In railway traffic systems, whenever disturbances occur, it is important to effectively reschedule trains while optimizing the goals of various stakeholders. Algorithms can provide significant benefits to support the traffic controllers in train resc...
Railroad daily operations can be significantly disrupted by unforeseen events such as adverse weather conditions and train accidents. These events often result in delays in delivering shipments on time. Therefore, studying how to reschedule freight t...
The disturbance of local areas with complex railway networks and high traffic density not only impedes the efficient use of rail networks in those areas, but also propagates delays to the entire railway network. This has motivated research on train r...
With the continuous development of the Automatic Train Operation (ATO) system in high-speed railways, automatic driving is progressively supplanting manual operations, ushering in a new era of predictability and reliability for high-speed railway tra...
Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the in...
This study discusses the issue of train rescheduling in high-speed railways (HSR) when unexpected interruptions occur. These interruptions can lead to delays, cancellations, and disruptions to passenger travel. An optimization model for train resched...
The goal of train re-scheduling is redesigning the time when trains arrive at and depart from stations of a railway section, and train control problem refers to determining the operating mode for a train in a railway section. It is quite necessary to...
Out-of-gauge trains are trains with loading freight that exceeds the loading limitation border. Considering collision avoidance, the out-of-gauge trains have speed restriction of their own, and the trains running on the parallel track. Therefore, it...
This paper studies the train rescheduling problem on high-speed railway corridor in the situation where contingencies occur and lead to sudden deceleration of some trains. First, we develop an adaptive rescheduling strategy (AR-S) which allows normal...
This paper develops a multi-objective mixed-integer linear programming model for the problem of robust rescheduling for capacitated urban rail transit (URT) trains to serve passengers from delayed high-speed railway (HSR) trains. The capacity of each...
The adjustment of rail traffic in the event of an electrical infrastructure disruption presents an important decision-making process for the smooth operation of the network. Railway systems are complex, and their analysis relies on expensive simulati...
Comparing to other modes of transportation, high-speed railway has the advantages of energy saving, environment friendly, safety and convenience for passengers, and has been more and more popular. However, unforeseen emergencies may disrupt the norma...
The cross-line operation mode, based on interoperability technology, is becoming increasingly common in urban rail transits (URTs). Compared to trains running on a single line, cross-line trains can greatly facilitate transfer passengers. Taking the...
A rescheduling algorithm for trains on a single-track railway was developed in case of disturbances that would cause conflicts between trains. This algorithm is based on mixed integer linear programming (MILP) with speed-up routines. The model consid...
For high-speed railway operations at the network level, unforeseen events that lead to operation interruptions are inevitable, which should be handled within a short period of time to reduce the influence of the events as much as possible. This paper...
Reinforcement learning has potential in the area of intelligent transportation due to its generality and real-time feature. The Q-learning algorithm, which is an early proposed algorithm, has its own merits to solve the train timetable rescheduling (...
Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typi...
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 incor...
This work considered a joint problem of train rescheduling and closure planning. The derivation of a new train run schedule and the determination of a closure plan not only must guarantee the satisfaction of all the given constraints but also must op...
Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solvi...
In high-speed railway networks, multiple spatiotemporal correlated disruptions often cause passenger trip failures and delay propagation. Conventional single-disruption rescheduling strategies struggle to resolve such cross-line conflicts, necessitat...
The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. F...
Unpredictable sudden disturbances such as machine failure, processing time lag, and order changes increase the deviation between actual production and the planned schedule, seriously affecting production efficiency. This phenomenon is particularly se...
Enterprise production is often interfered with by internal and external factors, resulting in the infeasible original production scheduling scheme. In terms of this issue, it is necessary to quickly decide the optimal production scheduling scheme aft...
The crew re-scheduling problem (CRSP) is a critical challenge in the operation and management of urban rail transit (URT) systems, especially when restoring service after disruptions. When a crew member unexpectedly leaves duty due to emergency event...
Airport slot rescheduling is a complex sequential decision-making problem in which operational disruptions, limited airport capacity, and stringent feasibility constraints require timely rescheduling decisions. While conventional optimization techniq...
When flexible job shop scheduling faces concurrent disturbances such as machine failures and rush orders, worker-centric constraints emphasized under Industry 5.0 must also be satisfied. Existing deep reinforcement learning methods for the Dynamic Fl...
Industrial IoT maintenance often stops at health prediction, leaving maintenance, rescheduling, safety, and communication to separate decision processes. This study presents MIRA, a safety-constrained graph-based multi-agent reinforcement learning ar...
Rescheduling is often needed when trains stay in segments or stations longer than specified in the timetable due to disturbances. Under crowded situations, it is more challenging to return to normal with heavy passenger flow. Considering making a tra...
In China, cross-track high-speed trains (CTHSTs) play an important role in railway passenger transportation, with an increasing number of cross-track passengers sourced from the expansion of high-speed railway (HSR) network. The CTHST generally has l...
In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is pro...
24 May 2020
Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, wh...
This paper presents an integrated mathematical model to improve the safety and operational efficiency of train traffic in centralised railway dispatching systems. The proposed approach combines the alternative graph model with a Mealy automaton to sy...
To address the issue of scheduling failure in carrier-based aircraft support operations under complex sea conditions caused by resource performance degradation and stochastic environmental disturbances, this paper proposes a robust and adaptive sched...
Hot Rolling Production Scheduling (HRPS) is a crucial combinatorial optimization problem characterized by severe conflicts between rigid physical rolling rules and strict order due dates. While real-time scheduling is essential for dynamic manufactur...
Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized an...
Nighttime ground-based astronomical observations are often hindered by unpredictable cloud cover, which significantly reduces observing efficiency and complicates manual schedule adjustments. We present a deep learning-based method that automatically...
Previous research focuses on approaches of deep reinforcement learning (DRL) to optimize diverse types of the single-objective dynamic flexible job shop scheduling problem (DFJSP), e.g., energy consumption, earliness and tardiness penalty and machine...
Background and Objectives: Healthcare facilities are complex systems due to the interaction between different factors (human, environmental, management, and technological). As complexity increases, it is known that the possibility of error increases;...
Short-horizon emergency re-optimization of airport apron ground-support operations works when its objective carries timing stability, and backfires when it does not. Using a new benchmark—89 days of Amsterdam and Chicago ADS-B ground traces inc...
Diffusion models are among the most common techniques used for image generation, having achieved state-of-the-art performance by implementing auto-regressive algorithms. However, multi-step inference processes are typically slow and require extensive...
In modern power systems, the rapid growth of renewable energy capacity, such as wind and solar photovoltaic (PV) power, has led to a decline in system equivalent inertia and primary frequency regulation margin. At the same time, net load fluctuations...