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In this paper, we introduce a new formulation of the multivehicle point-to-point network problem to be utilised in urban air mobility (UAM) vertiport-to-vertiport network operations. Vehicle routing problems (VRPs) and their variants have previously been studied and applied in real-world situations, but these problems require additional depot locations, and not all the vehicles can travel to all the locations. In UAM operations, additional depot locations may not be required, and all vehicles can travel to all locations, meaning that existing routing problems are not suitable for application to the management of UAMs. Therefore, we propose a new formulation for UAM vertiport-to-vertiport operation by introducing new constraints. In addition, we integrate dynamic scheduling with the flight mission by controlling cruise speed and waiting in each UAM at each vertiport location to generate an arrival and departure schedule for different vertiports that can avoid collisions and increase the number of vehicles. A computational experiment is conducted using an MILP model, and the results show that although our formulation satisfies the problem definition, the computation time increases exponentially with an increase in the problem size. A case study is conducted in the Seoul area involving five vertiports, with 10- and 15-vehicle scenarios studied. This case study shows that the cruise speed variable is active only for the lower and upper bounds under dynamic scheduling, whereas the waiting time variable can be controlled between user-defined limits that can be applied to the management of vertiport-to-vertiport UAM operations. [ABSTRACT FROM AUTHOR]

Interbay Automated Material Handling Systems (AMHS) are widely adopted especially in Semiconductor Wafer Fabrication Systems (SWFS). The dispatching method plays a major role in the control of AMHS. This paper proposes an efficient multi-objective dynamic dispatching method which will dynamically adjust vehicle-load assignments according to the real-time situation of the system. A multi-objective cost function with variable weights is established, taking into account various performance indices (i.e., transport time, throughput, cycle time, vehicle utilization, movement, and waiting time), and the corresponding mathematical model is formulated. Then, in order to obtain the suitable weights according to the real-time condition, an advanced method is developed based on fuzzy theory. After that, a Hungarian algorithm is adopted to solve the model. Finally, simulations are conducted to validate the proposed method. The results demonstrate that it has better comprehensive performance compared to the previous dispatching methods. [ABSTRACT FROM AUTHOR]

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation vehicular networks can be enhanced by incorporating vehicular edge or fog computing paradigm. However, the growing popularity and massive adoption of novel services make the edge resources insufficient. A possible solution to overcome this challenge is to employ the onboard computation resources of close vicinity vehicles that are not resource-constrained along with the edge computing resources for enabling tasks offloading service. In this paper, we investigate the problem of task offloading in a practical vehicular environment considering the mobility of the electric vehicles (EVs). We propose a novel offloading paradigm that enables EVs to offload their resource hungry computational tasks to either a roadside unit (RSU) or the nearby mobile EVs, which have no resource restrictions. Hence, we formulate a non-linear problem (NLP) to minimize the energy consumption subject to the network resources. Then, in order to solve the problem and tackle the issue of high mobility of the EVs, we propose a deep reinforcement learning (DRL) based solution to enable task offloading in EVs by finding the best power level for communication, an optimal assisting EV for EV pairing, and the optimal amount of the computation resources required to execute the task. The proposed solution minimizes the overall energy for the system which is pinnacle for EVs while meeting the requirements posed by the offloaded task. Finally, through simulation results, we demonstrate the performance of the proposed approach, which outperforms the baselines in terms of energy per task consumption. [ABSTRACT FROM AUTHOR]

Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. [ABSTRACT FROM AUTHOR]

Production scheduling is the key to manufacturing process decision support, which directly affects the efficiency and competitiveness of enterprises. The production process of discrete workshops is complex and changeable, and it is usually difficult to make adjustments quickly and accurately in response to disturbance events. In this paper, a workshop production scheduling method based on digital twin is proposed and applied to the manufacturing workshop of an aerospace factory. Combined with the advantages of real-time virtual real interaction fusion of digital twin technology, the dynamic scheduling problem under fault disturbance factors is studied. A high-fidelity digital twin workshop is established to realize the mapping and interaction between the real production and the virtual factory. Based on the vibration data of machine tool spindle, a fault prediction method of learning vector quantization neural network is proposed. The dynamic scheduling strategy of workshop production based on digital twin is constructed and compared with the scheduling results without digital twin under fault disturbance. The results show that the scheduling method based on digital twin can effectively deal with disturbances and improve workshop productivity. This study can be used for the application of digital twin and production scheduling in practical factories. [ABSTRACT FROM AUTHOR]

Modern astrophysics research has heavily relied on data measured from telescopes. Reasonable observational scheduling brings into play action on the output of highly sensitive data and utilization of telescopes. However, most existing observational scheduling methods aim to solve medium- to long-term problems with proposals as scheduled objects. Nevertheless, the Yunnan 40 m Radio Telescope (YRT40) has the requirements of short-term scheduling within a few days. In this paper, we present a time-dependent short-term observational scheduling method based on a genetic algorithm to achieve automatic scheduling of observation targets. We established a mathematical model of a multi-objective optimization with time dependence and the constraints of telescope hardware conditions with the aim of minimizing the slew time of the telescope and maximizing the scientific values of the targets. In the experiments, we adopted the actual observational targets of YRT40. The results from tests on the runtime of the telescope demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Fog Computing can facilitate the adoption of the Everything-as-a-Service paradigm in infrastructure segments that are located closer to the end user, or to the data source, compared to typical Cloud solutions. This enables combining the advantages of flexible service deployment models with the need to cope with the strict requirements–especially in terms of latency–of emerging applications in softwarized networks. Along comes the need to consider aspects of service orchestration specific to the Fog environment and its intrinsically dynamic nature. In this paper we propose an architecture for flexible Fog Computing service orchestration, with a particular focus on the awareness of service deployment models. We discuss the design choices and describe the components and operations of the proposed orchestration system. We then present a complete working implementation of such architecture, including insights on its ability to handle critical orchestration functions such as service discovery and resource monitoring. We also report on the experimental validation of the system and the performance evaluation on real-world equipment, proving the feasibility and the effectiveness of the approach on a dynamic Fog infrastructure. We complement the work by presenting the results of a combinatorial analysis, validated by simulation, of the service model-aware resource selection process. As a result of our investigation, we show that Fog services can be effectively deployed in a matter of a few seconds, or even in less than one second when suitable Fog nodes are available, taking advantage of the awareness of the available service models. [ABSTRACT FROM AUTHOR]

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research. [ABSTRACT FROM AUTHOR]

Conventional spectrum sharing approaches assume that an unlicensed user accessing a licensed spectrum band autonomously operates by sensing (or detecting) the presence of the primary user, which is licensed to use the given band, with minimal coordination from the primary user. However, in this case, incorrect sensing decisions and resource allocation on the unlicensed secondary users' (SU) part can lead to undesired degradation of the primary users' performance. Therefore, there has been a recent paradigm shift to enable cooperative spectrum sharing, in which interaction is allowed between the primary and the secondary network for efficient spectrum utilization while limiting the interference experienced by a primary user. In this paper, we consider a cooperative spectrum sharing architecture in which the SU is assumed to be a cellular network. We then propose a novel fractional-frequency-reuse-based dynamic resource allocation (DRA) algorithm at the secondary (cellular) network. The joint DRA optimization involves simultaneous spectrum allocation across different base stations and then across users within a base station. It is an NP-hard problem and is prohibitively complex to solve for a large cellular network. Therefore, to reduce the complexity of the joint problem and offer a tractable solution, we formulate layered scheduling algorithms for cross-layer resource allocation. Furthermore, the feedback overhead between the layers is kept minimal. Finally, using simulations, we compare our proposed DRA scheme with other resource allocation schemes for sum system throughput, as well as for fairness in allocation. We also present the result for the case when the available primary spectrum varies over time and locations of SUs. [ABSTRACT FROM AUTHOR]

We consider a centralized content delivery infrastructure where a large number of storage-intensive files are replicated across several collocated servers. To achieve scalable mean delays in file downloads under stochastic loads, we allow multiple servers to work together as a pooled resource to meet individual download requests. In such systems, basic questions include: How and where to replicate files? What is the impact of dynamic service allocation across request types, and whether such allocations can provide substantial gains over simpler load balancing policies? What are tradeoffs among performance, reliability and recovery costs, and energy? This paper provides a simple performance model for large systems towards addressing these basic questions. [ABSTRACT FROM AUTHOR]

These reminiscences relate to the period that Brian Randell spent between 1964 and 1966 first at IBM Research working on Project Y and then in the IBM Systems Development Division on the resulting ACS Project--then-secret projects that aimed to build a supercomputer that would be 100 times faster than Stretch. Randell's account is based in part on his memory, but also makes extensive use of the small set of files that he had retained, mainly relating to patent applications. A scanned copy of one of these files, the paper "Dynamic Instruction Scheduling," that he coauthored with Lynn Conway, Don Rozenberg, and Don Senzig in February 1966, is available as an online Web extra https://s3.amazonaws.com/ieeecs.cdn.csdl.public/mags/an/2015/03/man2015030055s.pdf and in Newcastle University's online archive at www.cs.ncl.ac.uk/publications/trs/papers/891.pdf. Because of space constraints just the initial three pages of this paper are included in the present article, which also includes the text from a section on "Interrupts" that was added to the 1969 IBM San Jose Technical Report version of the 1966 paper. [ABSTRACT FROM PUBLISHER]

In this paper, we study a simple question: when are dynamic relaying strategies essential in optimizing the diversity-multiplexing tradeoff (DMT) in half-duplex wireless relay networks? This is motivated by apparently two contrasting results even for a simple three-node network with a single half-duplex relay. When all channels in the system are assumed to be independent and identically fading, a static schedule where the relay listens half the time and transmits half the time combined with quantize-map-and-forward (QMF) relaying is known to achieve the full-duplex performance. However, when there is no direct link between the source and the destination, a dynamic decode-and-forward (DDF) strategy is needed to achieve the optimal tradeoff. In this case, a static schedule is strictly suboptimal and the optimal tradeoff is significantly worse than the full-duplex performance. In this paper, we study the general case when the direct link is neither as strong as the other links nor fully nonexistent, and identify regimes where dynamic schedules are necessary and those where static schedules are enough. We identify four qualitatively different regimes for the single-relay channel, where the tradeoff between diversity and multiplexing is significantly different. We show that in all these regimes one of the above two strategies is sufficient to achieve the optimal tradeoff by developing a new upper bound on the best achievable tradeoff under channel state information available only at the receivers. A natural next question is whether these two strategies are sufficient to achieve the DMT of more general half-duplex wireless networks with a larger number of relays. We propose a generalization of the two existing schemes through a dynamic QMF (DQMF) strategy, where the relay listens for a fraction of time depending on received channel state information but not long enough to be able to decode. We show that such a DQMF strategy is needed to achieve the optimal DMT in a parallel channel with two relays, outperforming both DDF and static QMF strategies. [ABSTRACT FROM AUTHOR]

A fundamental challenge in orthogonal-frequency-division-multiple-access (OFDMA)-based cellular networks is intercell interference coordination, and to meet this challenge, various solutions using fractional frequency reuse (FFR) have been proposed in the literature. However, most of these schemes are either static in nature, dynamic on a large time scale, or require frequent reconfiguration for event-driven changes in the environment. The significant operational cost involved can be minimized with the added functionality that self-organizing networks bring. In this paper, we propose a solution based on the center of gravity of users in each sector. This enables us to have a distributed and adaptive solution for interference coordination. We further enhance our adaptive distributed FFR scheme by employing cellular automata as a step toward achieving an emergent self-organized solution. Our proposed scheme achieves a close performance with strict FFR and better performance than SFR in terms of the edge user's sum rate. [ABSTRACT FROM PUBLISHER]

In the spectrum auction, if a buyer locates in a “critical” place, interfering with a lot of other buyers, his occupancy of the spectrum may deprive many other transmission opportunities. In this paper, we propose a Location-aware Online Truthful doUble auction Scheme (LOTUS), which incorporates the buyers' location information into auction mechanism design. In the online auction, the biggest challenge is how to allocate the spectrums based on the knowledge in the current time slot, without knowing the spectrum requests that may come afterward. To solve this problem, we propose considering the opportunity cost of allocating the spectrum to a buyer based on his local interference conditions. We introduce the “interference discount” to markdown a buyer's bid if he induces a wide range of interference. Furthermore, we take into account the spectrum heterogeneity and design mechanisms that guarantee the economic robustness of the auction. The simulation results show that LOTUS outperforms the existing online auction mechanism, significantly improving buyers' and sellers' utility. [ABSTRACT FROM PUBLISHER]

In most cloud computing platforms, the virtual machine quotas are seldom changed once initialized, although the current allocated resources are not efficiently utilized. The average utilization of cloud servers in most datacenters can be improved through virtual machine placement optimization. How to dynamically forecast the resource usage becomes a key problem. This paper proposes a scheduling algorithm called virtual machine dynamic forecast scheduling (VM-DFS) to deploy virtual machines in a cloud computing environment. In this algorithm, through analysis of historical memory consumption, the most suitable physical machine can be selected to place a virtual machine according to future consumption forecast. This paper formalizes the virtual machine placement problem as a bin-packing problem, which can be solved by the first-fit decreasing scheme. Through this method, for specific virtual machine requirements of applications, we can minimize the number of physical machines. The VM-DFS algorithm is verified through the CloudSim simulator. Our experiments are carried out on different numbers of virtual machine requests. Through analysis of the experimental results, we find that VM-DFS can save 17.08 % physical machines on the average, which outperforms most of the state-of-the-art systems. [ABSTRACT FROM AUTHOR]

This paper investigates resource provisioning in cyber-physical systems (CPSs) by developing a new definition of anelasticity. A flat semi-dormant multicontroller (FSDMC) model is established on a special type of CPS platform named arbitrated networked control system with dual communication channels. A novel, quantitative, and formal definition of anelasticity for the FSDMC is proposed. A new finite capacity M/M/c queuing system with ${N}$ -policy and asynchronous multiple working vacations of partial servers is established, and the FSDMC is modeled as a quasi-birth-and-death process to obtain the stationary probability distribution of the system. Based on the queueing model, we quantify various performance indices of the system to build a nonlinear cost-performance ratio (CPR) function. An optimization model is presented to minimize the CPR. A particle swarm optimization (PSO) algorithm is used to find the optimum solution of the optimization model and obtain the optimal configuration values of the system parameters under stability condition. By changing the system parameters, the sensitivity of the system performance indices and the CPR are analyzed, respectively. The unexpected workload varies randomly over time. Thus, an M/M/1/K queue is constructed in a Markovian environment by employing a three-state, irreducible Markov process. In this queue, the conditional average queue length and the probabilities of the three-state process are calculated. Then, the anelasticity value of the system is precisely determined. When the average arrival rate exceeds the average service rate in the queueing system, an optimal CPR unchanged adaptive algorithm based on PSO is designed to dynamically adjust the controller service rate. Extensive numerical results show the usefulness and effectiveness of the proposed techniques and exhibit that the system can maintain elastic invariance in adaptive adjustment parameters. [ABSTRACT FROM AUTHOR]

The deployment of Virtualized Network Functions is expected to be dynamic and swift when using Network Function Virtualization technology. The dynamic nature of workload from users requires the resource allocation of underlying infrastructure to be flexible to cope with the changes. Existing works investigated elastic NFV solutions by dynamically creating and dismantling Virtual Machine (VM) replicas, while maintaining balanced workload among VMs. However, those solutions are coarse-grained which may cause unnecessary resource over-provisioning as different network functions consume different amount of resources. In this paper, we present ElasticNFV, a dynamic and fine-grained cloud resource provisioning solution for VNF. ElasticNFV takes real-time resource demand of multiple service chains and allocates resources through an elastic provision mechanism. When a scaling conflict occurs, ElasticNFV provides a two-phase minimal migration algorithm to optimize the migration time and embedding cost of VNF instances. We implement ElasticNFV on top of the KVM platform to provide elastic VM for each VNF instance and Open vSwitch to form elastic intra-cloud network with virtual links between VNF instances. Our evaluation results show that ElasticNFV can improve VNF performance significantly, and achieve high resource utilization and fast migration time with low cost. [ABSTRACT FROM AUTHOR]

The undergoing transition from conventional to converter-interfaced renewable generation leads to significant challenges in maintaining frequency stability due to declining system inertia. In this paper, a novel control framework for Synthetic Inertia (SI) provision from Wind Turbines (WTs) is proposed, which eliminates the secondary frequency dip and allows the dynamics of SI from WTs to be analytically integrated into the system frequency dynamics. Furthermore, analytical system frequency constraints with SI provision from WTs are developed and incorporated into a stochastic system scheduling model, which enables the provision of SI from WTs to be dynamically optimized on a system level. Several case studies are carried out on a Great Britain 2030 power system with different penetration levels of wind generation and inclusion of frequency response requirements in order to assess the performance of the proposed model and analyze the influence of the improved SI control scheme on the potential secondary frequency dip. The results demonstrate that the inclusion of SI provision from WTs into Unit Commitment (UC) can drastically impact the overall system costs. [ABSTRACT FROM AUTHOR]

Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis. [ABSTRACT FROM AUTHOR]

The dramatic growth of mobile application usage has posed great pressure on application developers to better manage their backend capacity. Rule-based or schedule-based auto-scaling mechanisms have been proposed, but it is difficult or expensive to frequently adjust the backend capacity to track the burstiness of mobile traffic. In this paper, we explore a fundamentally different approach. Instead of scaling the backend in line with the mobile traffic, we smooth out traffic profiles to reduce the required backend capacity and increase its utilization. Our proposed solution, called Razor, is inspired by two key insights on mobile traffic. First, mobile traffic exhibits high short-term fluctuations but steady long-term trend, so that we may temporarily delay user requests and periodically adapt backend capacity based on the predicted traffic volume. Second, user requests have different priorities: while some requests are urgent (e.g., sending a message), some are delay-tolerant (e.g., changing the profile photo) and can be postponed without much influence on the user experience. Based on these observations, our design features a two-tier architecture: on a long timescale, Razor predicts future traffic using machine learning algorithms and plans the optimal backend capacity to minimize the budget with performance guarantee; on a short timescale, Razor schedules which requests to delay and by how much time to delay according to their delay tolerance. We implement a fully-functional prototype of Razor, and evaluate its performance with both real and synthetic traces. Extensive experimental results show that Razor can effectively help mobile application developers reduce their backend cost while guaranteeing the user experience. [ABSTRACT FROM AUTHOR]

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks. [ABSTRACT FROM AUTHOR]

This work is motivated by the need to adapt an optimal production schedule for dynamic and stochastic environments in semiconductor manufacturing systems. When unexpected events occur, such as machine breakdown and due date changes, manufacturers need to react quickly and revise the schedule accordingly. This paper presents a novel partial repair rescheduling solution that consists of a criterion and a scheduler. The former decides the segment of the original schedule to be repaired by detecting a match-up point. The impact of disturbances caused by unexpected events can be limited between the disturbing time and this match-up point. The scheduler generates a new schedule segment to replace the original and impacted one with single machine-oriented or machine-group-oriented match-up rescheduling algorithms. On practical demand, the proposed solution can be further updated due to the changing consideration of unexpected events and/or expanded performance criteria. The simulation results show that the proposed rescheduling methods have higher stability and efficiency than some well-known existing rescheduling methods. [ABSTRACT FROM AUTHOR]

In the production process, there is a common problem with the sequencing of production plan and job control, namely the workshop scheduling problem, which has a nonnegligible impact on the production efficiency, economic efficiency and long-term development of the enterprise. In order to improve the production efficiency of large-scale manufacturing workshops and reduce invalid processing procedures, this paper proposed an improved Quantum Evolutionary Algorithm (QEA) based on quantum algorithms and workshop scheduling related theories, and constructed graphical user interface (GUI) based on the proposed algorithm. The study found that quantum algorithms have higher accuracy and convergence speed in solving complex problems, and are more suitable for solving scheduling problems in large-scale manufacturing workshops; the improved QEA has the advantages of good global search ability, fast convergence speed, and can be combined with other algorithms more easily; in actual production scheduling, dynamic scheduling should be taken into consideration, and the proposed algorithm must be adjusted in time according to sudden situations. This study provided a theoretical basis for the scheduling and procedure adjustment and optimization of large-scale production workshops. [ABSTRACT FROM AUTHOR]

In this paper, the scheduling of downlink file transmission in one cell with the assistance of cache nodes with finite cache space is studied. Specifically, requesting users arrive randomly and the base station (BS) reactively multicasts files to the requesting users and selected cache nodes. The latter can offload the traffic in their coverage areas from the BS. We consider the joint optimization of the abovementioned file placement and delivery within a finite lifetime subject to the cache space constraint. Within the lifetime, the allocation of multicast power and symbol number for each file transmission at the BS is formulated as a dynamic programming problem with a random stage number. Note that there are no existing solutions to this problem. We develop an asymptotically optimal solution framework by transforming the original problem to an equivalent finite-horizon Markov decision process (MDP) with a fixed stage number. A novel approximation approach is then proposed to address the curse of dimensionality, where the analytical expressions of approximate value functions are provided. We also derive analytical bounds on the exact value function and approximation error. The approximate value functions depend on some system statistics, e.g., requesting users’ distribution. One reinforcement learning algorithm is proposed for the scenario where these statistics are unknown. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of integrated berth and quay crane allocation (I-BCAP) in general seaport container terminals and propose the model predictive allocation (MPA) algorithm and preconditioning methods for solving the I-BCAP. First, we propose a dynamical modeling framework based on discrete-event systems (DESs), which describes the operation of a berthing process with multiple discrete berthing positions and multiple quay cranes. Second, based on the discrete-event model, we propose the MPA algorithm for solving the I-BCAP using the model predictive control (MPC) principle with a rolling event horizon. The validation and performance evaluation of the proposed modeling framework and allocation method are done using: 1) extensive Monte Carlo simulations with realistically generated datasets; 2) real dataset from a container terminal in Tanjung Priuk port, located in Jakarta, Indonesia; and 3) real life field experiment at the aforementioned container terminal. The numerical simulation results show that our proposed MPA algorithm can improve the efficiency of the process where the total handling and waiting cost is reduced by approximately 6%–9% in comparison with the commonly adapted method of first-come first-served (FCFS) (for the berthing process) combined with the density-based quay cranes allocation (DBQA) strategy. Moreover, the proposed method outperforms the state-of-the-art hybrid particle swarm optimization (HPSO)-based and genetic algorithm (GA)-based method proposed in the recent literature. The real life field experiment shows an improvement of about 6% in comparison with the existing allocation method used in the terminal. [ABSTRACT FROM AUTHOR]

Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users’ perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers’ user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging. [ABSTRACT FROM AUTHOR]

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems that have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real-life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. In this research, a computational method based on a clonal selection principle and an affinity maturation mechanism of the immune response is used. This paper describes how we can incorporate simulation into an immune algorithm for the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdowns. The results obtained are analysed using a Taguchi experimental design. [ABSTRACT FROM AUTHOR]

Affected Operations Rescheduling (AOR) is a process of rescheduling only the operations directly and indirectly affected by a disruption through the propagation of change caused by a disruption while preserving as much as possible the robustness of the initial schedule (preschedule). AOR is basically a centralized heuristic, and thus not readily suitable for project rescheduling in a supply chain where independent and autonomous enterprises do not completely share all the relevant information between each other. This paper is mainly concerned with substantially extending AOR into a distributed version, distAOR, for distributed rescheduling. The primary emphasis of the discussion is placed upon how to deal with the research issues of enterprise autonomy and heterogeneity from the aspects of supply-chain modelling, problem formulation, and solution algorithm. The convergence of the resulting distAOR is theoretically demonstrated. The computational study reported in the first part indicates that the superior efficiency of distAOR is largely due to the fact that only minimum information is shared between enterprises, but at the cost of its lost effectiveness also due to limited information sharing. [ABSTRACT FROM AUTHOR]

The extremely competitive shipbuilding sector is going through a digital transformation in the context of Industry 4.0. The spatiotemporal problem has gained considerable attention during hull construction, particularly for blocks. There are fewer studies on intermediate products at lower levels, such as the dynamic spatiotemporal scheduling for hull parts under complex constraints in subassembly workshops. Therefore, this paper proposes a multi-queue two-level optimization method along with a dynamic spatiotemporal scheduling model based on the multi-directed acyclic graph (multi-DAG) and queueing theory. Firstly, a mathematical description of the complex constraints imposed by processing tasks and various resource types is provided. Then, a dynamic spatiotemporal scheduling model and an objective function are proposed to minimize the average time for parts in the system. At the sequencing level, different priority determination methods for queue events based on an improved genetic algorithm and the contribution ratio for release space strategy are proposed. At the service level, the allocation strategies for space and workers are constructed to improve utilization. Finally, several simulation experiments are conducted using the data gathered from the workshop. The proposed method is compared with the heuristic rules and combination rules using several queueing indicators and the space utilization vs. time graphs. [ABSTRACT FROM AUTHOR]

We introduce and analyze vehicle-allocation algorithms for overhead hoist transport (OHT) systems in semiconductor wafer-fabrication facilities (fabs). OHT is the most widely used type of automated material-handling system in fabs, comprising hundreds of vehicles delivering lots between processing machines. Timely transport unit delivery by OHT systems are critical to the efficient overall operation of a modern fab. We first describe the limitations of current OHT vehicle-allocation algorithms, and then detail an improved system that can be implemented in practical environments to manage the operation of hundreds of OHT vehicles. Our proposed system is based on reinforcement learning. In particular, it uses Q-learning-based dynamic vehicle-allocation algorithms to examine traffic conditions and then allocate an OHT vehicle to a delivery job. We propose multiple algorithms based on the Q-learning approach and compare their performance with that of conventional allocation approaches, which reveals the appropriate algorithms for use in industry. We demonstrate that our algorithms are efficient and sufficiently fast to be used in a practical large-scale setting. [ABSTRACT FROM AUTHOR]

The need of highly directional communications at mmWave band introduces high overhead for beam training and alignment, which also makes the medium access control (MAC) a grand challenge. However, the need of supporting highly directional multiple beams between transmitters and receivers makes the MAC design even harder. To harvest the gain of multi-beam mmWave communications, which benefits not only from the large bandwidth of mmWave spectrum but also the diversity of concurrent multi-user multi-beam transmissions, this paper studies the medium access control (MAC) layer related issues in multi-beam mmWave networks, including (1) efficient multi-beam training schemes to enable lower overhead thus faster AP association and beam alignment, (2) block-sparse mmWave channel estimation in different beam resolutions, and (3) effective concurrent radio resource allocation to facilitate better multi-user multi-beam transmissions. Simulation results demonstrate that the proposed schemes outperform existing techniques in improving the efficiency of mmWave communications thus achieving significantly higher network performances. To our best knowledge, we are the first to comprehensively consider both efficient training for beam alignment and resource scheduling in the MAC design to enable highly directional multi-user multi-beam concurrent transmissions in a mmWave network. [ABSTRACT FROM AUTHOR]

In this work, we seek to design a model that contributes to the study and resolution of a multi-objective rescheduling problem in dynamic permutation flow shop contexts. In this type of problem, where the objectives can be valued in heterogeneous units, the difficulty of achieving an optimal solution leads to finding a set of non-dominated efficient solutions (also called Pareto front). On the other hand, we will also consider the potential appearance of disruptions in planned scheduling (such as machine breakdowns or arrival of new priority jobs) that require a rapid re-planning of the aforementioned scheduling. In this paper, a hybrid dynamic non-dominated sorting genetic II metaheuristic (HDNSGA-II) is proposed to find the optimal Pareto front. The algorithm is applied to a benchmark already tested in previous studies, defined by three conflicting objective functions (makespan, total weighted tardiness, and stability) and three different types of disruption (machine breakdowns, incorporation of new jobs, and modifications in process times). According to the statistical comparison performed, the HDNSGA-II algorithm performs better in the designed environment, especially in larger problems. [ABSTRACT FROM AUTHOR]

Network Function Virtualization (NFV) is a new networking paradigm to enable dynamic network function deployment in networks. Existing studies focused on optimized function deployment and management in NFV. Unfortunately, these studies did not well address the problem of efficient security function enforcement in networks, which is the goal of deploying network functions (NFs), i.e., for real-time security function enforcement on the traffic, since optimal function deployment does not mean efficient security function enforcement on network traffic. In particular, they incurred significant NF enforcement cost. In order to address this issue, in this paper, we propose ${\textsf {FuncE}}$ that aims to solve the efficient real-time security function enforcement problem by developing unified dynamic flow and function scheduling. We formulate the problem as an integer linear programming problem and prove that it is NP-hard. We tackle the problem by decomposing it and developing heuristics to achieve near-optimal solutions. We conduct comprehensive experiments by using real topologies to demonstrate the effectiveness of the ${\textsf {FuncE}}$ design. The experimental results demonstrate that ${\textsf {FuncE}}$ achieves near-optimal network function enforcement, which incurs over 100 times less latency than the existing the optimal solver. In particular, compared to the state-of-art defenses, ${\textsf {FuncE}}$ processes the same number of candidate flows using over 50% less VNFs, while ensuring the same level of function enforcement. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the task scheduling problem in the UAV-enable Mobile Edge-Computing (MEC) system with the objectives of minimizing the cost and the completion time. A NSGA-II algorithm is proposed for the problem under study. The solution is represented as a two-dimension location sequence. Major components of NSGA-II are delicately designed including the feasible solution generation method (FSGM) and genetic operations of crossover, mutation and selection. Three strategies are introduced in FSGM. A simulated annealing local search is integrated into the crossover operation, and meanwhile two novel mutation methods are proposed. The Pareto-based metrics are introduced to evaluate the performance of the compared algorithms. Experimental results show that the proposal is more effective and robust than the three existing algorithms. [ABSTRACT FROM AUTHOR]

Highway charging scheduling for battery electric vehicles is a complex research issue depending on the fast charging capacity provided and the information available in coordinating drivers’ multiple charges at charging stations. Moreover, user’s partially-known preferences and potential dynamic events remain extra challenges in maximizing user’s satisfaction, improving the revenue of highway charging stations and utilizing the limited charging capacities. In such separate and simultaneous markets, users are reasonably modelled as the self-interested agents who aim to advance their own benefits but negotiable on their charging schedules. In this paper, we propose a simultaneous multi-round auction to address the highway charging scheduling problem, where users are allowed to bid and compromise on their preferred stops, charging time and energy simultaneously at separate charging stations. The objective is to maximize the total revenue of these stations. In the course of auction, users can gradually figure out how can their charges fit together by adaptively adjusting their bids placed at different stations. As a result, high-quality solutions are obtained and user’s privacy can be preserved by progressively eliciting their private preferences as necessary. In addition, we also develop a dynamic scheduling algorithm to address the changes of user’s reserved charges and unexpected arrivals of other vehicles. We conduct extensive experiments to validate our approach, the results demonstrate that it can achieve high efficiency with partial private information, as well as a higher revenue with dynamic scheduling algorithm. It can also greatly reduce the total waiting time of users against the first-come-first-serve policy. [ABSTRACT FROM AUTHOR]

This paper presents an integrated metro service scheduling and train unit deployment with a proximal policy optimization approach based on the deep reinforcement learning framework. The optimization problem is formulated as a Markov decision process (MDP) subject to a set of operational constraints. To address the computational complexity, the value function and control policy are parameterized by artificial neural networks (ANNs) with which the operational constraints are incorporated through a devised mask scheme. A proximal policy optimization (PPO) approach is developed for training the ANNs via successive transition simulations. The optimization framework is implemented and tested on a real-world scenario configured with the Victoria Line of London Underground, UK. The results show that the performance of proposed methodology outperforms a set of selected evolutionary heuristics in terms of both solution quality and computational efficiency. Results illustrate the advantages of having flexible train composition in saving operational costs and reducing service irregularities. This study contributes to real time metro operations with limited resources and state-of-art optimization techniques. [ABSTRACT FROM AUTHOR]

This article considers unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks, where low resource IoT devices periodically sample a stochastic process and need to upload more recent information to a Base Station (BS). Among the myriad of applications, there is a need for timely delivery of data (for example, status-updates) before the data becomes outdated and loses its value. Since transmission capabilities of IoT devices are limited, it may not always be feasible to transmit over one hop transmission to the BS. To address this challenge, UAVs with virtual queues are deployed as middle layer between IoT devices and the BS to relay recent information over unreliable channels. In the absence of channel conditions, the optimal online scheduling policy is investigated as well as dynamic UAV altitude control that maintains a fresh status of information at the BS. The objective of this paper is to minimize the Expected Weighted Sum Age of Information (EWSA) for IoT devices. First, the problem is formulated as an optimization problem that is however generally hard to solve. Second, an online model free Deep Reinforcement Learning (DRL) is proposed, where the deployed UAV obtains instantaneous channel state information (CSI) in real time along with any adjustment to its deployment altitude. Third, we formulate the online problem as a Markov Decision Process (MDP) and Proximal Policy Optimization (PPO) algorithm, which is a highly stable state-of-the-art DRL algorithm, is leveraged to solve the formulated problem. Finally, extensive simulations are conducted to verify findings and comprehensive comparisons with other baseline approaches are provided to demonstrate the effectiveness of the proposed design. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

We formulate and analyze a generic task scheduling problem: a set of tasks need to be executed on a pool of continuous resource such as spectrum and memory, each requiring a certain amount of time and contiguous resource; some tasks can be executed simultaneously in batch by sharing the resource, while others requiring exclusive use of the resource. We seek an optimal resource allocation and the related scheduling policy maximizing the overall system utility. This problem, termed as the contiguous-resource batching task scheduling problem, arises in a variety of engineering fields, where communication and storage resources are potential bottlenecks and thus need to be carefully scheduled. Two motivating examples are the spectrum bonding problem in dynamic spectrum access systems and the dynamic storage allocation problem in computer systems. In this paper, we investigate both offline and online scheduling settings. We first establish the NP-hardness of the offline setting and the inapproximability of the online setting in its generic form. Given the theoretical performance limit, we then develop approximation algorithms with mathematically proven performance guarantee in terms of approximation and competitive ratios for the offline and online settings. [ABSTRACT FROM AUTHOR]

In a multi-tenant cloud, cloud vendors provide services (e.g., elastic load-balancing, virtual private networks) on service nodes for tenants. Thus, the mapping of tenants’ traffic and service nodes is an important issue in multi-tenant clouds. In practice, unreliability of service nodes and uncertainty/dynamics of tenants’ traffic are two critical challenges that affect the tenants’ QoS. However, previous works often ignore the impact of these two challenges, leading to poor system robustness when encountering system accidents. To bridge the gap, this paper studies the problem of robust service mapping in multi-tenant clouds (RSMP). Due to traffic dynamics, we take a two-step approach: service node assignment and tenant traffic scheduling. For service node assignment, we prove its NP-Hardness and analyze its problem difficulty. Then, we propose an efficient algorithm with bounded approximation factors based on randomized rounding and knapsack. For tenant traffic scheduling, we design an approximation algorithm based on fully polynomial time approximation scheme (FPTAS). The proposed algorithm achieves the approximation factor of 2+ $\epsilon $ , where $\epsilon $ is an arbitrarily small value. Both small-scale experimental results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives. [ABSTRACT FROM AUTHOR]

The design of the communication infrastructure in cyber physical systems (CPSs) is of key importance since it conveys information from sensors to controllers. Scheduling in the medium access control layer, i.e., the selection of active communication links, plays an important role in improving efficiency of communication in CPS. Different from traditional scheduling algorithms designed for pure data communication networks, the scheduling in CPS is for the purpose of optimizing the physical dynamics and needs to be designed with awareness of the dynamics. In this paper, the scheduling problem in CPS is fitted into the framework of the hybrid systems, in which different selections of links correspond to different dynamic modes. Both centralized and distributed scheduling algorithms are designed. The proposed algorithms are also applied in the background of voltage control of distributed generations (DGs). Numerical simulations show that the proposed framework and algorithms achieve good performances. [ABSTRACT FROM PUBLISHER]

3D visualization and real-time rendering of large-scale scenes is an important task of virtual reality. In this paper, different from local loading and roaming of the scenes, we focus on dynamic loading and real-time roaming, based on the remote servers and clients, to facilitate the efficient transmission and reduce the transmission time. More specifically, a novel dynamic scheduling algorithm in the client side is used to optimize the loading and real-time rendering performance, i.e., this method can dynamically load and unload the partitioned data blocks from the server side according to the roaming viewpoints so that we can realize the infinite roaming of a large scale scene. In order to accommodate different networking scenarios, we also design a multi-resolution scheme for those server-stored scene blocks according to different communication channel conditions, so that the most suitable resolution scheme can be automatically chosen. As shown in the experiments, our method only requires a small amount of memory overhead, while efficiently realizes infinite roaming of the large city scenes. [ABSTRACT FROM AUTHOR]

For complex datapaths, resource sharing can help reduce area consumption. Traditionally, resource sharing is applied when the same resource can be scheduled for different uses in different cycles, often resulting in a longer schedule. Multipumping is a method whereby a resource is clocked at a frequency that is a multiple of the surrounding circuit, thereby offering multiple executions per global clock cycle. This allows a single resource to be shared among multiple uses in the same cycle. This concept maps well to modern field-programmable gate arrays (FPGAs), where hard macro blocks are typically capable of running at higher frequencies than most designs implemented in the logic fabric. While this technique has been demonstrated for static resources, modern digital signal processing (DSP) blocks are flexible, supporting varied operations at runtime. In this paper, we demonstrate multipumping for resource sharing of the flexible DSP48E1 macros in Xilinx FPGAs. We exploit their dynamic programmability to enable resource sharing for the full set of supported DSP block operations, and compare this to multipumping only multipliers and DSP blocks with fixed configurations. The proposed approach saves on average 48% DSP blocks at a cost of 74% more LUTs, effectively saving 30% equivalent LUT area and is feasible for the majority of designs, in which clock frequency is typically below half the maximum supported by the DSP blocks. [ABSTRACT FROM PUBLISHER]

As performance and energy efficiency have become the main challenges for next-generation high-performance computing, asymmetric multi-core architectures can provide solutions to tackle these issues. Parallel programming models need to be able to suit the needs of such systems and keep on increasing the application’s portability and efficiency. This paper proposes two task scheduling approaches that target asymmetric systems. These dynamic scheduling policies reduce total execution time either by detecting the longest or the critical path of the dynamic task dependency graph of the application, or by finding the earliest executor of a task. They use dynamic scheduling and information discoverable during execution, fact that makes them implementable and functional without the need of off-line profiling. In our evaluation we compare these scheduling approaches with two existing state-of the art heterogeneous schedulers and we track their improvement over a FIFO baseline scheduler. We show that the heterogeneous schedulers improve the baseline by up to 1.45$\times$ in a real 8-core asymmetric system and up to 2.1$\times$ in a simulated 32-core asymmetric chip. [ABSTRACT FROM AUTHOR]

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not fully exploit the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power, and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP).By using relative value iteration and special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. Motivated by the switch structures of the optimal policy, we propose a low-complexity suboptimal policy, which exhibits similar switch structures to the optimal policy, and design a low-complexity algorithm to compute this policy. [ABSTRACT FROM PUBLISHER]

Cloud computing has emerged as a very flexible service paradigm by allowing users to require virtual machine (VM) resources on-demand and allowing cloud service providers (CSPs) to provide VM resources via a pay-as-you-go model. This paper addresses the CSP’s problem of efficiently allocating VM resources to physical machines (PMs) with the aim of minimizing the energy consumption. Traditional energy-aware VM allocations either allocate VMs to PMs in a centralized manner or implement VM migrations for energy reduction without considering the migration cost in cloud computing systems. We address these two issues by introducing a decentralized multiagent (MA)-based VM allocation approach. The proposed MA works by first dispatching a cooperative agent to each PM to assist the PM in managing VM resources. Then, an auction-based VM allocation mechanism is designed for these agents to decide the allocations of VMs to PMs. Moreover, to tackle system dynamics and avoid incurring prohibitive VM migration overhead, a local negotiation-based VM consolidation mechanism is devised for the agents to exchange their assigned VMs for energy cost saving. We evaluate the efficiency of the MA approach by using both static and dynamic simulations. The static experimental results demonstrate that the MA can incur acceptable computation time to reduce system energy cost compared with traditional bin packing and genetic algorithm-based centralized approaches. In the dynamic setting, the energy cost of the MA is similar to that of benchmark global-based VM consolidation approaches, but the MA largely reduces the migration cost. [ABSTRACT FROM PUBLISHER]

When incorporating machine-to-machine (M2M) communications into the Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) networks, one of the challenges is the traffic overload since many machine-type communication (MTC) devices activated in a short period of time may require access to an evolved node B (eNodeB) simultaneously. One approach to tackle this problem is by using an access class barring (ACB) mechanism with an ACB factor to defer some activated MTC devices transmitting their access requests. In this paper, we first present an analytical model to determine the expected total service time, i.e., the time used by all MTC devices to successfully access the eNodeB. In the ideal case that the eNodeB is aware of the number of backlogged MTC devices, we determine the optimal value of the ACB factor to reduce traffic overload. To better utilize the random access resources shared among human users and MTC devices in LTE networks, we propose to dynamically allocate a number of random access preambles for MTC devices. We further propose two dynamic ACB (D-ACB) algorithms for fixed and dynamic preamble allocation schemes to determine the ACB factors without a priori knowledge of the system backlog. Simulation results show that the proposed D-ACB algorithms achieve almost the same performance as the optimal performance obtained in the ideal case. The proposed D-ACB for dynamic preamble allocation algorithm can reduce both the total time to serve all MTC devices and the average number of random access opportunities required by each MTC device. [ABSTRACT FROM PUBLISHER]

In the steelmaking-continuous casting (SCC) production process, machine breakdown is one of the most common disturbances which may make the current schedule unrealisable. Existing rescheduling models for machine breakdown only employ one constraint that charges cannot be processed on this machine in its failure period. However, this method is effective for steelmaking furnace breakdown and refining furnace breakdown but invalid for continuous caster breakdown. Due to the production characteristics of continuous caster, reallocating a casting order and a continuous caster for each unfinished charge on the broken down continuous caster is necessary before making a new schedule. Different reallocation strategies have different impacts on charge’s processing time and processing stage route in the dynamic scheduling process. Therefore, SCC dynamic scheduling for the continuous caster breakdown is different from the other machines. In this paper, the impacts of these strategies are studied, and a dynamic scheduling model which can be used to generate a new schedule for each strategy is built. To obtain a high-quality solution in acceptable computational time for this model with NP-hard feature, a hybrid algorithm featuring a genetic algorithm combined with a general variable neighbourhood search is developed based on the problem-specific characteristics. Computational experiments on practical production data show that the proposed rescheduling method is effective for SCC dynamic scheduling with continuous caster breakdown. [ABSTRACT FROM PUBLISHER]