a pre‐calculated target train speed band (TTSB). The results of numerical experiments show that the DP approach yields a more practical train speed profile. In contrast, the DP approach with the TTSB strategy can achieve a better trade‐off between solution accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two‐objective train operation optimization based on eco‐driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware‐in‐the‐loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow. [ABSTRACT FROM AUTHOR]

The couple between the power network and the transportation network (TN) is deepening gradually with the increasing penetration rate of electric vehicles (EV), which also poses a great challenge to the traditional voltage control scheme. In this paper, we propose a coordinated voltage control strategy for the active distribution networks considering multiple types of EV. In the first stage, the action of on-load tap changer and capacitor banks, etc., are determined by optimal power f low calculation, and the node electricity price is also determined based on dynamic time-of-use tariff mechanism. In the second stage, multiple operating scenarios of multiple types of EVs such as cabs, private cars and buses are considered, and the scheduling results of each EV are solved by building an optimization model based on constraints such as queuing theory, Floyd-Warshall algorithm and traffic flow information. In the third stage, the output power of photovoltaic and energy storage systems is fine-tuned in the normal control mode. The charging power of EVs is also regulated in the emergency control mode to reduce the voltage deviation, and the amount of regulation is calculated based on the fair voltage control mode of EVs. Finally, we test the modified IEEE 33-bus distribution system coupled with the 24-bus Beijing TN. The simulation results show that the proposed scheme can mitigate voltage violations well. [ABSTRACT FROM AUTHOR]

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems. [ABSTRACT FROM AUTHOR]

Optimizing resource utilization and train scheduling is essential to satisfy passengers and reduce operating costs. This study develops the train schedule under scenario-oriented stochastic conditions. The proposed approach is a multi-objective mathematical-based mixed integer linear programming (MILP) approach; the objective is to minimize the average passenger expectation and the total number of train operation cycles. The non-dominated sorting genetic algorithm (NSGA-II) has been developed with multi-crossover and multi-mutation operators, then hybrid with simulating annealing (SA) operator (NSGA-II-SA). The model with four meta-heuristic algorithms has been technically analyzed. In a case study, the train schedule in the double-track rail network of the Tehran–Mashhad railway of Iran has been compared with the golden point. Experimental results show that a proposed approach can suitably fit the problem considering important metrics with an improvement of %7.34 and %6.89 for the average passenger waiting time and the total number of train operation cycles, respectively. [ABSTRACT FROM AUTHOR]