Explore the vast range of titles available.

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.

The plant circadian clock evolved to increase fitness by synchronizing physiological processes with environmental oscillations. Crop fitness was artificially selected through domestication and breeding, and the circadian clock was identified by both natural and artificial selections as a key to improved fitness. Despite progress in Arabidopsis, our understanding of the crop circadian clock is still limited, impeding its rational improvement for enhanced fitness. To unveil the interactions between the crop circadian clock and various environmental cues, we comprehensively mapped abiotic stress inputs to the soybean circadian clock using a 2-module discovery pipeline. Using the “molecular timetable” method, we computationally surveyed publicly available abiotic stress-related soybean transcriptomes to identify stresses that have strong impacts on the global rhythm. These findings were then experimentally confirmed using a multiplexed RNA sequencing technology. Specific clock components modulated by each stress were further identified. This comprehensive mapping uncovered inputs to the plant circadian clock such as alkaline stress. Moreover, short-term iron deficiency targeted different clock components in soybean and Arabidopsis and thus had opposite effects on the clocks of these 2 species. Comparing soybean varieties with different iron uptake efficiencies suggests that phase modulation might be a mechanism to alleviate iron deficiency symptoms in soybean. These unique responses in soybean demonstrate the need to directly study crop circadian clocks. Our discovery pipeline may serve as a broadly applicable tool to facilitate these explorations.

Flight timetabling can greatly impact an airline’s operating profit, yet data-driven or model-based solutions to support it remain limited. Timetabling optimization is significantly complicated by two factors. First, it exhibits strong interdependencies with subsequent fleet assignment decisions of the airlines. Second, flights’ departure and arrival times are important determinants of passenger connection opportunities, of the attractiveness of each (nonstop or connecting) itinerary, and, in turn, of passengers’ booking decisions. Because of these complicating factors, most existing approaches rely on incremental timetabling. This paper introduces an original integrated optimization approach to comprehensive flight timetabling and fleet assignment under endogenous passenger choice. Passenger choice is captured by a discrete-choice generalized attraction model. The resulting optimization model is formulated as a mixed-integer linear program. This paper also proposes an original multiphase solution approach, which effectively combines several heuristics, to optimize the network-wide timetable of a major airline within a realistic computational budget. Using case study data from Alaska Airlines, computational results suggest that the combination of this paper’s model formulation and solution approaches can result in significant profit improvements as compared with the most advanced incremental approaches to flight timetabling. Additional computational experiments based on several extensions also demonstrate the benefits of this modeling and computational framework to support various types of strategic airline decision making in the context of frequency planning, revenue management, and postmerger integration.

Timetabling problem is known as an NP-hard problem that centres around finding an optimized allocation of subjects onto a finite available number of slots and spaces. It is perhaps the most challenging issues looked by colleges around the globe. Every academic institution faces a problem when preparing courses and exam plans. There are many restrictions raised while preparing a timetable. This paper proposed a method based on the evolutionary algorithms to solve the constrained timetable problem, which helps to create theory as well as lab schedule for universities. A smart adaptive mutation scheme is used to speed up convergence and chromosome format is also problem specific. Here in this paper two algorithms are compared in respect of Timetabling problems. Using GA (Genetic Algorithm) and MA (Memetic algorithm), we optimised the output by selecting the best solution from the available options to present a comprehensive curriculum system.

Earth observation satellites (EOS) are satellites equipped with optical sensors that orbit the Earth to take photographs of particular areas at the request of users. With the development of space technology, the number of satellites has increased continuously. Yet still, the number of satellites cannot meet the explosive growth of applications. Thus, scheduling solutions are required to satisfy requests and obtain high observation efficiency. While the literature on multi-satellite scheduling is rich, most solutions are centralized algorithms. However, due to their cost, EOS systems are often co-funded by several agents (e.g., countries, companies, or research institutes). Central solutions require that these agents share their requests for observations with others. To date, there has yet to be a solution for EOS scheduling that protects the private information of the interested parties. In this study, we model the EOS scheduling problem as a distributed constraint optimization problem (DCOP). This modeling enables the generation of timetables for the satellites in a distributed manner without a priori sharing users’ private information with some central authority. For solving the resulting DCOP, we use and compare the results of two different local search algorithms—Distributed Stochastic Algorithm and Maximum Gain Message—which are known to produce efficient solutions in a timely manner. The modeling and solving of the resulting DCOP constitute our new solution method, which we term Distributed Satellite Timetable Solver (DSTS). Experimental evaluation reveals that the DSTS method provides solutions of higher quality than a commonly used centralized greedy algorithm and is comparable to an additional centralized algorithm that we propose.

In this paper a specific algorithm of optimization of timetable of realizing projects in the framework of multi-project organization is considered. It allows to achieve the uniformity of labor costs and financial supplies (revenues). The algorithm is developed on the basis of solution of the double-transport problem.

The construction of timetable with all constrained is very difficult task. Timetable is the problem in which we assign teachers to respective course, time slots and rooms with some constraints. In this paper we apply fuzzy algorithm which satisfy all constrains by using one example.

Background Closing the gap between research production and research use is a key challenge for the health research system. Stakeholder engagement is being increasingly promoted across the board by health research funding organisations, and indeed by many researchers themselves, as an important pathway to achieving impact. This opinion piece draws on a study of stakeholder engagement in research and a systematic literature search conducted as part of the study. Main body This paper provides a short conceptualisation of stakeholder engagement, followed by ‘design principles’ that we put forward based on a combination of existing literature and new empirical insights from our recently completed longitudinal study of stakeholder engagement. The design principles for stakeholder engagement are organised into three groups, namely organisational, values and practices. The organisational principles are to clarify the objectives of stakeholder engagement; embed stakeholder engagement in a framework or model of research use; identify the necessary resources for stakeholder engagement; put in place plans for organisational learning and rewarding of effective stakeholder engagement; and to recognise that some stakeholders have the potential to play a key role. The principles relating to values are to foster shared commitment to the values and objectives of stakeholder engagement in the project team; share understanding that stakeholder engagement is often about more than individuals; encourage individual stakeholders and their organisations to value engagement; recognise potential tension between productivity and inclusion; and to generate a shared commitment to sustained and continuous stakeholder engagement. Finally, in terms of practices, the principles suggest that it is important to plan stakeholder engagement activity as part of the research programme of work; build flexibility within the research process to accommodate engagement and the outcomes of engagement; consider how input from stakeholders can be gathered systematically to meet objectives; consider how input from stakeholders can be collated, analysed and used; and to recognise that identification and involvement of stakeholders is an iterative and ongoing process. Conclusion It is anticipated that the principles will be useful in planning stakeholder engagement activity within research programmes and in monitoring and evaluating stakeholder engagement. A next step will be to address the remaining gap in the stakeholder engagement literature concerned with how we assess the impact of stakeholder engagement on research use.

Timetables are crucial for the efficient operation of public transportation. This paper tackles the timetabling problem of a regular bus line facing oversaturation due to stochastic fluctuation in demand and a lack of fleet, making it more complex than traditional undersaturated scenarios. By oversaturation, we mean some passengers may not board the first‐arrival bus and must wait for subsequent ones. We propose a reinforcement learning (RL) model based on the Markov decision process (MDP) to optimize the timetable. The objective is to minimize the expected total system costs, involving the costs of passengers and the operator. Constraints concerning vehicle capacity and fleet size are respected. The dynamics of passengers are captured in the state transition process. The proposed model is applied to the No. 8 bus route in Sanya, China. Specifically, the proximal policy optimization (PPO) algorithm is employed. Under deterministic demand, we compare the optimized timetables against the results obtained by classic models. The results show that the proposed model outperforms classical models in terms of both solution quality and computational efficiency. Under stochastic demand, as variance in demand increases, the benefit of the proposed model in the system cost and passenger waiting time becomes more significant. In addition, we optimize the fleet size, accounting for the acquisition cost of vehicles.

Both aviation and high-speed rail are developing rapidly, which promotes the popularity of intermodal services. However, most cooperation only comes from political support. Operational measures should be taken for further improvement. In such circumstance, a model is proposed in this study to improve the quality of services and strengthen the connectivity between two modes by optimizing the railway timetable. The model is set to maximize the connection numbers of two modes. To take journey is “air-rail” or “rail-air” into consideration, we divide passengers into four groups. The original model is linear and can be calculated by commercial solvers. The models were applied on a case China. The results showed that the model is effective, the connection numbers improved by 40.2%.