Efficient routing on complex networks

In this letter, we propose a new routing strategy to improve the transportation efficiency on complex networks. Instead of using the routing strategy for shortest path, we give a generalized routing algorithm to find the so-called {\it efficient path}, which considers the possible congestion in the nodes along actual paths. Since the nodes with largest degree are very susceptible to traffic congestion, an effective way to improve traffic and control congestion, as our new strategy, can be as redistributing traffic load in central nodes to other non-central nodes. Simulation results indicate that the network capability in processing traffic is improved more than 10 times by optimizing the efficient path, which is in good agreement with the analysis.Comment: 4 pages, 4 figure

Rescheduling frequency in an FMS with uncertain processing times and unreliable machines

Cataloged from PDF version of article.This paper studies the scheduling/rescheduling problem in a multi-resource FMS environment. Several reactive scheduling policies are proposed to address the effects of machine breakdowns and processing time variations. Both off-line and on-line scheduling methods are tested under a variety of experimental conditions. The performance of the system is measured for mean tardiness and makespan criteria. The relationships between scheduling frequency and other scheduling factors are investigated. The results indicated that a periodic response with an appropriate period length would be sufficient to cope with interruptions. It was also observed that machine breakdowns have more significant impact on the system performance than processing time variations. In addition, dispatching rules were found to be more robust to interruptions than the optimum-seeking off-line scheduling algorithm. A comprehensive bibliography is also included in the paper

Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

The present era demands the efficient modelling of any manufacturing system to enable it to cope with unforeseen situations on the shop floor. One of the complex issues affecting the performance of manufacturing systems is the scheduling of part types. In this paper, the authors have attempted to overcome the impact of uncertainties such as machine breakdowns, deadlocks, etc., by inserting slack that can absorb these disruptions without affecting the other scheduled activities. The impact of the flexibilities in this scenario is also investigated. The objective functions have been formulated in such a manner that a better trade-off between the uncertainties and flexibilities can be established. Consideration of automated guided vehicles (AGVs) in this scenario helps in the loading or unloading of part types in a better manner. In the recent past, a comprehensive literature survey revealed the supremacy of random search algorithms in evaluating the performance of these types of dynamic manufacturing system. The authors have used a metaheuristic known as the quick convergence simulated annealing (QCSA) algorithm, and employed it to resolve the dynamic manufacturing scenario. The metaheuristic encompasses a Cauchy distribution function as a probability function that helps in escaping the local minima in a better manner. Various machine breakdown scenarios are generated. A ‘heuristic gap’ is measured, and it indicates the effectiveness of the performance of the proposed methodology with the varying problem complexities. Statistical validation is also carried out, which helps in authenticating the effectiveness of the proposed approach. The efficacy of the proposed approach is also compared with deterministic priority rules

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

CONE

Memory protection

Accidental overwriting of files or of memory regions belonging to other programs, browsing of personal files by superusers, Trojan horses, and viruses are examples of breakdowns in workstations and personal computers that would be significantly reduced by memory protection. Memory protection is the capability of an operating system and supporting hardware to delimit segments of memory, to control whether segments can be read from or written into, and to confine accesses of a program to its segments alone. The absence of memory protection in many operating systems today is the result of a bias toward a narrow definition of performance as maximum instruction-execution rate. A broader definition, including the time to get the job done, makes clear that cost of recovery from memory interference errors reduces expected performance. The mechanisms of memory protection are well understood, powerful, efficient, and elegant. They add to performance in the broad sense without reducing instruction execution rate

Demystifying reinforcement learning approaches for production scheduling

Recent years has seen a sharp rise in interest pertaining to Reinforcement Learning (RL) approaches for production scheduling. This is because RL is seen as a an advantageous compromise between the two most typical scheduling solution approaches, namely priority rules and exact approaches. However, there are many variations of both production scheduling problems and RL solutions. Additionally, the RL production scheduling literature is characterized by a lack of standardization, which leads to the field being shrouded in mysticism. The burden of showcasing the exact situations where RL outshines other approaches still lies with the research community. To pave the way towards this goal, we make the following four contributions to the scientific community, aiding in the process of RL demystification. First, we develop a standardization framework for RL scheduling approaches using a comprehensive literature review as a conduit. Secondly, we design and implement FabricatioRL, an open-source benchmarking simulation framework for production scheduling covering a vast array of scheduling problems and ensuring experiment reproducibility. Thirdly, we create a set of baseline scheduling algorithms sharing some of the RL advantages. The set of RL-competitive algorithms consists of a Constraint Programming (CP) meta-heuristic developed by us, CP3, and two simulation-based approaches namely a novel approach we call Simulation Search and Monte Carlo Tree Search. Fourth and finally, we use FabricatioRL to build two benchmarking instances for two popular stochastic production scheduling problems, and run fully reproducible experiments on them, pitting Double Deep Q Networks (DDQN) and AlphaGo Zero (AZ) against the chosen baselines and priority rules. Our results show that AZ manages to marginally outperform priority rules and DDQN, but fails to outperform our competitive baselines

VANET Connectivity Analysis

Vehicular Ad Hoc Networks (VANETs) are a peculiar subclass of mobile ad hoc networks that raise a number of technical challenges, notably from the point of view of their mobility models. In this paper, we provide a thorough analysis of the connectivity of such networks by leveraging on well-known results of percolation theory. By means of simulations, we study the influence of a number of parameters, including vehicle density, proportion of equipped vehicles, and radio communication range. We also study the influence of traffic lights and roadside units. Our results provide insights on the behavior of connectivity. We believe this paper to be a valuable framework to assess the feasibility and performance of future applications relying on vehicular connectivity in urban scenarios