Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can significantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864,000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.
In dynamic environments, rescheduling, or reoptimization @cite_19 becomes a critical function for dealing with unpredictable disturbances. It usually raises the additional constraints of minimizing reoptimization time as well as the distance between the initial optimization solution and the one of reoptimization, e.g. dynamic rescheduling in manufacturing systems @cite_30 or shift rescheduling @cite_19 . For instance, a two-level holarchy is earlier adopted to combine global optimization scheduling with fast rescheduling when dynamic system disturbances occur @cite_19 . In contrast, the holarchic schemes of this paper do not undermine the learning performance, while adding resilience by localizing the learning process without reinitiating it.
@cite_30 @cite_19
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This paper considers the rescheduling of operations with release dates and multiple resources when disruptions prevent the use of a preplanned schedule. The overall strategy is to follow the preschedule until a disruption occurs. After a disruption, part of the schedule is reconstructed to match up with the preschedule at some future time. Conditions are given for the optimality of this approach. A practical implementation is compared with the alternatives of preplanned static scheduling and myopic dynamic scheduling. A set of practical test problems demonstrates the advantages of the matchup approach. We also explore the solution of the matchup scheduling problem and show the advantages of an integer programming approach for allocating resources to jobs. Abstract This paper addresses the problem of updating a machine schedule when either a random or an anticipated disruption occurs after a subset of the jobs has been processed. In such cases, continuing with the original schedule is likely to be suboptimal and may not even be feasible. The approach taken here differs from most rescheduling analysis in that the cost associated with the deviation between the original and the new schedule is included in the model. We concentrate on cases in which the shortest processing time (SPT) rule is optimal for the original problem. Both single and parallel two-machine environments are considered. During rail operations, unforeseen events may cause timetable perturbations, which ask for the capability of traffic management systems to reschedule trains and to restore the timetable feasibility. Based on an accurate monitoring of train positions and speeds, potential conflicting routes can be predicted in advance and resolved in real time. The adjusted targets (location-time-speed) would be then communicated to the relevant trains by which drivers should be able to anticipate the changed traffic circumstances and adjust the train's speed accordingly. We adopt a detailed alternative graph model for the train dispatching problem. Conflicts between different trains are effectively detected and solved. Adopting the blocking time model, we ascertain whether a safe distance headway between trains is respected, and we also consider speed coordination issues among consecutive trains. An iterative rescheduling procedure provides an acceptable speed profile for each train over the intended time horizon. After a finite number of iterations, the final solution is a conflict-free schedule that respects the signaling and safety constraints. A computational study based on a hourly cyclical timetable of the Schiphol railway network has been carried out. Our automated dispatching system provides better solutions in terms of delay minimization when compared to dispatching rules that can be adopted by a human traffic controller This paper studies a dynamic dial-a-ride problem bearing complex constraints on a time-dependent network. A flexible scheduling scheme is proposed to dynamically cope with different stochastic events, such as the travelling time fluctuation, new requests, absences of customers, vehicle breakdowns, cancellations of requests, traffic jams and so on. A fast heuristic is proposed to re-optimize the schedule when a new event occurs. This heuristic consists of a properly organized local search strategy and uses a secondary objective function to drive the search out of local optima. Intensive computational simulations were carried out to evaluate the performance of this scheduling scheme and the influence of different stochastic factors. The simulation results of different scenarios with different percentage of dynamic requests reveal that this scheduling scheme can generate high quality schedules and is capable of coping with various stochastic events.