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Nowadays, the number of vehicles has increased exponentially, but the bedrock capacities of roads and transportation systems have not developed in an equivalent way to efficiently cope with the number of vehicles traveling on them. Due to this, road jamming and traffic correlated pollution have increased with the associated adverse societal and financial effect on different markets worldwide. A static control system may block emergency vehicles due to traffic jams. Wireless Sensor networks (WSNs) have gained increasing attention in traffic detection and avoiding road congestion. WSNs are very trendy due to their faster transfer of information, easy installation, less maintenance, compactness and for being less expensive compared to other network options. There has been significant research on Traffic Management Systems using WSNs to avoid congestion, ensure priority for emergency vehicles and cut the Average Waiting Time (AWT) of vehicles at intersections. In recent decades, researchers have started to monitor real-time traffic using WSNs, RFIDs, ZigBee, VANETs, Bluetooth devices, cameras and infrared signals. This paper presents a survey of current urban traffic management schemes for priority-based signalling, and reducing congestion and the AWT of vehicles. The main objective of this survey is to provide a taxonomy of different traffic management schemes used for avoiding congestion. Existing urban traffic management schemes for the avoidance of congestion and providing priority to emergency vehicles are considered and set the foundation for further research.

Simulation optimization problems with stochastic constraints are optimization problems with deterministic cost functions subject to stochastic constraints. Solving the considered problem by traditional optimization approaches is time-consuming if the search space is large. In this work, an approach integration of beluga whale optimization and ordinal optimization is presented to resolve the considered problem in a relatively short time frame. The proposed approach is composed of three levels: emulator, diversification, and intensification. Firstly, the polynomial chaos expansion is treated as an emulator to evaluate a design. Secondly, the improved beluga whale optimization is proposed to seek N candidates from the whole search space. Eventually, the advanced optimal computational effort allocation is adopted to determine a superior design from the N candidates. The proposed approach is utilized to seek the optimal number of service providers for minimizing staffing costs while delivering a specific level of care in emergency department healthcare. A practical example of an emergency department with six cases is used to verify the proposed approach. The CPU time consumes less than one minute for six cases, which demonstrates that the proposed approach can meet the requirement of real-time application. In addition, the proposed approach is compared to five heuristic methods. Empirical tests indicate the efficiency and robustness of the proposed approach.

Effective process scheduling is essential for enhancing performance metrics like turnaround time, waiting time, and system throughput, especially in maritime computing systems, where real-time task execution and resource efficiency are vital for navigation, communication, and autonomous vessel operations. This paper presents a Dynamic Weighted Round Robin (DWRR) scheduling algorithm, enhanced with Aging and Threshold-Based Quantum Adjustment, to improve task scheduling efficiency in marine contexts. The suggested method dynamically modifies the time quantum according to process activity, incorporating priority levels and total waiting time to guarantee equitable and effective execution of maritime computing activities. Experimental results indicate that the suggested approach markedly enhances scheduling performance by decreasing Average Waiting Time (AWT) to 136 seconds, representing a 12% improvement over RMRR (154 seconds) and a 6.2% enhancement relative to alternative scheduling methods. Furthermore, it decreases Average Turnaround Time (ATT) to 79 seconds, representing a 61.22% enhancement over RMRR (204 seconds), while attaining a Response Time of 45 seconds, a 30.8% decrease relative to RMRR (65 seconds). The technique attains a throughput of 0.0172 and substantially reduces context switches to 6, surpassing current scheduling methodologies. The enhancements render the proposed scheduling framework exceptionally efficient for maritime computing applications, guaranteeing optimal resource usage, reduced latency, and improved system stability in crucial maritime operations.

This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm cannot be dedicated to real time operating system due to their large waiting time, large response time, large turnaround time and less throughput. This new algorithm improves all the drawbacks of round robin CPU scheduling algorithm. In addition, this paper presents analysis comparing proposed algorithm with existing round robin scheduling algorithm focusing on average waiting time and average turnaround time.

Public transportation is one of the most essential services in urban areas. The average passenger waiting time at a stop is an important criterion in evaluating the quality of public transportation systems. Accurate measurement of the average waiting time (AWT) at stops leads to more appropriate and timely schedules. Researchers in the past developed formulations for measuring the AWT at stops based on the assumptions that passengers arrive at stops in a uniform manner and buses arrive at stops independently. Studies on transportation systems in different cities indicate that using these formulations does not result in an accurate measure of the AWT and that there is a significant difference between the observed AWT and the one estimated by these formulations. Researchers related the cause of this discrepancy to the violation of the assumptions of uniform passenger arrival times and independent headways. We show that when the number of buses is not large (even when these assumptions hold true) the existing formulations still lack adequate accuracy. In this paper, we examine the process of formula development that resulted in a formulation that overestimates the AWT; we then propose accurate formulas for estimating the AWT with dependent and independent headways. A discrete simulation model has also been developed to further verify the inaccuracy of the existing formulations and to evaluate the performance of the proposed formulations. Furthermore, as a case study, the AWT at a bus stop in the Tehran bus transportation system has been studied and the accuracy of the proposed formulations has been examined and compared with that of the existing one.

Minimizing time cost in time-shared operating systems is considered basic and essential task, and it is the most significant goal for the researchers who interested in CPU scheduling algorithms. Waiting time, turnaround time, and number of context switches are the most time cost criteria used to compare between CPU scheduling algorithms. CPU scheduling algorithms are divided into non-preemptive and preemptive. Round Robin (RR) algorithm is the most famous as it is the basis for all the algorithms used in time-sharing. In this paper, the authors proposed a novel CPU scheduling algorithm based on RR. The proposed algorithm is called Adjustable Time Slice (ATS). It reduces the time cost by taking the advantage of the low overhead of RR algorithm. In addition, ATS favors short processes allowing them to run longer time than given to long processes. The specific characteristics of each process are; its CPU execution time, weight, time slice, and number of context switches. ATS clusters the processes in groups depending on these characteristics. The traditional RR assigns fixed time slice for each process. On the other hand, dynamic variants of RR assign time slice for each process differs from other processes. The essential difference between ATS and the other methods is that it gives a set of processes a specific time based on their similarities within the same cluster. The authors compared between ATS with five popular scheduling algorithms on nine datasets of processes. The datasets used in the comparison vary in their features. The evaluation was measured in term of time cost and the experiments showed that the proposed algorithm reduces the time cost.

In recent years, attraction to alternative urban mobility paradigms such as electric vehicles (EVs) is increasing since EVs can significantly minimise fossil fuel dependency and reduce carbon emission in urban areas. Nonetheless, there are several barriers toward widespread adoption of EVs. Moreover, as EV penetration increases in urban areas, uncoordinated charging may cause power outage. Deployment of EV charging network can allow EVs to communicate with the service provider to coordinate charging activities. Taking into account, increased growth of EVs, number of charging facilities will be inadequate in urban areas, so efficient EV charging management is required for managing and allocating scarce charging station (CS) resources. In this study, the authors have designed and implemented a smart EV charging management system utilizing charging strategy that includes effective reservation management and efficient slot allocation of CSs. Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, their scheduling scheme shall furnish a set of optimal solutions. Viewing user discontent factor and average waiting time, they have evaluated performance of proposed strategy. The proposed charging strategy is effective than the existing one in terms of average waiting time.

This paper undertakes an examination of elevator car dispatching methods in response to hall calls. Firstly, our study focuses on the establishment of a representation of an elevator group control system as a finite-state machine to understand the dynamics of elevator group control. Secondly, two primary heuristics are explored, with the first advocating directional continuity unless the highest or lowest floor has been reached, while the second permits direction change upon completing the final call, regardless of floor extremes. Identified inefficiencies in these heuristic solutions lead us to explore enhanced alternatives. Consequently, we delve into genetic algorithm (GA) and simulated annealing (SA) methodologies. Our focus initially centers on devising solution representations and determining fitness evaluations for both approaches. We employ a simulation-based optimization approach to identify the optimal parameter values for both simulated annealing and genetic algorithms. A subsequent comparative analysis is conducted to ascertain the most effective approach among these diverse solutions. A comparative analysis reveals that the GA-based approach significantly outperforms both existing heuristics and the SA-based method in minimizing average passenger waiting time at the cost of longer computational time.

The main purpose of an operating system is to control a group of processes, through a method known as CPU scheduling. The performance and efficiency of multitasking operating systems are determined by the use of a CPU scheduling algorithm. Round-robin scheduling is the best solution for time-shared systems, but it is not ideal for real-time systems as it causes more context shifts, longer wait times, and slower turnaround times. Its performance is mostly determined by the time quantum. Processes cannot have priorities set for them. Round-robin scheduling does not give more critical work greater consideration, which may affect system performance in solving processes. On the other hand, a priority algorithm can resolve processes' priority levels. This means that each process has a priority assigned to it, and processes with highest priority are executed first. If which process should come first and the process waiting time in CPU are not considered, this can cause a starvation problem. In this paper, a new CPU scheduling algorithm called the mix PI-RR algorithm was developed. The proposed algorithm is based on a combination of round-robin (RR) and priority-based (PI) scheduling algorithms for determining which tasks run and which should be waiting. The disadvantages of both round-robin and priority CPU scheduling algorithms are addressed by this novel algorithm. When using the proposed mix PI-RR algorithm, the performance measures indicated improved CPU scheduling. Other processes should not be affected by the CPU's requirements. This algorithm helps the CPU to overcome some of the problems of both algorithms.

Task scheduling in cloud computing represents a pivotal challenge, necessitating the efficient allocation of computing tasks to available resources. This challenge is crucial in diverse sectors such as e-commerce, e-learning, and e-health, and is compounded by the heterogeneity of tasks and resources, fluctuating demands, and the need to optimize multiple objectives like Makespan, resource utilization, and throughput. In the quest to resolve these complexities, meta-heuristic algorithms inspired by natural phenomena have gained prominence. Among them, the Tuna Swarm Optimization (TSO) algorithm stands out for its proficient ability to navigate and exploit the search space effectively. This paper introduces a novel algorithm, the Discrete Tuna Swarm Optimization for Task Scheduling (DTSO-TS), derived from the TSO algorithm. DTSO-TS algorithm's goal is to efficiently distribute tasks among virtual machines, balance workloads and improve resource utilization to minimize Makespan while increasing throughput. A fitness function provides optimal solutions to this goal. Creates a swarm before evaluating and refining solutions which have proven their worth. By contrasting it with well-known scheduling algorithms such as Ant-Colony-Based, Particle Swarm Optimisation, Genetic Algorithm, First Come First Serve, Round Robin, and Shortest Job First, we may evaluate DTSO-TS's effectiveness. According to the comparison results, DTSO-TS is the best option for scheduling tasks in cloud computing contexts.