Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods.

In semiconductor manufacturing, chamber cleaning operations (CCOs) are performed from time to time to clear particles and chemical gases that remain in the processing chambers of cluster tools (CTs) to ensure wafer quality. For single-armed CTs with just one CCO that could be completed in one system cycle time, researchers have proposed several scheduling approaches. In practice, more than one CCO may be required concurrently, thus failing the existing ones under such cases. Furthermore, the duration of a cleaning operation may be larger than one system cycle time, which highly complicates the scheduling of such tools. This work addresses this challenge by proposing several scheduling approaches and algorithms for single-armed CTs with several CCOs, each of which exceeds the duration of one system cycle. Suggestions are given for engineers in semiconductor fabrication plants to take the most suitable scheduling approach and algorithm for enhancing the productivity of CTs given their specific scenarios. Finally, two illustrative examples are provided to showcase the power of the proposed concepts and techniques.

With the development of robotics and Internet of Things, robot-assisted goods-to-person order picking systems become popular in smart warehouses. Order picking in such systems is a human-robot collaborative process, where robots carry pods to a picking station with human pickers who pick the demanded goods from them to fulfill orders. In it, pod selection, robot scheduling, and manual picking are highly coupled and together influence the efficiency of order picking. Their joint optimization is the key to enhancing operational efficiency but rarely studied in existing work. In order to fill such a research gap and meet high market demand, this work focuses on a novel human-robot collaborative order picking optimization problem. A mixed integer program is formulated to model it and provide an exact solution method for small-scale instances. To provide large-scale problems with efficient solutions in practical application scenarios, we propose an adaptive large-neighborhood-based tabu search algorithm. Specifically, an adaptive large neighborhood search method is designed and embedded into a tabu search algorithm with two tabu mechanisms. Experimental results indicate that the presented algorithm has significant advantages in solving the newly proposed problem. It substantially outperforms: 1) the independent use of adaptive large neighborhood search or tabu search, 2) Gurobi subject to an hour execution time, and 3) several competitive benchmark and newest well-performing algorithms. Its high performance implies its great potential in solving practical order picking optimization problems for Internet-of-Things-enabled robot-assisted smart warehouses.

With the rapid development of service computing, the demand for service recommendation is increasing. Quality of Service (QoS) prediction has been one of the key challenges for service recommendation. Existing deep learning-based methods have been proposed for QoS prediction, but further improvement of their neural network structures is still needed to improve the prediction accuracy. This work introduces multi-stage multi-scale feature fusion with individual evaluations to a deep learning model for accurate QoS prediction. In our model, non-negative matrix factorization is used to extract three-scale (i.e., global, local, and individual) features; distance similarity is exploited to find similar users and services; a multi-stage deep neural network is designed to fuse multi-scale features, where individual evaluations are input to each stage to correct QoS features. Finally, our model is compared with often-cited prediction methods, and the experimental results show that it can more accurately predict QoS than its peers.Note to Practitioners—Accurate QoS prediction is very helpful to recommend the most suitable services to users among many similar services. Affected by the sparsity of historical data, the accuracy of existing QoS prediction methods is often limited. The multi-scale features of users and services can be used to improve prediction accuracy. This work proposes a new QoS prediction method to do so. Specifically, it first extracts global and individual features through non-negative matrix factorization and uses distance similarity to obtain local features. Then, it proposes a new deep neural network that fuses the extracted multi-scale features in each learning stage, thereby improving QoS prediction for services recommendation.

To study the asymmetric jump behaviors of the stock markets, we propose a novel autoregressive conditional jump intensity (ARJI)—generalized autoregressive conditional heteroskedasticity (GARCH) model with a Markov chain. Compared with the existing models, it considers the asymmetric effects of the positive and negative shocks on jump volatilities. It is proposed to estimate the asymmetric jump volatilities of the stock markets in mainland China and Hong Kong under different volatility regimes. Multiple time-varying copula models are used to analyze the dynamic dependences of the jump risks between the two markets. Furthermore, we construct dynamic hedging portfolios for their spot and futures markets, estimate the minimum risk hedging ratios, and measure the hedging performance. Compared with other benchmark models, the results show that the proposed one has the best fitting effect for the Chinese stock markets. The correlations between the Chinese mainland and Hong Kong markets are always positive. When constructing hedging portfolios, the proposed model is superior to other models, which means that introducing asymmetric shocks on both normal and jump volatilities into a Markovian ARJI-GARCH model can effectively improve the performance of hedging portfolios. In addition, the results of the robustness test indicates that our proposed model performs well and is robust.

Feature selection (FS) is an essential technique widely applied in data mining. Recent studies have shown that evolutionary computing (EC) is very promising for FS due to its powerful search capability. However, most existing EC-based FS methods use a length-fixed encoding to represent feature subsets. This inflexible encoding turns ineffective when high-dimension data are handled, because it results in a huge search space, as well as a large amount of training time and memory overhead. In this article, we propose a length-adaptive genetic algorithm with Markov blanket (LAGAM), which adopts a length-variable individual encoding and enables individuals to evolve in their own search space. In LAGAM, features are rearranged decreasingly based on their relevance, and an adaptive length changing operator is introduced, which extends or shortens an individual to guide it to explore in a better search space. Local search based on Markov blanket (MB) is embedded to further improve individuals. Experiments are conducted on 12 high-dimensional datasets and results reveal that LAGAM performs better than existing methods. Specifically, it achieves a higher classification accuracy by using fewer features.

This work studies perishable products’ distribution using electric commercial vehicles (ECVs). Extra energy is consumed for refrigeration to keep such products from deteriorating, which shortens the limited driving range of ECVs. Besides charging at public recharging stations, their travel speed can be adjusted to improve their driving range and decrease distribution cost. We propose an Electric Vehicle Routing problem with variable vehicle speed and soft time windows for perishable products (EVRP-VS). An energy consumption rate function of refrigerated ECVs during driving is introduced into the problem. The function considers refrigeration and has a nonlinear relationship with ECV speed and weight. As long as the carriage is not empty, the vehicle needs refrigeration during driving and during its stay at customers and stations. A mathematical programming model is developed for EVRP-VS, to minimize total distribution cost, including vehicle cost, power cost, refrigeration cost, and penalty cost due to delayed delivery. An adaptive hybrid ant colony optimization (AHACO) with a two-stage speed optimization strategy is proposed to solve EVRP-VS. In the first stage, local speed optimization is used to optimize the speed of each ant (ECV) in each transfer step. In the second one, global speed optimization further optimizes the speed for each fixed route constructed by ants. AHACO is applied to many problem instances. Experimental results show that it can effectively solve EVRP-VS in comparison with CPLEX and other meta-heuristics.

In a distributed heterogeneous fog environment, fog nodes may change their state at any time. Their reliability changes accordingly. A dynamic analysis of state changes can help one detect fault-tolerant fog nodes, which is conducive to promoting the reliability of fog services. This article proposes a fault-tolerant model based on a Markov chain for a fog system’s performance optimization. The real-time reliability of fog nodes is analyzed by using dynamic distributed parameters. Thus, the state transition process of fog nodes is modeled with a continuous-time Markov chain. The steady-state probability of a fog system is analyzed. Then, a fault-tolerant strategy and its algorithms are designed to select nodes with the minimum cost based on their steady-state probabilities. The proposed method can predict the number of faulty ones of a fog system via the steady-state probability. An intelligent optimization method called simulated annealing (ISA) is designed and used to select the most appropriate fog nodes to substitute faulty ones. The experimental results show that the method is feasible and effective for selecting the right fault-tolerant nodes according to different performance requirements. ISA can well outperform such methods as random selection, discrete differential evolution, and simulated annealing in terms of cost and time.

Cost-effective medium and high voltage dc-dc converters have attracted significant research interests from academia and industry in recent years due to the pivotal roles they may play in MVDC and HVDC grids. Therefore, the first part of this paper uses qualitative discussions to critically review the topologies of high-power dc-dc converters, which are more likely to be adopted in MVDC and HVDC grids. From the most promising dc-dc converters identified based on qualitative discussions, few representatives are selected for further elaboration using simulation in the second part of the paper. The presented simulations aim to substantiate the claimed attributes of few selected these representative topologies. The observations drawn from this review have been summarized and thoroughly discussed.

In this paper a novel modulation technique is proposed to eliminate instabilities such as subharmonic and chaotic oscillations in dc-dc switching converters. This modulation technique injects a stabilizing signal which is generated internally from its own state variables so that the system is free from the problems due to externally injected signal, such as frequency mismatch, phase shift, etc. Stability analysis of the system is carried out using Floquet theory taking into account its switching nature. Our results show that the system has larger stable region in the parameter space compared to the conventional modulation techniques. Numerical simulations illustrate the performance of the proposed technique under line and load disturbances.

Power electronic systems exhibit different types of fast- and slow-scale instabilities which limit the stable operating range of the parameters. It has been shown that the stability of complex power electronic systems can be fruitfully investigated using the Filippov method, where the stability of the system is given by the eigenvalues of the monodromy matrix, which is a combination of the state transition matrices through each subsystem and those across the switching events, called saltation matrix. In this paper we show that the components of the saltation matrix can be used to change the stability status of the system, and propose three specific techniques, which can be used individually or together to extend the range of stability significantly. The performance of these techniques are shown using line and load disturbances.

This paper focuses on the role of networked microgrids as distributed systems for enhancing the power system resilience against extreme events. Resilience is an intrinsically complex property which requires deep understanding of microgrid operation in order to respond effectively in emergency conditions. The paper first introduces the definition and offers a generic framework for analyzing the power system resilience. The notion that large power systems can achieve a higher level of resilience through the deployment of networked microgrids is discussed in detail. In particular, the management of networked microgrids for riding through extreme events is analyzed. In addition, the merits of advanced information and communication technologies (ICTs) in microgrid-based distributed systems that can support the power system resilience are presented. The paper also points out the challenges for expanding the role of distributed systems and concludes that networked microgrids in particular provide a universal solution for improving the resilience against extreme events in Smart Cities.

In this paper, a typical intermediate bus architecture is modeled from circuit theory and nonlinear dynamics point of view. In the studied system a regulated DC-DC buck converter in the first level supplies two parallel connected regulated buck converters in the second level through an intermediate bus. The loads connected to the second level converters are resistive. The complexity of the system is due to the interaction of the output voltage of the first converter with the downstream converters. Unlike the earlier studies where averaged or simplified reduced-order model were used only for two cascaded converters, we have done our study using the exact switching model of the DC-DC converters. This paper shows different mechanisms of instability when the parallel converters are fed through another converters instead of constant voltage. This knowledge will help in designing more reliable intermediate bus architecture for different application under different operating conditions.

In this paper, a single-inductor single-input boost-type dual-output dc-dc converter is studied. Peak current-mode control is used for the main switch whereas the output switch is controlled by voltage-mode controller. To regulate both the output voltages proportional-integral compensator is introduced. In the state-space the five-dimensional system is divided into four subsystems by four switching surfaces. Due to the large number of subsystems, Filippov's method is used to obtain the stable operating region in the design parameter space. Coexisting attractors, period-doubling and Neimark-Sacker bifurcations are identified as the major causes of the possible instabilities. Detailed studies of the possible bifurcation scenarios (smooth as well as nonsmooth) have been done by detecting the stable as well as unstable orbits and by calculating their eigenvalues.