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On a daily basis, large-scale disruptions require infrastructure managers and railway operators to reschedule their railway timetables together with their rolling stock and crew schedules. This research focuses on timetable rescheduling for passenger train services on a macroscopic level in a railway network. An integer linear programming model is formulated for solving the timetable rescheduling problem, which minimizes the number of cancelled and delayed train services while adhering to infrastructure and rolling stock capacity constraints. The possibility of rerouting train services to reduce the number of cancelled and delayed train services is also considered. In addition, all stages of the disruption management process (from the start of the disruption to the time the normal situation is restored) are taken into account. Computational tests of the described model on a heavily used part of the Dutch railway network show that the model is able to find optimal solutions in short computation times. This makes the approach applicable for use in practice.

To focus on critical care needs of coronavirus patients, elective operations were postponed and selectively rescheduled. The effect of these measures on patients was unknown. We sought to understand patients’ perspectives regarding surgical care during the CoVID-19 pandemic to improve future responses. We performed qualitative interviews with patients whose operations were postponed. Interviews explored patient responses to: 1) surgery postponement; 2) experience of surgery; 3) impacts of rescheduling/postponement on emotional/physical health; 4) identifying areas of improvement. Interviews were recorded, transcribed, coded, and analyzed through an integrated approach. Patient perspectives fell within the following domains: 1) reactions to surgery postponement/rescheduling; 2) experience of surgery during CoVID-19 pandemic; 3) reflections on communication; 4) patient trust in surgeons and healthcare. We found no patient-reported barriers to rescheduling surgery. Several areas of care which could be improved (communication). There was an unexpected sense of trust in surgeons and the hospital. •Patients feel safe and comfortable having surgery during the CoVID-19 pandemic.•Patients desire more frequent and detailed communication surrounding surgical care.•Patients report a high level of trust in their surgeons and healthcare.

In this article the scheduling problem of dynamic hybrid flow shop with uncertain processing time is investigated and an ant colony algorithm based rescheduling approach is proposed. In order to reduce the rescheduling frequency the concept of due date deviation is introduced, according to which a rolling horizon driven strategy is specially designed. Considering the importance of computational efficiency in the dynamic environment, the traditional ant colony optimization is improved. On the one hand, a strategy of available routes compression to restrict ants’ movement is proposed so that the ants’ searching cycle for new solutions could be shorten. On the other hand, illuminating function in state transfer possibility is improved to facilitate the exploration of low pheromone trail. Performance of rolling horizon procedure and rescheduling algorithm are evaluated respectively through simulations, the results show the best parameters of rolling horizon procedure and demonstrate the feasibility and efficiency of rescheduling algorithm. An example from the practical production is addressed to verify the effectiveness of the proposed approach.

In this paper, we investigate the impact of rescheduling policies in the event of both rework and reconditioning in job shop manufacturing systems. Since these events occur in unplanned and disrupting manner, to address them properly, it is required to manage real-time information and to have flexible reaction capacity. These capabilities, of data acquisition and robotics, are provided by Industry 4.0 Technologies. However, to take full advantage of those capabilities, it is imperative to have efficient decision-making processes to deliver adequate corrective actions. In this sense, we propose an event-driven rescheduling approach. This approach consists of an architecture that integrates information acquisition, optimization process, and rescheduling planning. We study the performance of the system with several algorithms with two performance criteria, namely, (i) relative performance deviation (RPD) in terms of objective function and (ii) schedule stability. We also propose a hybrid policy that combines full rescheduling regeneration with stability-oriented strategies aimed to balance both criteria. We conducted extensive computational tests with instances from the literature under different scenarios. The results show that a sophisticated algorithm can obtain better quality schedules in terms of the objective function but at the expense of sacrificing stability. Finally, we analyze and discuss the results and provide insights for its use and implementation.

Generally, during the execution of the daily activity schedule, there is a mismatch between the plan and the reality. Faced with unexpected events, which affect the schedule, individuals need to reschedule their activities. In such situations, time is a crucial factor when rescheduling, as people feel time pressure because of the time constraints. Consequently, the rescheduling decision is made under the individual’s perceived time pressure ( P T P ). P T P does depend on not only the actual time pressure but also the individual’s characteristics. This paper aims to establish a model to simulate the individual decision behavior under P T P . Under different levels of P T P , individuals will choose different strategies to make the final decision based on their own characteristics. Our model proposes three decision strategies: optimal strategy under low-level P T P , salient strategy under medium-level P T P , and experience under high-level P T P . In addition, this paper argues that the choice probabilities within each strategy are affected by the empirical probabilities. The proposed strategy model for individuals’ rescheduling choices under P T P is validated by running several experiments.

In this paper we describe a real-time rolling stock rescheduling model for disruption management of passenger railways. Large-scale disruptions, e.g., due to malfunctioning infrastructure or rolling stock, usually result in the cancellation of train services. As a consequence, the passenger flows change, because passengers will look for alternative routes to get to their destinations. Our model takes these dynamic passenger flows into account. This is in contrast with most traditional rolling stock rescheduling models that consider the passenger flows either as static or as given input. Furthermore, we describe an iterative heuristic for solving the rolling stock rescheduling model with dynamic passenger flows. The model and the heuristic were tested on realistic problem instances of Netherlands Railways, the major operator of passenger trains in the Netherlands. The computational results show that the average delay of the passengers can be reduced significantly by taking into account the dynamic behavior of the passenger flows on the detour routes, and that the computation times of the iterative heuristic are appropriate for an application in real-time disruption management.

This chapter provides an in-depth discussion of the legal and regulatory frameworks surrounding cannabis in the United States, including federal law-as dictated by the Controlled Substances Act (CSA) and governed by various federal agencies like the FDA and DEA-as well as state law-as regulated by each state's laws and regulations authorizing medical and/or adult use cannabis. First, the chapter discusses the definition and classification of cannabis under the CSA, including scheduling under the CSA as well as the process for and potentiality of removing cannabis from Schedule I. Then, it describes the activities relating to industrial hemp that are permitted under the 2014 and 2018 Farm Bill. Next, the chapter addresses state-level cannabis laws. The chapter also analyzes the question of whether state cannabis laws are invalidated and superseded by federal law. Moreover, this section examines the factors underlying the extent of the Department of Justice's enforcement actions relating to state-authorized cannabis activities. The chapter then turns to CBD (cannabidiol) in particular, discussing CBD's legal status under the CSA; the FDA's role in regulating and approving CBD products for medical purposes; and the steps required to take an investigational CBD product through that approval process. The chapter concludes by contending that, while cannabis has had a long and twisting history, and although cannabis-derived products face daunting obstacles to achieving FDA approval as well as rescheduling under both federal and state law, the recent success of one product (Epidiolex ) should inspire other manufacturers to develop additional cannabis-derived products through the FDA process.

There is a manufacturing system where several parts are processed through machining workstations and later assembled to form final products. In the event of disruptions such as machine failure, the original flow-shop schedule needs to be revised and/or rescheduled. In such a scenario, rescheduling methods based on right-shift rescheduling and affected operations rescheduling work very well. Here in this study, the deviation of the make-span of the revised schedule from the original schedule is used as a performance measure. We have proposed three rescheduling methods. There are multiple factors that influence the performance of the rescheduling methodology. One of them is the make-span deviation of the schedule, and the factors influencing it are optimality of the initial solution, failure duration, deviation of make-span, rescheduling method, size, and instant of failure. The initial schedule and problem size depend on the flow-shop manufacturing system for which scheduling is performed, but the method of rescheduling depends on the decision as to which rescheduling methodology is to be selected. Computations are performed using full factorial experimentation. We also observed that right-shift rescheduling is the preferred rescheduling method in the majority of situations. In contrast, the affected operation rescheduling method is also equally suitable when the initial solution is created using modified bottleneck minimum idleness.

In this paper, a rescheduling approach based on genetic algorithm (GA) for solving the flexible scheduling problem subject to machine breakdown is proposed. In the proposed approach, event-driven rolling horizon rescheduling policy is employed to trigger the rescheduling procedure. Computational experiments are conducted on several benchmark data to prove the performance of the proposed approach. The results show that the proposed approach combines the rescheduling strategies of right-shift rescheduling and routing changing rescheduling to optimize the robustness and stability of rescheduling solution simultaneously.

This study solves the train timetable rescheduling (TTR) problem from a brand-new perspective. We assume that train traffic controllers take three main actions, i.e., adjusting dwelling times, running times, and train orders, to reschedule the timetable in real-time dispatching. To raise the interpretability of rescheduling models, we propose a graph neural network (GNN) approach to map the train timetable data into evolution graphs that fit the paradigm of train operation processes. Based on graphs, two experiments from node and edge perspectives were investigated based on train operation data, i.e., (1) node experiment: train dwelling times and running times are predicted; and (2) edge experiment: an algorithm based on evolution graph, called overtaking identification algorithm (OIA), is proposed to identify train overtaking based on the consequences of the node experiment. Timetable rescheduling solutions are obtained by integrating the GNN, OIA, and train operation constraints. Experimental results show that the proposed approach has a satisfactory predictive performance. Timetable rescheduling cases under diverse delay scenarios are examined, showing that the proposed method is superior to other three standard rule-based benchmarks regarding train delays of the disturbed train groups under the given scenarios. Additionally, the model exhibits high efficiency in the three timetable rescheduling scenarios, demonstrating the model’s applicability in real-time train dispatching.