Earticle

Scheduling of dismantling old research reactor need to consider time, cost and safety for the worker. The biggest issue when dismantling facility for research reactor is safety for the worker and cost. Large portion of a budget is spending for the labor cost. To save labor cost for the worker, reducing a lead time is inevitable. Several algorithms applied to reduce read time, and safety considered as the most important factor for this project. This research presents three different dismantling scheduling scenarios. Best scenario shows the specific scheduling for worker and machine, so that it could save time and cost

Aiming to the demand and characteristics of vehicle scheduling optimization problem in emergency rescue, this paper establishes a multi-objective optimization model, which takes minimizing variable bidirectional distance, path risk and cost as the optimization target. To avoid the prematurely falling into local optimization of ant colony system(ACS)algorithm, and to improve the algorithm adaptability, computational efficiency and the quality of optimal solution, this paper proposed and realized a chaos-based improved ant colony system algorithm, which can overall updates the chaos disturbance to pheromone. Simulation results show that the algorithm is feasible, which can well meet the demand of vehicle scheduling optimization in emergency rescue.

Considering the complexity of flexible workshop scheduling, combined with plant production process characteristics and constraints, we constructed a multi-agent system model to solve multi-objective flexible workshop scheduling problems. This paper proposed an algorithm which was a combination of the ant colony algorithm and Q-learning algorithm. This paper also analyzed and implemented how to solve the workshop scheduling optimization problem. Finally, this paper proved the validity of methods to solve the multi-objective flexible workshop scheduling optimization problems with examples on JADE platform.

This paper addresses the problem of the autonomous vehicle storage and retrieval system (AVS/RS) scheduling optimization. AVS/RS relies on rail guide vehicle (RGV) to provide horizontal movement within a tier and uses lifts to provide vertical movement between tiers. Firstly, the process of RGVs’ compound operation is analyzed, and the corresponding mathematical model is established. Then, an improved artificial fish swarm algorithm (IAFSA) is proposed to solve the model. According to the characteristics of the storage and retrieval operation in the system, an encoding and decoding method is designed, which contains RGV task allocation and elevator selection information. The tabu list and the optimal strategy are introduced into this algorithm, coupled with memory action and communication action to avoid the algorithm to trap in local optimal solution. Meanwhile, the adaptive step and visual are used to increase the late convergence of this algorithm. Finally, simulations based on the concrete living example of AVS/RS in a provincial verification center are given.The results obtained by the proposed algorithm are compared with another two optimization algorithm. Analysis shows that the proposed algorithm has the characteristics of fast convergence and the best solution, so as to improve the practicality and robustness of the algorithm.

Enterprises to achieve global optimization and better diversification and personalized product production scheduling, from the unity of the tasks and resources manufacturing, based on the perspective of global optimization in production scheduling method. With global production scheduling of matching and process model resources as the foundation for the realization of enterprise process production scheduling optimization and level continue working process decoupling point, this paper manufacturing task and manufacturing process is divided into generalization and differential theory , applications from manufacturing process optimization results obtained path, based on the manufacturing process optimization mathematical model to study the production scheduling, and by using the improved genetic algorithm, and finally for example. Above scheduling method simplified the scheduling process, reduce the scheduling of difficulty, shorten the time scheduling, and finally achieve enterprise resource efficient use.

In this paper, for efficient energy consumption through the residential demand response in the smart grid, an optimization algorithm, which can provide a schedule plan for the home appliance usages, is proposed. In order to minimize the average electricity price based on the time-varying electricity price in conjunction with the peak hourly load, which decides the capacity of the electric supply facilities, we establish a mixed integer linear programming problem considering various energy consumption patterns of home appliances. In addition, a photovoltaic system and an energy storage are added to the residential side to achieve further efficient schedule plans. By measuring the power consumptions of the home appliances with respect to the time, we constructed the power consumption patterns of each appliance and numerically analyzed the performance of our algorithm by using a real time-varying electricity price and the solar cell power profile obtained through a mathematical model.

In order to obtain the minimum supply and demand scheduling under Cloud manufacturing platform, the fisher fishing algorithm is applied in it. Firstly, the optimization algorithm of fisher fishing is studied. Secondly, the supply and demand scheduling mode under Cloud manufacturing platform is constructed, and the corresponding optimization mathematical model is established. Finally, the simulation results of supply and demand scheduling time is carried out, results show that the fisher fishing algorithm is an effective tool.

In cloud computing environment, there are a large number of users which lead to huge amount of tasks to be processed by system. In order to make the system complete the service requests efficiently, how to schedule the tasks becomes the focus of cloud computing Research. A task scheduling algorithm based on PSO and ACO for cloud computing is presented in this paper. First, the algorithm uses particle swarm optimization algorithm to get the initial solution quickly, and then according to this scheduling result the initial pheromone distribution of ant colony algorithm is generated. Finally, the ant colony algorithm is used to get the optimal solution of task scheduling. The experiment simulated on CloudSim platform shows that the algorithm has good effect in real-time performance and optimization capability. It is an effective task scheduling algorithm.

In the process of coverage for multiple targets, due to the existence of a large number of redundant data make the effective monitoring area coverage decreased and force the network to consume more energy. Therefore, this paper proposes a multi-target k-coverage preservation protocol. First of all, establish the affiliation between the sensor nodes and target nodes through the network model, present a method to compute the coverage expected value of the monitoring area; secondly, in the network energy conversion, using scheduling mechanism in sensor nodes to attain the network energy balance, and achieve different network coverage quality through different nodes energy conversion. Finally, simulation results show that NMCP can effectively reduce the number of active nodes meeting certain coverage requirements and then improve the network lifetime.

Considering the production scheduling problem of processes industry and the disadvantages of conventional differential evolution algorithm, a method of production scheduling oriented to energy consumption optimization for process industry is proposed in this paper, which is based on self-adaptive differential evolution algorithm. Based on the analysis of production scheduling problems for processes industry, a production scheduling model is established, whose goal is to obtain the minimum of total process energy consumption. Since the basic differential evolution algorithm has the disadvantage that the search performance is very sensitive to the parameter settings, the self-adaptive control evolution strategy are used to control the parameters scale factor(F) and crossover probability(CR) to improve the global search ability and convergence speed. A practical production scheduling problem is taken as an example here, the established model and self-adaptive differential evolution algorithm are adopted to realize scheduling simulation with the minimum total process energy consumption. The simulation results show that the production scheduling method oriented to energy consumption optimization is superior to the production scheduling method oriented to process time optimization, and it can realize the goal of reducing energy consumption.

Computational Grid is a high performance computing environment that participating machines resources are used through software layer as transparent and reliable. Task assignment problem in Grid Computing is a NP-Complete problem that has been studied by several researchers. The most common objective functions of task scheduling problems are Makespan and Flowtime. This paper gives a classification of meta-heuristic scheduling algorithms in distributed computing that are applicable to grid environment and addresses scheduling problem of independent tasks on Computational Grids. A Hybrid Discrete Particle Swarm Optimization and Min-min algorithm (HDPSO) is presented to reduce overall Completion Time of task.

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems and often require an optimal solution for the success of a business organization by balancing demand and supply. This work reviews some of the fundamental theory of differential evolution, the methodology for master production scheduling calculation and most important results. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithm. The analysis reveals that the DE algorithm provides a better solution with reasonable computational time.

Task scheduling and resource scheduling are the core issue in cloud computing. Pointing at the premature problem in the scheduling algorithm of particle swarm, we propose a scheduling algorithm of cloud task particle swarm based on “fission” mechanism in this paper. The particle in traditional particle swarm algorithm gets “fission” by the new algorithm in appropriate place, to get more kinds of the particles, contributing to the particle swarm diversity, avoiding premature convergence of the swarm. As the experimental result shows that, the algorithm in this paper has faster scheduling efficiency than the FIFO and the PSO, also solves the premature problem in PSO.

Cloud computing is a new technology provides computing resources as services, and allows users to access these resources via the Internet without the need to own knowledge and experience, or even control of infrastructure that support these services. Job scheduling is considered one of the main issues in cloud computing. The main task of job scheduling is how to find an optimal mapping of set of jobs to a set of available resources. Unsuitable mapping of jobs to resources usually leads to inefficient cloud performance. The current methods for cloud job scheduling process produce acceptable solution but not optimal solution. This paper proposes a new job scheduling mechanism using Glowworm Swarm Optimization (GSO). The proposed mechanism aims to find the best mapping in order to minimize the execution time of jobs. The proposed mechanism based on information of jobs (cloudlets) and resources (virtual machines) such as length of jobs, speed of resources and identifier for both. The scheduling function in the proposed job scheduling mechanism firstly creates a set of jobs and resources to generate the population by assigning the jobs to resources randomly and evaluates the population using fitness values which represent the execution times of jobs. Secondly the function used iterations to regenerate populations based on glowworms behavior to produce the best job schedule that gives the minimum execution time of jobs. The methodology of this research is based on simulation of the proposed mechanism using the CloudSim simulator. The evaluation process of the proposed mechanism started with a set of different experiments. These experiments revealed that, the proposed mechanism minimized the execution time of jobs. The proposed mechanism is compared with the First Come First Servers (FCFS) algorithm and experimental results revealed that the proposed mechanism has a better performance than FCFS for minimizing the execution time of jobs.

The key to improve the container terminal efficiency is the integrated optimization of the quay crane (QC) and the yard truck (YT) scheduling, which is normally settled separately and considered in a certain condition in classical literatures. To improve the operation efficiency and simulate the practical operation, a PSO-based integrated QC-YT scheduling optimization model with uncertain factors is established in this research, considering two uncertain factors of YT travel speed and unit time of QC loading/discharging operation that affect the operating efficiency of the terminal greatly. The goal is the minimal operated time of QCs with the coordination of YTs. To solve this difficult combinatorial problem, the PSO algorithm is developed. PSO is evaluated for combinatorial problems with uncertain factors, which represents a new application of PSO. Numerical experiments show that the model of this research gives systemic simulation for the scheduling process with uncertain factors. And the results are better than model without uncertainties in terms of the accuracy and stability.

The paper proposed a network scheduling in cloud computing based on intelligence Particle Swarm Optimization algorithm aimed at the disadvantages of cloud computing network scheduling. Firstly, on the basis of cloud model, used intelligence Particle Swarm Optimization algorithm with strong ability of global searching to find the better solution of cloud computing network scheduling then turned the better solution into the initial pheromone of improved Particle Swarm Optimization algorithm, and found out the cloud computing network scheduling and the algorithm’s global optimal solution through improved Particle Swarm Optimization information communications and feedbacks. Finally, made comparison test of the three benchmark function on the basis of MATLAB, the results showed, compared with traditional intelligence Particle Swarm Optimization algorithms, the improved algorithm can preferably allocate the resources in cloud computing model, the effect of prediction model time is more close to actual time, can efficiently limit the possibility of falling into local convergence, the optimal solution’s time of objective function value is shorten which meet the user’s needs more.