Hybrid Shuffled Shepherd-Hippopotamus optimization algorithm for repetitive activity scheduling in construction projects

In Static List Scheduling (SLS), initially the ALAP (As Late As Possible) algorithm is performed, followed by ASAP (As Soon As Possible) algorithm. The time interval for control steps and successor selection are the vital drawback of ASAP algorithm. Hence, in this paper, the optimized scheduling algorithm is proposed for High Level Synthesis (HLS), where the ALAP in SLS is followed by Force Directed Scheduling (FDS). The proposed scheduling algorithm is implemented in Icarus Verilog and targeted in ZC7020 FPGA board. The proposed optimized scheduling algorithm is evaluated in terms of control steps and resource utilization. The evaluation is carried out for various benchmarks such as, EW Filter, IIR filter, FIR filter, AR filter and Discrete Cosine Transform (DCT). The achieved results in terms of the control steps and resource utilization (i.e. LUT and Flip-flop) shows the proposed optimized scheduling algorithm outperforms the conventional Static List Scheduling (SLS) algorithm.

Tasks of an embedded system application are managed by a real-time operating system (RTOS). Most RTOSs adopt just fixed priority scheduling, which is not optimal in all cases. Some applications require earliest deadline first (EDF) scheduling, which is an optimal scheduling algorithm. In order to develop an efficient real-time embedded system, the scheduling algorithm of a RTOS should be selectable. The paper presents a method to customize the scheduling algorithm and the resource access protocol of an OSEK OS using aspect-oriented programming. We define aspects to replace the fixed priority scheduling mechanism of the OSEK OS with an EDF scheduling mechanism. We also define aspects to replace the resource access protocol of the OSEK OS with the resource access protocol for EDF scheduling. By using the aspects, we can customize the scheduling algorithm and the resource access protocol without modifying the original source code. This improves the maintainability of the source code of a RTOS. We have applied the aspects to the OSEK OS and have got a customized RTOS with EDF scheduling. The evaluation results show that the overhead of aspect-oriented programming is small enough.

Optimal scheduling for energy harvesting mobile sensing devices

Data collection is a key function for wireless sensor networks. There has been numerous data collection scheduling algorithms, but they fail to consider the deep and complex relationship among network lifetime, sink aggregated information and sample cycle for wireless sensor networks. This paper gives the upper bound on the sample period under the given network topology. An optimal schedule algorithm focusing on aggregated information named OSFAI is proposed. In the schedule algorithm, the nodes in hotspots would hold on transmission and accumulate their data before sending them to sink at once. This could realize the dual goals of improving the network lifetime and increasing the amount of information aggregated to sink. We formulate the optimization problem as to achieve trade-off among sample cycle, sink aggregated information and network lifetime by controlling the sample cycle. The results of simulation on the random generated wireless sensor networks show that when choosing the optimized sample cycle, the sink aggregated information quantity can be increased by 30.5%, and the network lifetime can be increased by 27.78%.

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

Objective: To develop a task scheduling algorithm that efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment, optimizing resource utilization, execution time, cost, and overall system performance. Method: A Modified Sunflower Optimization Algorithm for Task Scheduling (MSOTS) is proposed to improve efficiency in cloud environments. The traditional sunflower optimization algorithm is enhanced with Levy flight, crossover, and mutation operations to achieve a better balance between exploration and exploitation while preventing entrapment in local minima. These enhancements help improve convergence speed and solution quality. CloudSim is utilized for comprehensive performance evaluation, comparing MSOTS with existing algorithms in terms of execution time, cost, and resource utilization. The randomly generated dataset (500 tasks and 50 VMs) is used for analyze the performance of the MSOTS. Findings: The results demonstrate that MSOA significantly reduces makespan and cost while enhancing resource utilization. Specifically, the proposed method reduces makespan by 22.43% compared to PSO and 14.95% compared to SOA. Additionally, cost is reduced by 14.47% and 10.38% compared to PSO and SOA, respectively, while resource utilization increases by 3.89% and 2.51% over these methods. These findings indicate that MSOA effectively improves task scheduling performance in terms of makespan, cost efficiency, and resource utilization, making it a promising approach for cloud computing environments. Novelty: The MSOA is integrated with single-point crossover, swap mutation, and Lévy flight to update the solution, which prevents entrapment in local minima and premature convergence and enhances task scheduling efficiency, optimizes resource allocation, minimizes execution time, and reduces overall costs in cloud computing environments. Keywords: Meta-heuristic, Sunflower optimization, Levy flight, Crossover, Mutation

A Genetic Algorithm for Optimal Resource-driven Project Scheduling

With recent advances in technology, resource control is a significant challenge for geographically distributed clouds. Users geographically close to the server get better services due to low latency. A few existing scheduling algorithms can provide better strategies through efficient job scheduling and resource allocation techniques. However, these algorithms are not efficient enough for distanced clouds. In order to gain maximum profits with optimized scheduling algorithms, it is necessary to utilize resources efficiently and also prioritize the tasks that are near to the servers. This paper proposes job scheduling algorithms in a distributed datacenter (DC) network, where the algorithms assign user's workloads to Virtual Machines (VMs) hosted to DC close to the users to reduce response time. The aspect of the proposed algorithms is the use of delay to evaluate if a VM provide a low or a high delay, it is required that the location of the end user generating the tasks should be known. Our results show that prioritizing tasks for the nearest servers not only improve the quality of service (QoS) but also demonstrates better utilization of the resources.

This paper mainly conducts researches on the optimization and scheduling problems of the location selection for logistics distribution center. In order to effectively savings the transportation costs, and optimize the selection of urban vehicle scheduling path. Especially for the complex road network, there are many parameters for setting, which has made the traditional scheduling algorithm more complex in computation. In addition, it is not conducive to solve the practical application problems. In this condition, in order to avoid the problems caused by the traditional optimal scheduling algorithm for the location selection, including the factors are too many, while the factors are associated tightly with each other, and so on, this paper thus has proposed an improved and optimized ant colony algorithm for logistics location selection. This algorithm has firstly decomposition the obtained solution algorithm to the pairs, and then by using an improved optimized ant colony algorithm, we have transform these solutions from the feature of uncertainties into the ones of certainty, which has greatly de-sized the solution process. Simulation results show that the proposed optimal scheduling algorithm has reduced the computational complexity, optimizing a site model and provide new ways to solve the logistics locating selection problems.

Over the past decade, scheduling in distributed computing system has been an active research. However, it is still difficult to find an optimal scheduling algorithm to achieve load balancing for a specific scientific application which is executed in an unpredictable environment. This is due to the complex nature of the application which changes during runtime and due to the dynamic nature and unpredictability of the computational environment. This paper addresses these issues by presenting a Simulated Annealing (SA) approach as an optimizer which is an improved version of EG-EDF with Tabu Search optimizer. Instead of using Tabu Search, this work used SA to optimize the scheduling algorithm. The scheduling algorithms have been evaluated using three main criteria; number of delayed jobs, makespan time and total tardiness. Our results show the improvements to the main criteria mentioned.

Earlier Deadline First and Rate Monotonic have been proved to be optimal scheduling algorithms. EDF works efficiently in underload condition and RM works efficiently in overload condition. Whereas D_R_EDF algorithm combines the advantages of both EDF and RM algorithms. It has been found that EDF, RM and D_R_EDF algorithms fail to work for incoming periodic tasks not satisfying the constraints imposed by them. A new algorithm capable of scheduling any type of incoming periodic task is proposed. The algorithm presented in this work is an improved version of D _R_EDF algorithm as it is capable of migrating the task(s) to the co-processor which are otherwise missed by the EDF, RM and D_R_EDF algorithms. The new algorithm is found to be performing successfully for all the periodic tasks with and without the constraints of the existing algorithms.

Noise analysis with different scenarios has been conducted for an oil and gas field to determine whether workers during sandblasting operations were fully protected from high noise to prevent ear injuries and downtime. The main purpose is to minimize noise doses affecting workers through an optimal scheduling algorithm. Data collected from a noise meter was fully evaluated and analyzed in terms of LAeq—A-weighted average energy equivalent sound pressure levels—and Lpk energy peak, in the audible range from 12.5 Hz to 20 kHz based on the one-third octave band frequencies. All noise results were found above the maximum permissible exposure limit 85 dB(A). Another important feature is the sharp rise observed in all weighted levels up to a mean value of 67 dB at low frequency of 80 Hz. A sharp decrease in the high frequency noise levels beyond 8 kHz is also observed. Comparisons between different scenarios are also presented. Additional noise sources such as compressors and air coolers add up an important fraction of the workers’ noise exposure dose and highly contribute to the hearing loss. Finally, a fair scheduling algorithm to optimize noise exposure time by minimizing the noise dose is proposed.

An algorithm for optimal scheduling and risk assessment of projects

Biogeography-based optimization algorithm for large-scale multistage batch plant scheduling

This paper describes an exact algorithm for the optimal cyclic scheduling in a robotic cell with flexible processing times and a material handling robot. The problem is formulated with the prohibited intervals of the cycle time based on the capacity constraints of the machines and the robot. After the analysis of the developed mathematic model, the problem is transformed to enumerate the non-prohibited intervals for the cycle time. An efficient branch and bound algorithm is proposed to complete this enumeration procedure. Computational results on randomly generated test instances indicate that the algorithm is effective.