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Multiprocessor System-on-Chips (MPSoCs) computing architectures are gaining popularity due to their high-performance capabilities and exceptional Quality-of-Service (QoS), making them a particularly well-suited computing platform for computationally intensive workloads and applications. Nonetheless, The scheduling and allocation of a single task set with precedence restrictions on MPSoCs have presented a persistent research challenge in acquiring energy-efficient solutions. The complexity of this scheduling problem escalates when subject to conditional precedence constraints between the tasks, creating what is known as a Conditional Task Graph (CTG). Scheduling sets of Periodic Conditional Task Graphs (PCTGs) on MPSoC platforms poses even more challenges. This paper focuses on tackling the scheduling challenge for a group of PCTGs on MPSoCs equipped with shared memory. The primary goal is to minimize the overall anticipated energy usage, considering two distinct power models: dynamic and static power models. To address this challenge, this paper introduces an innovative scheduling method named Energy Efficient Successor Tree Consistent Earliest Deadline First (EESEDF). The EESEDF approach is primarily designed to maximize the worst-case processor utilization. Once the tasks are assigned to processors, it leverages the earliest successor tree consistent deadline-first strategy to arrange tasks on each processor. To minimize the overall expected energy consumption, EESEDF solves a convex Non-Linear Program (NLP) to determine the optimal speed for each task. Additionally, the paper presents a highly efficient online Dynamic Voltage Scaling (DVS) heuristic, which operates in O(1) time complexity and dynamically adjusts the task speeds in real-time. We achieved the average improvement, maximum improvement, and minimum improvement of EESEDF+Online-DVS 15%, 17%, and 12%, respectively compared to EESEDF alone. Furthermore, in the second set of experiments, we compared EESEDF against state-of-the-art techniques LESA and NCM. The results showed that EESEDF+Online-DVS outperformed these existing approaches, achieving notable energy efficiency improvements of 25% and 20% over LESA and NCM, respectively. Our proposed scheduler, EESEDF+Online-DVS, also achieves significant energy efficiency gains compared to existing methods. It outperforms IOETCS-Heuristic by approximately 13% while surpassing BESS and CAP-Online by impressive margins of 25% and 35%, respectively.

The traditional bundle relaying scheme rarely utilizes the impact of selfish nodes in Vehicular Delay Tolerant Networks (VDTNs) that may not be able to transmit messages to other nodes because of limited resources in the road transport system. Here the Railway Transport System (RTS) is used to transfer the data from one end to another end into the network. In RTS, the proposed approach utilizes stationary nodes deployed along the railway tracks (usually at railway stations) with a large memory size, known as Track Side Units (TSUs), and mobile nodes which are trains equipped with devices having large buffer capacity and message relaying capability. In this paper, the VDTN scenario is developed using RTS in which all nodes have high buffer capacity and power availability hence storage and transfer of message bundles will be done through all nodes. Further, this paper proposed a novel model for transmitting data in VDTN environment using an Efficient Deterministic Bundle Relaying Scheme with Bulk Bundle Release (DBRS-BBR). To validate this, a mathematical model of queuing processes, M/G/1: FIFO/ $\infty /\infty $ , is applied at the TSUs and mobile units, and a Deterministic Scheduling technique is applied for relaying the bundles. To evaluate the proposed DBRS-BBR scheme, different performance measures are used, including Mean Queueing Delay, Mean Transit Delay, and Mean End-To-End Delay. This proposed scheme has the potential to improve message bundle transmission in remote areas and it outperforms the existing Probabilistic Bundle Relaying Scheme with Bulk Bundle Release (PBRS-BBR) in terms of various performance measures.

Scheduling of robots is one of the imperative assignment in a multi robot system. Scheduling is prerequisite when there is a multiple task need to be assigned to multi robot in an arranged manner. There is a growing need for robots to perform complex tasks autonomously. Multi-robot environment becomes complex as there are multiple factors need to be addressed simultaneously which require fast computation and more space. Using cloud computing platform could be one of the optimal solution for this problem. This paper presents the use of cloud computing platform for implementing the proposed Periodic Min-Max Algorithm (PMW) for multi robot task scheduling. Amazon web service (AWS) platform is utilized for deploying the algorithm for multi robot task scheduling. The task performed by the robots is considered as a single service in context with cloud platform and it withdraw an advantage when the number of services increases with time. Time requirement to complete the task and the load balancing parameter are analysed using the proposed approach and is compared with other relevant work. The results presented in the paper clearly shows the performance improvement in both the parameters. There is an improvement of about 3-7% in both the parameters and are reported in the paper. The paper also emphasize on the deployment of cloud computing platform for the service robots. Time completion factor is analysed and reported in the paper to proof the advantage of using cloud platform for the service robots. The novel way of using the algorithm with cloud server seeks many advantage are also observed, analysed and presented in the paper.

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities.

In modern networking research, infrastructure-assisted unmanned autonomous vehicles (UAVs) are actively considered for real-time learning-based surveillance and aerial data-delivery under unexpected 3D free mobility and coordination. In this system model, it is essential to consider the power limitation in UAVs and autonomous object recognition (for abnormal behavior detection) deep learning performance in infrastructure/towers. To overcome the power limitation of UAVs, this paper proposes a novel aerial scheduling algorithm between multi-UAVs and multi-towers where the towers conduct wireless power transfer toward UAVs. In addition, to take care of the high-performance learning model training in towers, we also propose a data delivery scheme which makes UAVs deliver the training data to the towers fairly to prevent problems due to data imbalance (e.g., huge computation overhead caused by larger data delivery or overfitting from less data delivery). Therefore, this paper proposes a novel workload-aware scheduling algorithm between multi-towers and multi-UAVs for joint power-charging from towers to their associated UAVs and training data delivery from UAVs to their associated towers. To compute the workload-aware optimal scheduling decisions in each unit time, our solution approach for the given scheduling problem is designed based on Markov decision process (MDP) to deal with (i) time-varying low-complexity computation and (ii) pseudo-polynomial optimality. As shown in performance evaluation results, our proposed algorithm ensures (i) sufficient times for resource exchanges between towers and UAVs, (ii) the most even and uniform data collection during the processes compared to the other algorithms, and (iii) the performance of all towers convergence to optimal levels.

Coarse-Grained Reconfigurable Array (CGRA) architectures are potential high-performance and power-efficient platforms. However, mapping applications efficiently on CGRA, which includes scheduling and binding operations on functional units and variables on registers, is a daunting problem. SiLago is a recently developed VLSI design framework comprising two large-scale reconfigurable fabrics: Dynamically Reconfigurable Resource Array (DRRA) and Distributed Memory Architecture (DiMArch). It uses the Vesyla compiler to map applications on these fabrics. The present version of Vesyla executes binding and scheduling sequentially, with binding first, followed by scheduling. In this paper, we proposed an Integer Linear Programming (ILP)-based exact method to solve scheduling and binding simultaneously that delivers better solutions while mapping applications on these fabrics. The proposed ILP combines two objective functions, one for scheduling and one for binding, and both of these objective functions are coupled with weightage factors $\alpha $ and $\beta $ so that the user can have the flexibility to prioritize either scheduling or binding or both based on the requirements. We determined the binding and execution time of image processing tasks and various routines of the Basic Linear Algebraic Subprogram (BLAS) using the proposed ILP for multiple combinations of weightage factors. Furthermore, a comparison analysis has been conducted to compare the latency and power dissipation of several benchmarks between the existing and proposed approaches. The experimental results demonstrate that the proposed method exhibits a substantial reduction in power consumption and latency compared to the existing method.

Integrated Access and Backhaul (IAB) technology promises to facilitate cost-effective deployments of 5G New Radio (NR) systems operating in both sub-6 GHz and millimeter-wave (mmWave) bands. As full-duplex wireless systems are in their infancy, initial deployments of IAB networks may need to rely on half-duplex operation to coordinate transmissions between access and backhaul links. However, the use of half-duplex operation not only makes the scheduling of links in the IAB networks interdependent, but also the number of their feasible combinations grows exponentially with the network size, thereby posing challenges to the efficient design of such systems. In this paper, by accounting for mmWave radio characteristics, we propose a joint resource allocation and link scheduling framework to enhance the user equipment (UE) throughput in multi-hop in-band IAB systems. We keep the problem in the form of linear programming type for the feasibility of the practical applications. We show that the increased number of uplink and downlink transmission time interval (TTI) configurations does not result in improved UE throughput as compared to two-TTI configuration. Further, we demonstrate that in-band IAB systems tend to be backhaul-limited, and the utilization of multi-beam functionality at the IAB-donor alleviates this limitation by doubling the average UE throughput. Finally, we show that the use of proportional-fair allocations allows the average UE throughput to be improved by around 10% as compared to the max-min allocations.

The availability of low-cost unmanned aerial vehicles (UAVs), or drones, has made their organisation in fleets more feasible. The required coordination for managing these fleets comes with an increased complexity. When used for long-durability, autonomous inspection missions, it is necessary to recharge the drones due to their limited battery capacity. By providing a set of nearby charging stations, the fleets can autonomously recharge and sustain indefinite missions. In order to reduce congestion at these charging stations, effective scheduling of charging cycles can have a significant impact on the mission execution time. In this paper, we propose a novel centralized method for scheduling charging time windows, taking into account the travel distances and occupation of charging stations. We formulate a mixed-integer linear program (MILP) model with two extensions to reduce the computational complexity. The solution to this problem assigns a set of charging windows to each drone, minimizing the mission execution time and ensuring batteries will not fully deplete. The performance of our proposed method is evaluated through a series of experiments, based on a discrete-event simulator. Our results reveal a clear benefit over a greedy approach, reducing the mission execution time by up to 39.8%. Through careful parameter selection, a trade-off between mission execution time and scheduling time can be found.

Containers are recognized for their lightweight and virtualization efficiency, making them a vital element in modern application orchestration. In this context, the scheduler is crucial in strategically distributing containers across diverse computing nodes. This paper presents a novel two-stage container scheduling solution that addresses node imbalances and efficiently deploys containers. The proposed solution formulates the scheduling process as an optimization problem, integrating various objective functions and constraints to enhance server consolidation and minimize energy consumption. The confidentiality of migrated containers is ensured through encryption, and the associated costs are incorporated into the optimization constraints. This approach ensures security in container scheduling, considering container attributes as input features in our proposed attributes-based encryption model. By carefully selecting containers and destination nodes, this work seeks to establish balance within cloud-based clusters. This contributes to the improvement of container orchestration systems and their effectiveness in real-world scenarios. The proposed solution’s efficacy is demonstrated in its ability to efficiently deploy containers in multi-data center cloud environments and seamlessly migrate them between hosts within the same data center or across different data centers. Our results show optimal consolidation with a reduction in the number of running hosts, ranging from 4% to over 18%. Additionally, the solution promotes minimal total power consumption with savings ranging from 3.5 to 16.25 megawatts, while also ensuring balanced server loads.

Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) serves as a customized communication solution designed to meet the specific needs of industrial settings. Though 5G is the promising solution for ZT-DI-M2M, optimized scheduling in 5G remains challenging to design over aspects such as network overload and congestion control, especially in the uplink transmission of ZT-DI-M2M. Effective allocation of resources for Machine-Type Communication (MTC) devices stands out as a pivotal hurdle within the domain of 5G networks. This challenge directly influences the longevity of battery-operated devices and the Quality of Service (QoS) experienced by applications. This paper aims to develop a Group-Based and Energy Aware (GBEA) resource allocation algorithm for M2M communication in the 5G networks. The GBEA algorithm solves the resource allocation problem by initially clustering the active nodes concerning their delays. Consequently, inter and intra-cluster resource distribution occurs through the Gaussian Mixture Model Expectation Maximization (GMM-EM) algorithm. The GBEA algorithm optimizes the resource allocation of M2M devices by factoring delay, energy, proximity, and fairness into the allocation process. The simulation outcomes unveil the superiority of the GBEA scheduling algorithm over established state-of-the-art resource allocation methods. It showcases remarkable enhancements in throughput, delay sensitivity, and energy efficiency, boasting nearly 1.5-fold, 1.75-fold, and 6.5-fold respective improvements.

This paper endeavors a probabilistic framework to ascertain optimal operation of a microgrid with a special focus on challenges that storage systems bring about. The objective function optimizes the operation of renewable energy resources and startup/shutdown costs of nonrenewable energy resources along with the main grid and storage units’ costs as a mixed-integer nonlinear programming (MINLP) problem. The optimization problem is solved based on a modified bird mating optimization (MBMO) algorithm and a novel cumulative mutation process. In order to capture the high uncertainties associated with the market price, Photovoltaic, Wind Turbine output powers, and load demands, a reduced unscented transformation (RUT) method has been exploited. The RUT method can effectively model the correlation of variables using (m+2) sampling points. The framework presented in this paper has considered five case studies with multiple seasonal and property features. Implementing the proposed framework on a typical test microgrid and a real large-scale microgrid proves its effectiveness and accuracy through various operational conditions, changing the storage units’ structure and characteristics, RESs’ correlation modeling, and avoiding convergence to local minimums by adopting a newly mutated population. This extensive analysis provides options for MG operation by studying compound cases and providing solutions for every scenario. Promising results regarding the execution time, cost function and its SD values as well as head-to-head points for battery investment costs have been found.

System-of-systems (SoS) have recently been used in several applications and scenarios in the field of safety, defense, and healthcare. In an SoS environment, the entire system is divided into sub-systems, which provides more flexibility and reduces the management cost of the entire system. SoS have been widely used in healthcare monitoring services, where patients are provided with medical sensors that send their medical measurements to a remote unit for further processing and decision-making. These sensors communicate with an access point using the wireless channel, which gives patients flexibility in mobility and makes the monitoring system more convenient and comfortable. However, sending data over the wireless channel presents several challenges, such as contention between the different sensors in accessing the channel and the bit errors associated with the noisy wireless channel. In this paper, an SoS healthcare monitoring framework is proposed, where a wireless communication protocol is proposed that addresses the sensors’ node network access contention and mitigates the bit errors of the communication channel by providing forward error correction bits to the transmitted packets. In addition, the protocol takes into consideration the sensors’ importance and criticality, such that more important sensors are given more network access time and more error correction bits, which in turn results in a robust transmission process with low transmission delay. The simulation results show the proposed wireless communication protocol’s effectiveness in lowering the packet loss, giving higher priority and having higher throughput for the more critical sensors.

This paper introduces a dynamic scheduling algorithm designed to minimize makespan within a smart manufacturing system, accommodating delays in the production process. The proposed approach relies on Model Predictive Control (MPC) principles and adapts flow-shop scheduling theory to solve an open-shop scheduling problem. It aims to strike a balance between the ideal, delay-free solution and robustness in the case of processing time delays. By combining MPC theory with flow-shop scheduling, the algorithm offers a robust approach to open-shop scheduling problems, even with uncertain processing times. Iterated upon the arrival of each new job on the shop floor, the algorithm incorporates a control horizon to predict impending job arrivals and seamlessly integrates them into the scheduling process. Efficiency is examined through a comprehensive case study, where it is compared against a similar, offline scheduling algorithm. This novel method not only optimizes scheduling but also adapts to dynamic scenarios, reducing the computational demand and the information needed to optimize the production process, thus making it suitable for agile manufacturing environments. The results demonstrate the algorithm’s efficacy in achieving competitive scheduling performance with nearly the same makespan as the offline algorithm, while accounting for uncertainties in processing times. A robustness analysis confirms the reliability of the proposed approach, showing an average improvement of 5% in makespan across different delay magnitudes.

IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms.

This paper addresses the problem of scheduling the uplink bandwidth of Low Earth Orbit (LEO) satellites among multiple Internet of Things (IoT) slices with diverse Quality of Service (QoS) requirements. The scheduling process involves twofold decisions—the amount of bandwidth allocated and the allocation duration. Resource scheduling for satellite IoT services is challenging because of limited bandwidth availability during a satellite pass, especially for LEO satellites. Another challenge is to compute a fair allocation schedule for IoT services with different latency demands, packet transmission frequency, required data volume, and the number of IoT devices. To address these challenges, we propose a fairness-aware inter-slice scheduler for satellite IoT services in this work. The proposed scheduler computes service priority based on respective traffic demands. We propose two algorithms for the scheduler based on weighted greedy and Simulated Annealing (SA), respectively. The weighted greedy algorithm schedules the services greedily based on the priority order. The SA algorithm enhances the greedy solution by ensuring the allocated bandwidth is proportional to the respective priority values. The simulation results show that the proposed SA algorithm achieves up to 21.11% more proportional fairness than the Simulated Annealing and Monte Carlo (SA-MC) benchmark scheme.

The IEEE 802.1 Time-Sensitive Networking (TSN) Task Group has been developing various standards since 2012 to provide deterministic and time-critical services through IEEE 802 networks. The Time-Aware Shaper (TAS) is a key component in TSN that was introduced to enable precise forwarding and shaping of time-sensitive network traffic. It allows configuring exclusive transmission slots for selected traffic classes. To achieve the best benefit from the TAS, numerous scheduling algorithms have been presented to find schedules for the transmission of time-critical network streams optimizing the stream end-to-end transmission times in TAS-based networks. However, most existing works do not address how the scheduling of critical network traffic affects the necessary number of critical transmission slots that is limited in hardware and the resulting bandwidth utilization for other traffic classes. In this paper, we have investigated how a selected set of heuristic and meta-heuristic algorithms for scheduling critical network streams in TAS-based networks affects the utilization of network resources, and how the configuration of TAS can be improved using a compression algorithm. Therefore, we make use of existing algorithms and propose alternatives that have an influence on input data ordering, scheduling procedures, and the TAS configuration. The evaluation showed that some of our proposed modifications can improve bandwidth utilization and reduce the number of transmission slots for critical traffic significantly. Further on, the compression algorithm can bring a remarkable improvement in isolated cases.

Time-Sensitive Networking (TSN) is an enhancement of Ethernet which provides various mechanisms for real-time communication. Time-triggered (TT) traffic represents periodic data streams with strict real-time requirements. Amongst others, TSN supports scheduled transmission of TT streams, i.e., the transmission of their frames by end stations is coordinated in such a way that none or very little queuing delay occurs in intermediate nodes. TSN supports multiple priority queues per egress port. The TAS uses so-called gates to explicitly allow and block these queues for transmission on a short periodic timescale. The TAS is utilized to protect scheduled traffic from other traffic to minimize its queuing delay. In this work, we consider scheduling in TSN which comprises the computation of periodic transmission instants at end stations and the periodic opening and closing of queue gates. In this paper, we first give a brief overview of TSN features and standards. We state the TSN scheduling problem and explain common extensions which also include optimization problems. We review scheduling and optimization methods that have been used in this context. Then, the contribution of currently available research work is surveyed. We extract and compile optimization objectives, solved problem instances, and evaluation results. Research domains are identified, and specific contributions are analyzed. Finally, we discuss potential research directions and open problems.

Project scheduling is one of the most essential processes and plays a critical role in determining the success of construction projects. The reliable time contingency enables project planners to effectively address uncertainties and various types of risks that may affect the project duration. The traditional scheduling technique such as deterministic methods and probabilistic methods may not be suitable when used for planning construction projects with uncertainties and risks. This paper proposes a new framework that integrates risk management into project scheduling to establish a more reliable project schedule. The proposed model adopts the adaptive neuro-fuzzy inference system (ANFIS) called the ANFIS-TOOL to model the possibility of risk occurrences integrated with project scheduling in terms of risk lag time (ANFIS-RLT) to generate risk-integrated project duration (RPD). The learning capabilities of adaptive neural networks are utilized to adjust the parameters of the model according to the fuzzy rules, aiming to achieve the most suitable representation of the model. In a real-life case study, a sheet pile wall with a temporary bracing system (SPBS) was applied to demonstrate the application of this technique. The root mean square error (RMSE) was used to validate the accuracy of the model before being applied to real construction projects with a high degree of accuracy. This model can be used as an excellent tool to generate a more reliable schedule baseline in construction projects.

Microgrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs within the microgrid are met without wasting electricity. With higher requirements for electric vehicle charging stations (EVCSs), schedulers are essential to ensure EV charging demands are met while being profitable and flattening peak load on the main power grid (MPG). This paper introduces two novel microgrid models, combining energy generated by a DER, the possibility of storage with an energy storage system (ESS), a load entity in the form of an EVCS and electricity trading with the MPG. The model incorporates all important environment parameters created by these players in an intelligent way that keeps the action space relatively small and thus avoiding the problems associated with a high computational complexity. These models are proven to successfully shift the load from the MPG, while still providing high customer satisfaction and throughput, in a profitable way, despite costs incurred by the DER. Instead of relying on models, real data is used, ensuring that the model is robust. Additional real world stress tests are carried out with respect to electricity costs, wind energy generation, and charging rates. Reinforcement learning is implemented to find the optimal scheduler by maximizing overall profits. In all cases considered a self-sustaining system is established, that is a more profitable and reliable EVCS.

Time-Sensitive Networking (TSN) has enabled a lot of advancements in industrial automation, aviation, tactile networking, and other ad-hoc networking applications. The bounded latency, reliability, and self-recovering mechanisms for a network are some of the core attributes of the TSN architecture. The reliability of bench-marking of a given TSN architecture, when done with simulators, will not match with that of hardware systems. But the TSN hardware needs more capital investment along with more development time as it involves understanding the hardware-specific parameters. The research in this paper has solved this shortcoming by implementing an open-source and secured SDN(Software Defined Networking)-based TSN framework integrating IEEE’s 802.1 Qbv and 802.1Qcc standards. The novelty of implementation involves i) Realization of Centralized User Configuration (CUC), Centralized Network Configuration (CNC) with open source tools. ii) An algorithm called Dynamic TSN Path Finder (DTPF) is implemented for automatically identifying TSN edges participating in TSN flow. iii) Emulation of hardware environment leveraging Linux-based queuing disciplines and traffic shapers. This unique open source-based TSN architecture is then tested with both TSN and Non-TSN traffic, to demonstrate the gating logic and the delay characteristics based on queuing discipline when applied to virtual queues.

In dynamic real-time systems (RTS), the synchronous communication model is a source of unpredictable behaviors caused by the difficulty of estimating the maximum lockdown time in a process. Inter-task communication is a critical issue in RTS, even in the case of uniprocessor architectures. Using an FPGA-based development platform, through an SoC project, the implementation of HW_nMPRA_RTOS (a unified acronym for multi pipeline register architecture (nMPRA) where $n$ is the degree of datapath resource multiplication, hardware scheduler engine (nHSE) for $n$ threads and RTOS application programming interface (API)), dedicated processor architecture was developed, simulated and validated. This paper proposes an innovative soft-core implementation to reduce interrupt latencies while maintaining strong spatial and temporal isolation. Among the results which contain relevance and novelty, we can mention: rapid tasks context switching (1 clock cycle); The implementation of a distributed and versatile interrupt system that allows the interrupt attachment to any task; The implementation of a static scheduler and support for the dynamic tasks scheduling; Rapid response to events of up to 2 clock cycles. We demonstrate the architecture’s predictability, scalability, and performance by running a set of benchmark applications on several configurations of HW_nMPRA_RTOS synthesized on a Xilinx 7 Series FPGA.

The wafer transfer robot is a key part of integrated circuit equipment which performs the transit of wafers precisely, quickly and steadily. The dynamic accuracy of the manipulator of the wafer transfer robot directly affects the quality of transferring and processing wafers and even the scheduling and control of the cluster tools. Thus, it is essential to study the influence of the dynamic accuracy of the manipulator of wafer transmission robots on the scheduling and control of cluster tools. In this paper, single-arm cluster tools are taken as the research object. The horizontal torsional vibration equations of the manipulator of the $R$ - $\theta $ robot are constructed, and the torsional vibration attenuation characteristics of the manipulator are analyzed. Based on the torsional vibration equations, the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator are explored when the manipulator loads and unloads the wafers. Then the two-stage approach is proposed for the scheduling and control of single-arm cluster tools. The first stage determines the minimum waiting times of the manipulator according to the intrinsic relationships between the dynamic accuracy of the manipulator and the waiting times of the manipulator when the manipulator is waiting for loading and unloading wafers in each processing module and load lock. The second stage achieves the scheduling optimization and control of single-arm cluster tools with dynamic accuracy constraints and wafer residency time constraints by establishing a mathematical programming model for the scheduling and control of single-arm cluster tools. Finally, illustrative examples are presented to analyze the influence of the dynamic accuracy of the manipulator on the scheduling and control of single-arm cluster tools.

In wireless sensor networks (WSNs), target coverage is an important issue which aims at finding a set of sensors to monitor the targets for maximizing both the surveillance quality and network lifetime. However, most of them assumed that each sensor is battery powered and the Boolean Sensing Model (BSM) is applied. Sensors powered by battery have a limited lifetime while the BSM is difficult to reflect the physical features of sensing. This paper proposes target coverage mechanisms, called C-MMQT and D-MMQT, which consider the solar-powered sensors and allows the battery to be recharged for maintaining the perpetual lifetime of sensor networks. The proposed mechanisms apply the Probabilistic Sensing Model (PSM) and consider that different targets have different importance. Two challenges have been overcome in this paper. First, each sensor is well scheduled for switching between recharging and working states for maintaining its perpetual lifetime since its battery is solar powered. Second, the sensors that are able to monitor the common target are well scheduled for cooperative sensing to maximize the surveillance quality since PSM is applied. Two efficient sensor schedules are proposed to maximize the surveillance quality of the bottleneck target which has the lowest surveillance quality. Performance study shows that the proposed mechanisms outperform the existing mechanisms in terms of Quality of Monitoring, Average utility, Fairness and Efficiency index.

With the rise of green concept, shared bikes are booming. The accompanying unbalanced scheduling problem is a scientific problem that needs to be solved urgently. Aiming at the problem of shared bikes scheduling with travel uncertainty, a multi-objective integer programming model is established based on the consideration of static demand of fix time period, station capacity limit, penalty cost and other practical factors. In addition, this paper gives the basic formula to calculate the parameters in the model. An algorithm based on “ant colony algorithm” is then given to solve the model. Taking the massive data provided by the “Mobike” company in 2017 as an example, this paper uses the program analysis data to prove the feasibility and effectiveness of the model and get the initial optimization plan. Finally, the data simulation is carried out to verify the feasibility and accuracy of the optimization scheme and the optimization scheme is adjusted accordingly to obtain the final optimization scheme. The research results show that the final optimization scheme proposed in this paper has certain reference value for the scheduling problem of Shanghai “Mobike”.

This paper considers a time window periodic maintenance strategy with different duration windows and job scheduling activities in a single machine environment. The aim is to minimize the number of tardy jobs through the integration of production scheduling and periodic maintenance intervals. A mixed-integer linear programming model (MILP) is proposed to optimize small-sized test instances. Furthermore, an ant colony optimization (ACO) algorithm is developed to solve larger sized test instances. Subsequently, to measure the efficiency of the solutions obtained by ACO, Moore's algorithm is also developed to benchmark with ACO. To test the efficiency and the effectiveness of the ACO algorithm, a set of data for small and large sized problems was generated in which several parameters were adopted and then ten replicates were solved for each combination. The small sized instances were solved by the MILP. Then, the results obtained showed that the proposed ACO was able to obtain the exact solutions within reasonable CPU times, thus, it outperformed the CPLEX solver with respect to CPU. The large sized instances were solved by the Moore's algorithm and compared to ACO. Then, the results obtained showed that the ACO outperforms Moore's algorithm for all the instances tested. It can be concluded that the developed ACOis very efficient and effective in solving the problem considered in this paper.

This paper develops a stochastic framework for the energy management of a microgrid to minimize the energy cost from the grid. It considers the uncertainties in solar photovoltaic (PV) generation, load demand, and electricity price. Furthermore, the opportunity of flexible load demand, i.e., the effect of demand response (DR), on the test system is studied. The uncertainties are modeled by using Monte Carlo simulations and the generated scenarios are reduced to improve the computational tractability. In general, microgrid scheduling is implemented by using substation (source node) price as a reference, but that reference price is not the same at all nodes. Therefore, this paper develops the nodal price based energy management in a microgrid to improve the scheduling accuracy. The stochastic energy management framework is formulated as a mixed integer non-linear programming (MINLP). Four case studies are simulated for a modified 15-node radial distribution network integrated with solar PV and battery energy storage system (BESS) to validate the effectiveness of the energy management framework for a microgrid with nodal pricing.

In the paper, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this paper involves many attributes, including working calendar, due dates, and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for the online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of the practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm, and improve the solution quality. To this end, three multi-agent-based hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for the online scheduling problem. To evaluate the performance of evolved SPs, a 5-fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent-based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multi-objective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded in the manufacturing execution system that is built on JAVA and successfully applied in several manufacturing plants.

In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster. Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services. In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud. From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails. This wastes much time and resources as the remaining instances are running for an impossible successful termination. 2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources. Such clustering can help the co-allocation of online services jobs with batch jobs. 3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations. Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation. 4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds. We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them. Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design.

Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the non-dominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios.

In this paper, a cooperative solution for vehicle-to-everything (V2X) communications is proposed and presented, which can guarantee the reliability and latency requirements for the 5G enhanced V2X (eV2X) services. Cooperation is useful for both in-coverage and out-of-coverage vehicular communications' scenarios. The proposed solution relies on the sidelink (SL) device-to-device (D2D) communications for the V2X communications. In this paper, we first provide a performance evaluation of the SL D2D V2X communications in terms of resource allocation and scheduling. The resource allocation is known as modes 3 and 4 SL D2D communications, and the scheduling is using a semi-persistent scheduling (SPS) approach. Simulation results are obtained in order to identify and highlight the reliability tradeoffs considering different payload sizes and SPS parameters. In the sequel, a cooperative solution that decreases the transmission collision probability is devised and presented, which is able to significantly improve the reliability of the future 5G eV2X communications. Different application scenarios are simulated to obtain the results that can guarantee the latency requirements per 5G eV2X use case as specified in 3GPP Rel.16 toward ultrareliable and low latency communications.

This paper studies resource scheduling for delay minimization in multi-server cellular edge computing systems. The traditional method defines queue length-based Lyapunov functions and designs scheduling algorithms which solve the corresponding queue length control problem. Different from the traditional method, this paper defines the delay-based Lyapunov function. Specifically, the formulas of the communication delay and computing delay in cellular edge computing systems are derived without needing the assumptions about traffic's statistics. Then, a resource scheduling algorithm which directly minimizes the weighted sum of the communication delay and computing delay is proposed. The simulation results show that the total delay of the proposed scheduling algorithm is decreased as compared to that of the traditional method. The impact of parameters on the delay performance is also evaluated.

The usage and adoption of electric vehicles (EVs) have increased rapidly in the 21st century due to the shifting of the global energy demand away from fossil fuels. The market penetration of EVs brings new challenges to the usual operations of the power system. Uncontrolled EV charging impacts the local distribution grid in terms of its voltage profile, power loss, grid unbalance, and reduction of transformer life, as well as harmonic distortion. Multiple research studies have addressed these problems by proposing various EV charging control methods. This manuscript comprehensively reviews EV control charging strategies using real-world data. This review classifies the EV control charging strategies into scheduling, clustering, and forecasting strategies. The models of EV control charging strategies are highlighted to compare and evaluate the techniques used in EV charging, enabling the identification of the advantages and disadvantages of the different methods applied. A summary of the methods and techniques for these EV charging strategies is presented based on machine learning and probabilities approaches. This research paper indicates many factors and challenges in the development of EV charging control in next-generation smart grid applications and provides potential recommendations. A report on the guidelines for future studies on this research topic is provided to enhance the comparability of the various results and findings. Accordingly, all the highlighted insights of this paper serve to further the increasing effort towards the development of advanced EV charging methods and demand-side management (DSM) for future smart grid applications.

This paper studies a single-machine scheduling problem with a two competing agents in which the performance criteria of the first and second agents are to minimize the mean lateness and number of tardy jobs, respectively. Due to the non-deterministic polynomial-time hardness of this problem, we propose an effective and efficient algorithm, denominated as the SPT-M algorithm, to generate the non-dominated solutions of the Pareto set. Computational results conducted on a test problem set reveal that the proposed SPT-M algorithm can generate an efficient Pareto frontier in remarkably short computing time. The contribution of this paper could help practitioners to determine the tradeoffs between the jobs of two agents competing for a single resource.

In a sensor, mesh, or ad hoc network, different transmissions take place between multiple source-destination pairs, which is referred to as data flows. In this paper, each data flow is associated with a certain duty cycle schedule to avoid collisions of multiple flows in an energy-efficient manner. Cooperative routes in the network are assumed to be linear and strip-shaped, where each hop or a level contains multiple nodes, and the distance between the nodes is kept constant. We assume all nodes in a cooperative route, which corresponds to one source-destination pair, follow the same duty cycle schedule, while schedules of different routes are orthogonal in time to avoid interference between routes. Therefore, when a new route is formed, it is important to select a duty cycle schedule that is not already in use by the nodes participating in the new route. As part of route set-up, an orthogonal frequency division multiplexing packet travels from the source to the destination, identifying the schedules in use by the nodes along the path. Our objective in this paper is to analyze the probabilities of detection for this method of detecting schedules, which is useful to gain insights into duty cycling-based medium access protocol design in the presence of multiple data flows. To this end, the successive transmissions in a linear network are modeled using an irreducible discrete time Markov chain. Also, three different approaches to detect a certain schedule are investigated, which are distinguished by the binary integration process with multiple sub-carriers corresponding to the schedule. We derive the probability transition matrix for the Markov chain based on different distributions of the received signal energy, the left eigenvector of which yields the probability of detection.

Abstract This paper contributes to the well-known challenge of active user participation in demand side management (DSM). In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency. Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness. In this paper, a personalized–real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it. Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare (user satisfaction) of electricity consumers. The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

In this paper, we consider the problem of scheduling shiftable loads, over multiple users, in smart electrical grids. We approach the problem, which is becoming increasingly pertinent in our present energy-thirsty society, using a novel distributed game-theoretic framework. In our specific instantiation, we consider the scenario when the power system has a local-area Smart Grid subnet comprising of a single power source and multiple customers. The objective of the exercise is to tacitly control the total power consumption of the customers' shiftable loads, so to approach the rigid power budget determined by the power source, but to simultaneously not exceed this threshold. As opposed to the “traditional”paradigm that utilizes a central controller to achieve the load scheduling, we seek to achieve this by pursuing a distributed approach that allows the users1 to make individual decisions by invoking negotiations with other customers. The decisions are essentially of the sort, where the individual users can choose whether they want to be supplied or not. From a modeling perspective, the distributed scheduling problem is formulated as a game, and in particular, a so-called “Potential”game. This game has at least one pure strategy Nash equilibrium (NE), and we demonstrate that the NE point is a global optimal point. The solution that we propose, which utilizes the theory of learning automata (LA), permits the total supplied loads to approach the power budget of the subnet once the algorithm has converged to the NE point. The scheduling is achieved by attaching a LA to each customer. The paper discusses the applicability of three different LA schemes, and in particular, the recently-introduced Bayesian learning automata. Numerical results, obtained from testing the schemes on numerous simulated data sets, demonstrate the speed and the accuracy of proposed algorithms in terms of their convergence to the game's NE point.

It is a great challenge to find an optimal one-wafer cyclic schedule for a single-arm multicluster tool that is widely adopted in semiconductor fabrication. Aiming to tackle this significant problem, an optimal scheduling strategy is determined first for each individual tool under the condition that the bottleneck individual tool is transport-bound. Then, by developing a Petri net model with robot waiting being explicitly described to reveal the properties of the entire system, this paper shows that to schedule such a tool optimally is to allocate the robot waiting time properly. Then, this paper presents the necessary and sufficient conditions for the existence of an optimal one-wafer cyclic schedule. Thereafter, an efficient algorithm is developed to check the given conditions and find such a schedule efficiently if existing. Finally, two industrial examples are used to verify that the proposed method is applicable and effective.

This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared channel. This scenario presents multiple challenges in terms of radio resource scheduling, link adaptation, and inter-cell interference, which are identified and addressed throughout this paper. We propose a joint link adaptation and resource allocation policy that dynamically adjusts the block error probability of URLLC small payload transmissions in accordance with the instantaneous experienced load per cell. Extensive system-level simulations of the downlink performance show promising gains of this technique, reducing the URLLC latency from 1.3 to 1 ms at the 99.999% percentile, with less than 10% degradation of the eMBB throughput performance as compared with conventional scheduling policies. Moreover, an exhaustive sensitivity analysis is conducted to determine the URLLC and eMBB performance under different offered loads, URLLC payload sizes, and link adaptation and scheduling strategies. The presented results give valuable insights on the maximum URLLC offered traffic load that can be tolerated while still satisfying the URLLC requirements, as well as what conditions are more appropriate for dynamic multiplexing of URLLC and eMBB traffic in the upcoming 5G systems.

This paper presents a cloud theory-based iterated greedy (CTIG) algorithm for solving the no-wait flowshop scheduling problem (NWFSP) with the objective of minimizing the sum of makespan and total weighted tardiness. The performance of the proposed CTIG algorithm is evaluated by comparing its computational results to those of the best-to-date meta-heuristic algorithm, particle swarm optimization (PSO), as presented in this paper. The experimental results concerning two sets of benchmark problem instances in this paper demonstrate that the CTIG algorithm obtains more (near) optimal solution in less computational time than the PSO algorithm. The computational results in this paper fill the research gap in the development of a novel algorithm to improve the solution quality in the case of the NWFSP with the objective of minimizing the sum of makespan and total weighted tardiness.

Finite network lifetime severely limits the usage of sensor networks. To prolong the lifetime, researchers adopt mobile chargers to recharge sensors with external power sources. Prior studies customarily assumed that a mobile charger sequentially visits and charges sensors. However, this method is inefficient, since it normally incurs considerable charging waiting time. To address the limitation, in this paper, we propose to replenish sensors with a mobile worker carrying multiple portable chargers. The worker simultaneously charges multiple sensors in a small region by deploying the chargers onto the sensors. We claim that this parallel charging mode significantly reduces waiting time incurred per sensor and thus improves charging efficiency. Based on the novel design, this paper contributes two novel approaches called periodic multi-charger (PMC) and on-demand multi-charger (OMC). The PMC offers guaranteed and sustainable power supplies via periodic charging schedules, while the OMC derives charging schedules in real time to adapt to the dynamism in energy consumption patterns. Both the algorithms achieve high charging efficiency by simultaneously charging sensors with multiple chargers in each charging round. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches.

In this paper, we consider a generalized extensible bin packing problem with overload cost, first proposed by Denton et al. in 2010, in which the total size of items packed into a bin is allowed to exceed its capacity, and the cost incurred each bin is equal to the fixed cost plus the overload cost, the objective is to minimize the total cost of all bins. According to the characteristics of the problem, we first propose an improved ant colony optimization algorithm (IACO), which enhances the positive feedback effect of ACO by improving the update method of pheromone and the adaptive adjustment parameters. We also introduce a variable neighborhood search method in ACO to improve the convergence of the algorithm and get rid of the phenomenon of local extrema. Then, we present a discrete particle swarm optimization algorithm (DPSO) to solve the problem. In order to ensure the uniform distribution and high quality of the initial particle swarm, we use some heuristic methods in the initialization process of the swarm, so that the initial particle can cover the entire search space with a large probability, which effectively improves the performance of DPSO algorithm. Finally, we compare and analyze the performance of these proposed algorithms through two sets of computational experimental frameworks. Compared with some algorithms in the literature, computational results signify that the improved ACO algorithm and MDPSO algorithm are more competitive than some other metaheuristic algorithms.

This paper considers the inland waterborne transport (IWT) problem, and presents a scheduling approach for inland vessels and locks to generate optimal vessel and lock timetables. The scheduling strategy is designed in the switching max-plus-linear (SMPL) systems framework, as these are characterized by a number of features that make them well suited to represent the IWT problem. In particular, the resulting model is linear in the max-plus algebra, and SMPL systems can switch between modes, an interesting feature due to the presence of vessel routing and ordering constraints in the model. Moreover, SMPL systems can be transformed into mixed-integer linear programming (MILP) problems, for which efficient solvers are available. Finally, a realistic case study is used to test the approach and assess its effectiveness.

This article provides a review about how uncertainties in increasingly complex production and supply chains should be addressed in scheduling tasks. Uncertainty management will be particularly important in Industry 5.0 solutions that will require the close integration of operators and technical systems. To prepare for these challenging developments, this work reviews the sources of uncertainty and the scheduling algorithms that deal with the different types of models developed to handle the uncertain nature of the elements and the environment of complex technological systems. The paper not only identifies the challenges, but also the main building blocks that can help to manage and reduce uncertainties based on the I4.0 and I5.0 solutions. We hope that this study will serve as a starting point for R&D projects and algorithm developments, which will be needed primarily in the field of multi-agent, multistage and inverse optimizations.

Radar and communication coexistence is an upcoming technology with numerous research opportunities in the medium access control (MAC) layer, particularly in scheduling and radio resource management (RRM). More efficient scheduling algorithms are needed with the wide range of applications that the wireless environment is experiencing. We investigate an echo-based scenario in the radar-aided vehicular communication system in which an echo is reflected from a target. Unlike the conventional scheduling mechanisms where signal-to-interference-plus-noise ratio (SINR) is exploited, this paper proposes a new radar-aided communication scheduling algorithm by utilizing parameters such as range and velocity with the classical SINR measurements. The proposed algorithm schedules the available resources by extracting information from the radar echo. The proposed radar-aided communication scheduling scheme provides a more flexible design by adding new parameters, resulting in a more efficient algorithm in a broad variety of scenarios. The proposed scheme is beneficial for B5G communication systems that allow localization and sensing as key features of next-generation wireless networks.

Several emerging applications in wireless communications are required to achieve low latency, but also high traffic rates and reliabilities. From a latency point of view, most of the state-of-the-art techniques consider the average latency which may not directly apply to scenarios with stringent latency constraints. In this paper, we consider scheduling under a max-delay constraint; this is an NP-hard problem. We propose a novel approach to tackle the scheduling problem by directly addressing the constraint. We consider the downlink of a multi-cell wireless communication network with nodes communicating with users each facing their own delay constraint on randomly arrived packets. Packets must be scheduled to meet the users’ delay constraints. Our main contributions are first, proposing a new search approach, Super State Monte-Carlo Tree Search (SS-MCTS), as a version of regular MCTS modified for large-scale probabilistic environments; second, developing trained value and policy networks to reduce computational complexity, and finally, addressing the scheduling problem through a reinforcement learning framework. Our numerical results demonstrate that the proposed approach significantly improves the packet delivery rate over a baseline approach while meeting the max-delay constraint, and addressing the scalability as the main issues in large action-state spaces.

This paper introduces $\Delta $ -MILP, a powerful variant of the mixed-integer linear programming (MILP) optimization framework to solve NASA’s Deep Space Network (DSN) scheduling problem. This work is an extension of our original MILP framework (DOI:10.1109/ACCESS.2021.3064928), and inherits many of its constructions and strengths, including the base MILP formulation for DSN scheduling. To provide more feasible schedules with respect to the DSN requirements, $\Delta $ -MILP incorporates new sets of constraints including 1) splitting larger tracks into shorter segments and 2) preventing overlapping between tracks on different antennas. Additionally, $\Delta $ -MILP leverages a heuristic to balance mission satisfaction and allows to prioritize certain missions in special scenarios including emergencies and landings. Numerical validations demonstrate that $\Delta $ -MILP now satisfies 100% of the requested constraints and provides fair schedules amongst missions with respect to the state-of-the-art for the most oversubscribed weeks of the years 2016 and 2018.

The primary focus of most approaches that model interference in WSN is to understand the conditions under which the signals received by a destination node are interfered by the transmissions of other nodes. While these models support the basic understanding of interference phenomenon between two nodes; models that attempt to map the nodes whose transmissions can potentially interfere with other nodes is highly desirable. To this end, we propose the Composite Interference Mapping (CIM) model that provides a holistic framework for determining a comprehensive map of all potential interferers for each and every active link in the WSN. Based on CIM model, we propose three Interference-Fault Free Transmission (IFFT) scheduling algorithms viz., (i) IFFT-STDMA and IFFT-ESTDMA for topology-free, and (ii) LL-IFFT-ESTDMA for tree-structured WSNs, which also addresses the Exposed Station (ES) and Hidden Terminal (HT) problems. We present the analytical studies to support the CIM model developed in the paper. The results of the simulation experiments have shown that the proposed CIM-based IFFT algorithms perform much better compared to some of the well-known algorithms in performance metrics such as: number of time slots, packet drop, number of dead nodes, honouring precedence relations, and addressing ES and HT problems.

Electric vehicles (EVs) are widely deployed throughout the world, and photovoltaic (PV) charging stations have emerged for satisfying the charging demands of EV users. This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system (CBSCS), in order to enhance the economic efficiency while reducing its adverse effects on power grid. The proposed method involves a multi-objective optimization scheduling model, which minimizes the total operation cost and smoothes load fluctuations, simultaneously. Afterwards, we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III (NSGA-III) for solving this scheduling problem. Finally, simulation studies verify the effectiveness of the proposed multi-objective operation method.

The relentless technology scaling has provided a significant increase in processor performance, but on the other hand, it has led to adverse impacts on system reliability. In particular, technology scaling increases the processor susceptibility to radiation-induced transient faults. Moreover, technology scaling with the discontinuation of Dennard scaling increases the power densities, thereby temperatures, on the chip. High temperature, in turn, accelerates transistor aging mechanisms, which may ultimately lead to permanent faults on the chip. To assure a reliable system operation, despite these potential reliability concerns, fault-tolerance techniques have emerged. Specifically, fault-tolerance techniques employ some kind of redundancies to satisfy specific reliability requirements. However, the integration of fault-tolerance techniques into real-time embedded systems complicates preserving timing constraints. As a remedy, many task mapping/scheduling policies have been proposed to consider the integration of fault-tolerance techniques and enforce both timing and reliability guarantees for real-time embedded systems. More advanced techniques aim additionally at minimizing power and energy while at the same time satisfying timing and reliability constraints. Recently, some scheduling techniques have started to tackle a new challenge, which is the temperature increase induced by employing fault-tolerance techniques. These emerging techniques aim at satisfying temperature constraints besides timing and reliability constraints. This paper provides an in-depth survey of the emerging research efforts that exploit fault-tolerance techniques while considering timing, power/energy, and temperature from the real-time embedded systems’ design perspective. In particular, the task mapping/scheduling policies for fault-tolerance real-time embedded systems are reviewed and classified according to their considered goals and constraints. Moreover, the employed fault-tolerance techniques, application models, and hardware models are considered as additional dimensions of the presented classification. Lastly, this survey gives deep insights into the main achievements and shortcomings of the existing approaches and highlights the most promising ones.

Industrial waste is a major environmental concern. This paper proposes an optimization approach to better manage the operations of a food production line, with the aim of minimizing material waste. The configuration considered is inspired by a real situation. It is a two-stage flow shop with dedicated machines at stage 1 and identical parallel machines at stage 2. A mathematical model is first proposed that considers the minimization of the number of idle times exceeding an acceptable threshold as an objective. A positive time lag for the product and machine setup is considered as a constraint. To solve large problem instances, a hybrid genetic algorithm (HGA) is proposed. In addition, we developed a two-phase procedure based on a decision tree to select the parameters of the HGA. The results show that we could achieve the minimization of material waste without altering too much, another industrial goal, which is the total production time.