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Chemical algorithms are statistical control algorithms described and represented as chemical reaction networks. They are analytically tractable, they reinforce a deterministic state-to-dynamics relation, they have configurable stability properties, and they are directly implemented in state space using a high-level visual representation. These properties make them attractive solutions for traffic shaping and generally the control of dynamics in computer networks. In this paper, we present a framework for deploying chemical algorithms on field programmable gate arrays. Besides substantial computational acceleration, we introduce a low-overhead approach for hardware-level programmability and re-configurability of these algorithms at runtime, and without service interruption. We believe that this is a promising approach for expanding the control-plane programmability of software defined networks (SDN), to enable programmable network dynamics. To this end, the simple high-level abstractions of chemical algorithms offer an ideal northbound interface to the hardware, aligned with other programming primitives of SDN (e.g., flow rules). [ABSTRACT FROM AUTHOR]

This paper considers multi-degree cyclic scheduling in automated electroplating lines with multiple hoists. In a line, hoists share the same overhead track and cannot cross over each other while transporting parts. To avoid conflicts among hoists, the hoist assignment principle without overlapping is applied. Identical parts with processing time windows are produced. The objective is to maximize the throughput of the line, equivalently to minimize the cycle time in given degree cycles. Previous work mainly focuses on simple cycles, i.e., 1-degree cycles. This paper considers multi-degree cycles to improve the throughput, which is the main contribution of this research. Operations of multi-degree cycles are analyzed in details. Then, a mixed-integer linear programming model is formulated to obtain the optimal schedules. Numerical examples are used to illustrate the schedules obtained in multi-degree cycles based on the model proposed. A number of randomly generated instances simulating practical data are tested. Computational results show the benefits of multi-degree cycles and the efficiency of the approach proposed in practice. [ABSTRACT FROM AUTHOR]

This paper aims to crack the individual EV charging scheduling problem considering the dynamic user behaviors and the electricity price. The uncertainty of the EV charging demand is described by several factors, including the driver’s experience, the charging preference and the charging locations for realistic scenarios. An aggregate anxiety concept is introduced to characterize both the driver’s anxiety on the EV’s range and uncertain events. A mathematical model is also provided to describe the anxiety quantitatively. The problem is formulated as a Markov Decision Process (MDP) with an unknown state transition function. The objective is to find the optimal sequential charging decisions that can balance the charging cost and driver’s anxiety. A model-free deep reinforcement learning (DRL) based approach is developed to learn the optimal charging control strategy by interacting with the dynamic environment. The continuous soft actor-critic (SAC) framework is applied to design the learning method, which contains a supervised learning (SL) stage and a reinforcement learning (RL) stage. Finally, simulation studies verify the effectiveness of the proposed approach under dynamic user behaviors at different charging locations. [ABSTRACT FROM AUTHOR]

In this paper, we study the channel access problem of vehicles in a cognitive radio vehicular network, where each vehicle opportunistically accesses the channel resources of the primary network in order to successfully receive the necessary data packets within a time deadline. Given the access priority constraint and the limited bandwidth of the primary network, a smart channel connection scheme is indispensable to ensure a decent quality of service (QoS) at the vehicles’ side. Due to the competitive nature of vehicles, the vehicle access control is formulated as a multi-agent access problem that comes with an intrinsic challenge, i.e. the partial observation of the information about the environment dynamics. On top of that, considering the temporal usage profile of the primary network, the environment dynamics are also time-dependant, and hence making the aforementioned access control a non-Markovian problem. Consequently, the estimation of the system states, which are used for the decision making process of a vehicle, is very challenging. To deal with the issues arising from such non-Markovian problem, we propose a vehicle connection algorithm based on a deep recurrent Q-learning network. With the aid of a recurrent Long Short Term Memory (LSTM) layer integrated into a deep Q-network, the time-correlated system states can be properly estimated, thereby improving the vehicle channel access policy. Besides, we introduce novel reward quantities that help improving the network performance and the capability to flexibly adapt to unexplored scenarios. A new structure of the cumulative reward function is also presented to balance the performance trade off between the cooperative and competitive objectives. Simulation results are provided to verify the advantage and the stability of our proposed algorithm over the benchmark schemes. [ABSTRACT FROM AUTHOR]

In yard-crane scheduling problems, as loading operations take priority over unloading, the delivery sequence of unloading from the quaysides to the yard is uncertain. The delivery sequence changes may make crane scheduling more difficult. As a result, the crane operations schedules developed statically become suboptimal or even infeasible. In this paper, we propose a dynamic scheduling problem considering uncertain delivery sequences. A mixed-integer linear program is developed to assign tasks to cranes and minimize the makespan of crane operations. We propose an iterative solution framework in which the schedules are re-optimized whenever the delivery sequence change is revealed. A genetic algorithm is proposed to solve the problem, and a greedy algorithm is designed to re-optimize and update the solution. To make the updated solution take effect as soon as possible, regarding batch-based task assignment, the tasks in the scheduling period are divided into several batches. In this case, the instant requests arising from the delivery sequence change are added to corresponding batch tasks and re-optimized together with the tasks of this batch. In addition, a relaxation model is formulated to derive a lower bound for demonstrating the performance of the proposed algorithm. Experimental results show that the average gap between the algorithm and the lower bound does not exceed 5%. The greedy insertion algorithm can re-optimize the instant requests in time. Therefore, the proposed iterative re-optimization framework is feasible and has the advantages (necessity) of batch-based task assignment. [ABSTRACT FROM AUTHOR]

Equipment failure is a common problem in mining operations, resulting in significant delays and reductions in production efficiency. To address this problem, this paper proposes a dynamic scheduling model for underground metal mines under equipment failure conditions. The model aims to minimize the impact of equipment failures on production operations while avoiding extensive equipment changes. A case study of the southeastern mining area of the Chambishi Copper Mine is presented to demonstrate the effectiveness of the proposed model. The initial plan was generated using the multi-equipment task assignment model for the horizontal stripe pre-cut mining method. After equipment breakdown, the proposed model was used to reschedule the initial plan. Then, a comparative analysis was carried out. The results show that the proposed model effectively reduces the impact of equipment failures on production operations and improves overall mining execution at a low management cost. In general, the proposed model can assist schedulers in allocating equipment, coping with the disturbing effects of equipment failure, and improving mine production efficiency. [ABSTRACT FROM AUTHOR]

In this paper, an optimal multi-timescale demand side scheduling framework, i.e., the combination of week-ahead and day-ahead, for industrial customers is proposed. Different demand side management (DSM) techniques suitable for distinct week-ahead and day-ahead timescales cooperate for achieving the overall optimal demand scheduling in the entire multi-timescale frame. Specifically, in the week-ahead scheduling, a dynamic scenario generation method is proposed to accurately simulate uncertainties of customer electricity demand time-series during the scheduling horizon, which can represent not only the marginal distribution of possible customer loads at each time instant but also the joint distribution among multiple loads at different time instants. In addition, priorities of various DSM techniques accepted by DSM participants and their willingness are also considered, aiming at mitigating impacts on their normal manufacturing process. With actual historical load data of industrial customers from advanced metering infrastructure system, the dynamic scenario generation method is shown to be effective in preserving statistic features of load fluctuations, and the proposed optimal multi-timescale coordinated demand side scheduling model is demonstrated to be an effective DSM approach. [ABSTRACT FROM AUTHOR]

Network virtualization and programmability allow operators to deploy a wide range of services over a common physical infrastructure and elastically allocate cloud and network resources according to changing requirements. While the elastic reconfiguration of virtual resources enables dynamically scaling capacity in order to support service demands with minimal operational cost, reconfiguration operations make resources unavailable during a given time period and may incur additional cost. In this paper, we address the dynamic cloud network control problem under non-negligible reconfiguration delay and cost. We show that while the capacity region remains unchanged regardless of the reconfiguration delay/cost values, a reconfiguration-agnostic policy may fail to guarantee throughput-optimality and minimum cost under nonzero reconfiguration delay/cost. We then present an adaptive dynamic cloud network control policy that allows network nodes to make local flow scheduling and resource allocation decisions while controlling the frequency of reconfiguration in order to support any input rate in the capacity region and achieve arbitrarily close to minimum cost for any finite reconfiguration delay/cost values. [ABSTRACT FROM AUTHOR]

A battery charging station (BCS) is a charging facility that supplies electric energy for recharging electric vehicles’ depleted batteries (DBs). A BCS has a certain number of charging bays and maintains a dynamic inventory of fully charged batteries (FBs). This paper studies a BCS scheduling (BCSS) problem whose target is to schedule the charging processes of the charging bays such that the charging cost is minimized while satisfying the FB demand. Specifically, the BCSS problem has two types of operations: 1) loading DBs into the charging bays and then unloading them to the FB inventory when they are fully charged and 2) controlling the charging rate of each charging bay. We formulate the BCSS problem as a mixed-integer program with quadratic battery degradation cost. A generalized benders decomposition algorithm is then developed to solve the problem efficiently. The salience of the developed algorithm is that: 1) each charging bay can solve its own subproblem separately and 2) each subproblem can be further partitioned into multiple independent and identically structured quadratic programming problems, and thus the algorithm facilitates an efficient parallel implementation. We perform extensive real data simulation to validate the optimization model and demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper provides an overview of our 10-year research on the application of stochastic and dynamic programming techniques to address health care operational deficiencies in a demand-driven way. We first describe the main operational deficiencies motivating our research in the capacity allocation and scheduling of diagnostic equipment and operating rooms. We then present main findings of extensive field studies to show current practices and key features of the problems under consideration. Applications of stochastic and dynamic programming to these problems are discussed by giving key assumptions, mathematical models, properties of the optimal solution, solution approaches and main numerical findings. The relaxation of the key assumptions is shown to lead to various future research directions that have drawn significant interests of the operations research and industrial engineering communities. We conclude by identifying barriers and potential solutions on the path from theories to applications. [ABSTRACT FROM AUTHOR]

This paper deals with dynamic industry scheduling problem in agri-food production. The decision-making study in this paper is articulated around the management of perishable products under constrained resources. The scheduling of logistics operations is considered at the operational level. Two metaheuristics are proposed to solve dynamic scheduling under perturbations. The uncertainty sources considered in this study are the expiration date of product components and production delays. The proposed Genetic Algorithm (GA) and the Ant Colony Optimization Algorithm (ACO) take into consideration two objective functions: minimizing the makespan and reducing the number of perishable products. The algorithms are tested on a flow-shop agri-food system. [ABSTRACT FROM AUTHOR]

Public transport systems in a metropolitan area experiences several complex issues, like resource scarcity, resource allocation, congestion, resource reliability and so on, due to the dynamic arrivals of heterogeneous commuter and exceptional occurrence of unforeseen events. The progress of these issues may lead to economic losses, under-utilization of transport resources, and commuters’ queuing delay. In this paper, we propose a novel dynamic public transport vehicle allocation scheme based on Emergent Intelligence (EI) technique in a metropolitan area. In addition, we demonstrate the EI technique’s capability for solving public transport system problems. To do so, the EI technique maintains historical information, commuters’ arrival rates, resource avaialability, deficit resources and surplus resources of neighbor depots’s agent. In the proposed scheme, the EI technique is utilized to collect, analyze, share and optimally allocate transport resources effectively. The proposed EI technique provides reliable services (allocation and scheduling) by coordinating with a reliable neighborhood depot’s agent. We have build mathematical models for estimation of resources, utilization and reliability parameters. The proposed scheme is exhaustively tested by simulation and analyzed with varying commuters’ arrival rates, number of vehicles, number of requests, and different values of reliability parameters. The proposed scheme’s results (analytical, simulation and comparison) show the reliabiltiy, accuracy and real time deployability. [ABSTRACT FROM AUTHOR]

Despite the robust literature on the nature of business models and their implications for firm performance, research on the organizational antecedents of business model innovations (BMIs) is still evolving. In this paper, we empirically examine the extent to which firm-level strategic agility predicts the adoption of three (value creation, value capture, and value proposition) types of BMIs. Furthermore, we propose that the relationship between firm-level strategic agility and BMI adoption is contingent on the degree of environmental turbulence. Finally, we explore the mediating role that BMI plays in the relationship between firm-level strategic agility and firm performance. Our analysis of data from 432 German firms in the electronics industry indicates that strategic agility is positively related to BMI and that this relationship is indeed strengthened by the degree of environmental turbulence. Additionally, our findings show that, while value proposition and value creation BMIs have positive relationships with firm performance, value capture innovation is negatively related to firm performance; these findings are contrary to our prediction. Finally, the results of our mediation tests indicate that BMI serves as an important intermediary mechanism through which firms’ strategic agility contributes to superior firm performance. [ABSTRACT FROM AUTHOR]

This paper explores how to promote business model innovation through intraindustry search and extraindustry search with the assistance of fit dynamic capabilities (internal coordination capability and stakeholder engagement capability). Based on evolutional learning and business model literatures, six hypotheses are proposed and examined with data from 204 firms in China. The results show that intraindustry search has an inverted U-shaped effect on business model innovation, whereas extraindustry search has a positive effect. Internal coordination capability strengthens the effect of intraindustry search but weakens that of extraindustry search. Stakeholder engagement capability strengthens the effect of extraindustry search but weakens that of intraindustry search. Our findings enrich the business model literature by identifying different roles of intraindustry search and extraindustry search as the antecedents of business model innovation and their fitness with different types of dynamic capabilities. [ABSTRACT FROM AUTHOR]

At present, discrete workshops demand higher transportation efficiency, but the traditional scheduling strategy of the logistics systems can no longer meet the requirements. In a transportation system with multiple automated guided vehicles (multi-AGVs), AGV path conflicts directly affect the efficiency and coordination of the whole system. At the same time, the uncertainty of the number and speed of AGVs will lead to excessive cost. To solve these problems, an AGVs Multi-Objective Dynamic Scheduling (AMODS) method is proposed which is based on the digital twin of the workshop. The digital twin of the workshop is built in the virtual space, and a two-way exchange and real-time control framework based on dynamic data is established. The digital twin system is adopted to exchange data in real time, create a real-time updated dynamic task list, determine the number of AGVs and the speed of AGVs under different working conditions, and effectively improve the efficiency of the logistics system. Compared with the traditional scheduling strategy, this paper is of practical significance for the scheduling of the discrete workshop logistics systems to improve the production efficiency, utilization rate of resources, and dynamic response capability. [ABSTRACT FROM AUTHOR]

With the development of the IC industry, electronic design automation (EDA) tools are also developing. Currently, EDA tools have been migrating to high‐performance computing clusters (HPC) or the cloud to meet increasing computing and storage requirements. EDA tasks are scientific workflows, whose scheduling is a well‐known NP‐hard problem. In this paper, we propose a novel workflow scheduling algorithm HEWS, which achieves better performance through a novel hierarchical sorting method. We conducted a series of simulation experiments in different environments, and the experimental results show that our HEWS scheduling algorithm achieves better scheduling performance compared to several conventional scheduling methods, the waiting time and makespan of workflow are significantly reduced. [ABSTRACT FROM AUTHOR]

Cloud computing distributes task-parallel among the various resources. Applications with self-service supported and on-demand service have rapid growth. For these applications, cloud computing allocates the resources dynamically via the internet according to user requirements. Proper resource allocation is vital for fulfilling user requirements. In contrast, improper resource allocations result to load imbalance, which leads to severe service issues. The cloud resources implement internet-connected devices using the protocols for storing, communicating, and computations. The extensive needs and lack of optimal resource allocating scheme make cloud computing more complex. This paper proposes an NMDS (Network Manager based Dynamic Scheduling) for achieving a prominent resource allocation scheme for the users. The proposed system mainly focuses on dimensionality problems, where the conventional methods fail to address them. The proposed system introduced three -threshold mode of task based on its size STT, MTT, LTT (small, medium, large task thresholding). Along with it, task merging enables minimum energy consumption and response time. The proposed NMDS is compared with the existing Energy-efficient Dynamic Scheduling scheme (EDS) and Decentralized Virtual Machine Migration (DVM). With a Network Manager-based Dynamic Scheduling, the proposed model achieves excellence in resource allocation compared to the other existing models. The obtained results shows the proposed system effectively allocate the resources and achieves about 94% of energy efficient than the other models. The evaluation metrics taken for comparison are energy consumption, mean response time, percentage of resource utilization, and migration. [ABSTRACT FROM AUTHOR]

Communication and control are two intensively coupled aspects in a networked control system (NCS), the design of one may affect the quality or performance of the other. The co-design problem of control and communication scheduling for NCSs has attracted tremendous attention. This paper provides an overview of recent advances in control and scheduling co-design for networked control systems. First, a basic framework of control and scheduling co-design problem setup is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis and discussions are given, along the lines of the scheduling schemes including static scheduling, dynamic scheduling, and random scheduling. Finally, some unsettled issues and trending topics in this direction are outlined for future research. [ABSTRACT FROM AUTHOR]

In data-center servers, the dynamic core allocation for Latency-Critical (LC) applications can play a crucial role in improving energy efficiency under Service Level Objective (SLO) constraints, allowing cores to enter idle states (i.e., C-states) that consume less power by turning off a part of hardware components of a processor. However, prior studies focus on the core allocation for application threads while not considering cores involved in network packet processing, even though packet processing affects not only response latency but also energy consumption considerably. In this paper, we first investigate the impacts of the explicit core allocation for network packet processing on the tail response latency and energy consumption while running LC applications. We observe that co-adjusting the number of cores for network packet processing along with the number of cores for LC application threads can improve energy efficiency substantially, compared with adjusting the number of cores only for application threads, as prior studies do. In addition, we propose a dynamic core allocation, called CoreNap, which allocates/de-allocates cores for both LC application threads and packet processing. CoreNap measures the CPU-utilization by application threads and packet processing individually, and predicts response latency and power consumption when the combination of core allocation is enforced via a lightweight prediction model. Based on the prediction, CoreNap chooses/enforces the energy-efficient combination of core allocation. Our experimental results show that CoreNap reduces energy consumption by up to 18.6% compared with state-of-the-art study that adjusts cores only for LC application in parallel packet processing environments. [ABSTRACT FROM AUTHOR]

Dynamic off-chain routing in payment channel network (PCN)-based Internet of Things (IoT) is attracting increasing research attention. However, there are two major issues in dynamic routing in PCN-based IoT with resource-limited devices. The first issue is how to achieve high long-term transaction efficiency in PCN with dynamic channel capacities. The second issue is how to achieve a lightweight routing algorithm deployed on IoT devices while achieving high transaction efficiency, i.e., successful payment amount and success ratio. Therefore, in this paper, we propose a compact deep reinforcement learning (DRL) algorithm to learn the joint dynamic and lightweight routing policy for maximizing long-term transaction efficiency. To obtain optimal performance in dynamic routing problems for off-chain systems, a proximal policy optimization algorithm is employed to create an actor–critic learning structure for training the teacher DRL model. To obtain a compact and efficient student DRL model, an adaptive pruning technique is utilized for pruning unnecessary parameters of networks in the teacher model adaptively without affecting its learning ability. Furthermore, knowledge distillation is leveraged to improve the performance of the student network. Thus, a compact and efficient student DRL model can be developed and implemented to maximize the long-term transaction efficiency in off-chain systems on resource-limited IoT devices. The simulation results demonstrate that the proposed DRL algorithm outperforms the other baseline algorithms in PCN transaction efficiency while requiring only 10% of the computation and storage resources compared with that of the original teacher model. [ABSTRACT FROM AUTHOR]

The shift towards high-throughput technologies and automation in research and development in industrial biotechnology is highlighting the need for increased automation competence and specialized software solutions. Within bioprocess development, the trends towards miniaturization and parallelization of bioreactor systems rely on full automation and digital process control. Thus, mL-scale, parallel bioreactor systems require integration into liquid handling stations to perform a range of tasks stretching from substrate addition to automated sampling and sample analysis. To orchestrate these tasks, the authors propose a scheduling software to fully leverage the advantages of a state-of-the-art liquid handling station (LHS) and to enable improved process control and resource allocation. Fixed sequential order execution, the norm in LHS software, results in imperfect timing of essential operations like feeding or Ph control and execution intervals thereof, that are unknown a priori. However, the duration and control of, e.g., the feeding task and their frequency are of great importance for bioprocess control and the design of experiments. Hence, a software solution is presented that allows the orchestration of the respective operations through dynamic scheduling by external LHS control. With the proposed scheduling software, it is possible to define a dynamic process control strategy based on data-driven real-time prioritization and transparent, user-defined constraints. Drivers for a commercial 48 parallel bioreactor system and the related sensor equipment were developed using the SiLA 2 standard greatly simplifying the integration effort. Furthermore, this paper describes the experimental hardware and software setup required for the application use case presented in the second part. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic resource allocation (RA) problem in cooperative OFDMA systems to maximize the average utility of all mobile stations (MSs) under different services. We propose a dynamic optimization framework for RA by considering three dynamic situations: time-varying fading channel, MSs' change of states, and relay stations (RSs)' change of states. Moreover, a dynamic RA algorithm based on discrete particle swarm optimization (DPSO) is proposed. The correlation between the adjacent frames is exploited to improve the performance of the dynamic RA algorithm. Simulation results show that the proposed dynamic algorithm achieves better performance at linear complexity, compared with the existing algorithms under different dynamic environments, while guaranteeing fairness to a proper level. [ABSTRACT FROM AUTHOR]

Specific computing (SC) and diverse computing (DC), as two main visual computation manners, have been widely utilized in Media Cloud. However, how to choose SC or DC in a practical scenario is still an open and challenging problem. Unfortunately, the traditional fluid-based analysis method cannot address this issue due to the uncertain relationship between the computing manner and service dynamics. In this paper, we analytically study the characteristics of SC and DC by designing a so-called collapsing approximation (CA) method to precisely approximate the distribution of the service dynamics. On the qualitative end, we derive an exact expression for the dynamics of CA, thus enabling the cloud designer to choose different computing manners according to the application requests and analyze its impact on the degrees of DC. On the technical end, we show that the evolution of the service dynamics process can be approximated by the unique solution to a collapsing model over a finite time period. The highlight of this paper lies in demonstrating that the optimal computing configuration should largely depend on SC, and a little on DC. [ABSTRACT FROM AUTHOR]

Amidst a contemporary fast-changing business environment, scholars and practitioners alike increasingly recognize knowledge management (KM) and dynamic capabilities as key elements in the development of firms’ competitive advantage. Our understanding of the effect of KM on firm performance, nonetheless, is still limited, as in fact are the circumstances under which KM and dynamic capabilities affect firms’ ambidexterity, which reflects firms’ ability to conduct synchronous exploration and exploitation activities. Thus, building on KM and dynamic capability literature, and implementing a quantitative methodology, this paper aims to investigate the elusive relationship among KM orientation, dynamic capabilities, and ambidextrous entrepreneurial intensity (EI). Employing a dataset composed of 181 Italian firms operating in the ICT industry, and using structural equation modeling, the research subsequently investigates whether and how this relationship affects the overall firm performance. Results indicate that KM orientation has a positive and significant impact on ambidextrous EI and performance, especially when the firm has substantial dynamic capabilities. These findings further facilitate the identification and prescription of explicit scholarly and managerial implications. [ABSTRACT FROM AUTHOR]

Supply and production uncertainties can affect the scheduling and inventory performance of final production systems. Facing such uncertainties, production managers normally choose to maintain the original production schedule, or follow the first-in-first-out policy. This paper develops a new, dynamic algorithm policy that considers scheduling and inventory problems, by taking advantage of real-time shipping information enabled by today’s advanced technology. Simulation models based on the industrial example of a chemical company and the Taguchi’s method are used to test these three policies under 81 experiments with varying supply and production lead times and uncertainties. Simulation results show that the proposed dynamic algorithm outperforms the other two policies for supply chain cost. Results from Taguchi’s method show that companies should focus their long-term effort on the reduction of supply lead times, which positively affects the mitigation of supply uncertainty. [ABSTRACT FROM PUBLISHER]

In order to increase customer satisfaction and competitiveness, manufacturing systems need to combine flexibility with Just-in-Time (JIT) production. Until now, research on JIT scheduling problems has been mostly limited to high volume assembly lines rather than job-shop-like systems, due to their combinatorial complexity. In this paper, we propose a generic strategy for dynamically controlling task schedules by coupling genetic algorithms and distributed arrival-time control to optimise JIT performance. We explore two such hybrid approaches: a sequential approach where the two algorithms work separately and an integrated approach where the distributed arrival time control is embedded into the genetic algorithm. Performance of these approaches is benchmarked with quadratic linear programme solutions to get a gauge of their relative strengths in a static environment. Results from applying these approaches to a job-shop-like automated cell verify their effectiveness for JIT manufacturing under realistic dynamically changing environment. [ABSTRACT FROM AUTHOR]

Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) mobile communications system is envisioned to serve various mission-critical services such as industrial automation, cloud robotics, and safety-critical vehicular communications. To satisfy the stringent end-to-end latency requirement of these services, fog computing has been regarded as a promising technology to be integrated into 5G networks, in which computing, storage, and network functions are provisioned close to end users, thus significantly reducing the latency caused in transport networks. However, in the context of fog-computing-enabled 5G networks, the high mobility feature of users brings critical challenges to satisfy the stringent quality of service requirements. To address this issue, service migration, which transmits the associated services from the current fog server to the target one to follow the users' travel trace and keep the service continuity, has been considered. However, service migration cannot always be completed immediately and may lead to a situation where users experience a loss of service access. In this regard, low-latency service migration plays a key role to reduce the negative effects on services being migrated. In this paper, the factors that affect the performance of service migration are analyzed. To enable low-latency service migration, three main enabling technologies are reviewed, including migration strategies, low-latency, and high-capacity mobile backhaul network design, and adaptive resource allocation. Based on a summary of the reviewed technologies, we conclude that dynamic resource allocation is the worthiest one to research. Therefore, we carry out a use case, where reinforcement learning (RL) is adopted for autonomous bandwidth allocation in support of low-latency service migration in a dynamic traffic environment and evaluate its performance compared to two benchmarks. The simulation demonstrates that the RL-based algorithm is able to self-adapt to a dynamic traffic environment and gets converged performance, which has an obviously smaller impact on non-migration traffic than the two benchmarks while keeping the migration success probability high. Meanwhile, unlike the benchmarks, the RL-based method shows performance fluctuations before getting converged, which may cause unstable system performance. It calls for future research on advanced smart policies that can get convergence quickly, particularly for handling the migration of latency-sensitive services in 5G transport networks. [ABSTRACT FROM AUTHOR]

In this paper, we consider the downlink transmission of URLLC packets requiring very low latency and ultra-reliability. Because of the low latency constraint, the Base Station may not have enough time to acquire the instantaneous Channel State Information (CSI) of the corresponding device and has then to transmit urgent packets immediately in the absence of CSI. To enhance reliability, we explore frequency diversity where a packet can be simultaneously sent over multiple channels. Using a Markov Decision Process framework, we address the problem of dynamic channel allocation to the URLLC devices in absence of instantaneous CSI. More precisely, we define a multi-agent MDP wherein the state of each device is the packet loss rate experienced in the previous time slots and the decision variable is how to split the available orthogonal channels across the devices. We design a new low-complexity algorithm which avoids the exhaustive enumeration of all possible resource allocations and enables significant computational savings compared to the Value Iteration algorithm. We investigate the gap between our proposed low complexity algorithm and the Value Iteration policy. We provide numerical performance results and show that our algorithm can achieve more than 80% of the optimal reward with substantial computational complexity reduction. [ABSTRACT FROM AUTHOR]

Recently, the 5G is widely deployed for supporting communications of high mobility nodes including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the main components for constructing the wireless heterogeneous network (HetNet). To further improve the radio utilization, the Time Division Duplex (TDD) is considered to be the potential full-duplex communication technology in the high mobility 5G network. However, the high mobility of users leads to the high dynamic network traffic and unpredicted link state change. A new method to predict the dynamic traffic and channel condition and schedule the TDD configuration in real-time is essential for the high mobility environment. In this paper, we investigate the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner. In the proposal, the deep neural network is employed to extract the features of the complex network information, and the dynamic Q-value iteration based reinforcement learning with experience replay memory mechanism is proposed to adaptively change TDD Up/Down-link ratio by evaluated rewards. The simulation results show that the proposal achieves significant network performance improvement in terms of both network throughput and packet loss rate, comparing with conventional TDD resource allocation algorithms. [ABSTRACT FROM AUTHOR]

High-level synthesis (HLS) of field programmable gate array (FPGA)-based accelerators has been proposed in order to simplify accelerator design process with respect to design time and complexity. However, modern HLS tools do not consider dynamic memory allocation constructs in high-level programming languages like C and limit themselves to static memory allocation. This paper proposes a dynamic memory allocation and management scheme, called Hi-DMM, for inclusion in commercial HLS design flows. Hi-DMM performs source-to-source transformation of user C code with dynamic memory constructs into C-source code with the dynamic memory allocator and management scheme developed in this paper. The transformed C-source code is amenable to synthesis by commercial tools like Vivado HLS. Relying on buddy tree-based allocation schemes and efficient hardware implementation of the allocators, Hi-DMM achieves ${4\times }$ speed-up in both fine-grained and coarse-grained memory allocation compared to previous works. Experimental results obtained by including Hi-DMM with Vivado-HLS show that dynamic memory allocation of FPGA memory resources can be achieved at a much lower latency with minimal resource overhead, paving the way for synthesis of dynamic memory constructs in commercial HLS flows. [ABSTRACT FROM AUTHOR]

As a key enabling technology for 5G network softwarization, network function virtualization (NFV) provides an efficient paradigm to optimize network resource utility for the benefits of both network providers and users. However, the inherent network dynamics and uncertainties from 5G infrastructure, resources, and applications are slowing down the further adoption of NFV in many emerging networking applications. Motivated by this, in this paper, we investigate the issues of network utility degradation when implementing NFV in dynamic networks, and design a proactive NFV solution from a fully stochastic perspective. Unlike existing deterministic NFV solutions, which assume given network capacities and/or static service quality demands, this paper explicitly integrates the knowledge of influential network variations into a two-stage stochastic resource utilization model. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. The experimental results demonstrate that the proposed solution not only improves 3~5 folds of network performance, but also effectively reduces the risk of service quality violation. [ABSTRACT FROM AUTHOR]

This paper presents a method for scheduling resources in complex systems that integrate humans with diverse hardware and software components, and for studying the impact of resource schedules on system characteristics. The method uses discrete-event simulation and integer linear programming, and relies on detailed models of the system’s processes, specifications of the capabilities of the system’s resources, and constraints on the operations of the system and its resources. As a case study, we examine processes involved in the operation of a hospital emergency department, studying the impact staffing policies have on such key quality measures as patient length of stay (LoS), number of handoffs, staff utilization levels, and cost. Our results suggest that physician and nurse utilization levels for clinical tasks of 70% result in a good balance between LoS and cost. Allowing shift lengths to vary and shifts to overlap increases scheduling flexibility. Clinical experts provided face validation of our results. Our approach improves on the state of the art by enabling using detailed resource and constraint specifications effectively to support analysis and decision making about complex processes in domains that currently rely largely on trial and error and other ad hoc methods. [ABSTRACT FROM AUTHOR]

Providing backhaul connectivity for macro and pico base stations (BSs) constitutes a significant share of infrastructure costs in future heterogeneous networks (HetNets). To address this issue, the emerging idea of flexible backhaul is proposed. Under this architecture, not all the pico BSs are connected to the backhaul, resulting in a significant reduction in the infrastructure costs. In this regard, pico BSs without backhaul connectivity need to communicate with their nearby BSs in order to have indirect accessibility to the backhaul. This makes the radio resource management (RRM) in such networks more complex and challenging. In this paper, we address the problem of cross-layer RRM in HetNets with flexible backhaul. We formulate this problem as a two-timescale non-convex stochastic optimization, which jointly optimizes flow control, routing, interference mitigation, and link scheduling in order to maximize a generic network utility. By exploiting a hidden convexity of this non-convex problem, we propose an iterative algorithm which converges to the global optimal solution. The proposed algorithm benefits from low complexity and low signaling, which makes it scalable. Moreover, due to the proposed two-timescale design, it is robust to the backhaul signaling latency as well. Simulation results demonstrate the significant performance gain of the proposed solution over various baselines. [ABSTRACT FROM AUTHOR]

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements. [ABSTRACT FROM PUBLISHER]

Coarse grained reconfigurable architectures (CGRA) have been applied to numerous fields of computing- and data-intensive applications, such as computer vision, baseband communication, and cipher processing. A CGRA usually comprises hundreds of reconfigurable computing-cells (RCC), which account for a majority of the die area. As such, RCCs have a higher probability of being attacked by hardware Trojans, which seriously affects CGRA behavior. However, a CGRA can be dynamically and partially reconfigured via configuration contexts at runtime; this property could be utilized as an effective countermeasure against malicious hardware. This particular topic has yet to undergo significant research. This paper proposes a secure mapping approach called dynamic resource management based on security value (DRMaSV) to enhance CGRA capability against hardware Trojans by selectively protecting RCCs. DRMaSV realizes run-time monitoring based on an adapted triple modular redundancy mechanism under hardware resource constraints (i.e., area constraints). First, in order to measure the capability against hardware Trojans, a security capability metric called “security value” (SV) is defined, with measurements categorized as “Influence” and “Unreliability.” Here, both the circuit architecture and the level of Unreliability for modules used in the circuit are considered. Next, a DRM strategy to maximize the SV under hardware resource constraints is introduced. This strategy is described by the dynamic programming model (i.e., 0/1 knapsack problem), which can obtain an optimal solution. Finally, a mapping approach for CGRAs is derived by attaching the DRM strategy to a generic mapping flow. Simulations show that the proposed secure mapping approach ensures a given number of correct outputs, which then allows the number of outputs affected by activated Trojans under any given hardware resource constraint (area constraint) or overhead (area overhead) to be minimized. The results of actual chip design experiments are in agreement with the simulation results, indicating that the proposed secure mapping approach is effective. [ABSTRACT FROM AUTHOR]

The problem of allocating jobs to appropriate servers in cloud computing is studied in this paper. We consider that the jobs of various types arrive in some unpredictable pattern and the system is required to allocate a certain ratio of jobs. In order to meet the hard allocation ratio requirement in the presence of unknown arrival patterns, one can increase the capacity of servers by expanding the size of data centers. We then aim to find the minimum capacity needed to meet a given allocation ratio requirement. We study this problem for both systems with persistent jobs, such as video streaming, and systems with dynamic jobs, such as database queries. For both systems, we propose online job allocation policies with low complexity. For systems with persistent jobs, we prove that our policies can achieve a given hard allocation ratio requirement with the least capacity. For systems with dynamic jobs, the capacity needed for our policies to achieve the hard allocation ratio requirement is close to a theoretical lower bound. Two other popular policies are studied, and we demonstrate that they need at least an order higher capacity to meet the same hard allocation ratio requirement. Simulation results demonstrate that our policies remain far superior than the other two even, when the jobs arrive according to some random process. [ABSTRACT FROM AUTHOR]

Although some excellent works have been done on networking, caching, and computing, these three important areas have traditionally been addressed separately in the literature. In this paper, we describe the recent advances in jointing networking, caching, and computing and present a novel integrated framework: software-defined networking, caching, and computing (SD-NCC). SD-NCC enables dynamic orchestration of networking, caching, and computing resources to efficiently meet the requirements of different applications and improve the end-to-end system performance. Energy consumption is considered as an important factor when performing resource placement in this paper. Specifically, we study the joint caching, computing, and bandwidth resource allocation for SD-NCC and formulate it as an optimization problem. In addition, to reduce computational complexity and signaling overhead, we propose a distributed algorithm to solve the formulated problem, based on recent advances in alternating direction method of multipliers (ADMM), in which different network nodes only need to solve their own problems without exchange of caching/computing decisions with fast convergence rate. Simulation results show the effectiveness of our proposed framework and ADMM-based algorithm with different system parameters. [ABSTRACT FROM AUTHOR]

Solid state drives (SSDs) have been widely deployed in personal computers, data centers, and cloud storages. In order to improve performance, SSDs are usually constructed with a number of channels with each channel connecting to a number of nand flash chips, each flash chip consisting of multiple dies and each die containing multiple planes. Based on this parallel architecture, I/O requests are potentially able to access parallel units simultaneously. Despite the rich parallelism offered by the parallel architecture, recent studies show that the utilization of flash parallel units is seriously low. This paper shows that the low parallel unit utilization is highly caused by the access conflict among I/O requests. In this paper, we propose parallel issue queueing (PIQ), a novel I/O scheduler at the host systems. PIQ groups I/O requests without conflicts into the same batch and I/O requests with conflicts into different batches. Hence, the multiple I/O requests in one batch can be fulfilled simultaneously by exploiting the rich parallelism of SSDs. Extensive experimental results show that PIQ delivers significant performance improvement especially for the applications which have heavy access conflicts. [ABSTRACT FROM PUBLISHER]

Software defined networking allows network providers to share their physical network (PN) among multiple tenants by means of network slicing, where several virtual networks (VNs) are provisioned on top of the physical one. In this scenario, PN resource utilization can be improved by introducing advanced orchestration functionalities that can intelligently assign and redistribute resources among the slices of different tenants according to the temporal variation of the VN resource requirements. This is a concept known as dynamic slicing. This paper presents a solution for the dynamic slicing problem in terms of both mixed integer linear programming formulations and heuristic algorithms. The benefits of dynamic slicing are compared against static slicing, i.e., an approach without intelligent adaptation of the amount of resources allocated to each VN. Simulation results show that dynamic slicing can reduce the VN rejection probability by more than 1 order of magnitude compared to static slicing. This can help network providers accept more VNs into their infrastructure and potentially increase their revenues. The benefits of dynamic slicing come at a cost in terms of service degradation (i.e., when not all the resources required by a VN can be provided), but the paper shows that the service degradation level introduced by the proposed solutions is very small. [ABSTRACT FROM PUBLISHER]

Manufacturing systems engineeringwill be greatly affected by advances in technology, including cheaper, ubiquitous sensors, increasing computational speeds, the ability to hold more data and move it faster, and artificial intelligence. This paper discusses the importance of human involvement in the field, especially the role of intuition. The issues examined are the purposes of manufacturing systems engineering; the importance of intuition, and more generally, the role that humans play in the design and operations of manufacturing systems; the effects of new systems-related technologies; how analytical models help the development of intuition; how the use of generic software without intuition can lead to trouble; how intuition is needed to determined what data, and how much of it, is needed for a given purpose. It also describes some successful applications of manufacturing systems research and proposes an organisational structure for manufacturing systems groups. [ABSTRACT FROM AUTHOR]

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with “a priori” and “a posteriori” optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, “interactive” and “Pareto pruning” methods, as well as the consideration of human factors in flexible production systems, remain under-explored.