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Smart home scheduling, as one of the most effective techniques in Demand Side Management (DSM), is now attracting more and more research interests in the recent years. In this paper we propose an efficient scheduling algorithm for smart home resident to reduce the monetary cost of the electricity. The proposed algorithm is an improved particle swarm optimization(PSO) algorithm that can schedule the...
Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques,...
The periodic vehicle routing problem (PVRP) can be applied to extend deliveries to a known number of customers in one day to several days (a period). Vehicle routing has to be planned in accordance with different customer clusters with service demand on each day of the period. Therefore, a two-dimensional discrete PSO (TDPSO) is designed in this study to find solutions to the two sub-problems when...
Scheduling continues to be a predominant area of research in computer science, especially with the advent of cloud computing and Internet-scale applications, which require global dissemination and high-availability to meet the variable demand of users from geographically distributed locations. While there are various algorithms which address the problem of scheduling, the issue of assigning compute...
Cloud computing is the network of distributed remote servers to access the information anytime, anywhere. Thus, it also referred to as ubiquitous computing. Cloud computing offers the high performance environment that has great sharing of resources across the distributed servers. Though there are large numbers of tasks to be allocated to these resources, it becomes very necessary to efficiently assign...
Cloud Computing is a new emerging paradigm that provisions various computing resources to meet the developing computational needs. Scheduling the task poses many difficulties, because the cloud computing resources are complex, dynamic, heterogeneous, distributed in nature. Task Scheduling aims at minimising the makespan and maximising the resource utilisation. This paper gives an elaborate idea about...
With the increasing growth of applications deployed to the cloud computing platform, cloud providers have made a higher request for cloud resource utilization efficiency, but there is bottlenecks in cloud service provisioning base on VM. Against these problems, we proposed a VM-Container hybrid hierarchical resource scheduling mechanism which classified tasks into different levels according to the...
Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers’ requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization...
Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm...
Metaheuristic algorithm is efficient for solving NP-complete problems such as scheduling. An algorithm called the exploration control particle swarm optimization (ECPSO) is proposed to solve grid task scheduling, a new velocity update rule on the basis of exploration capability is suggested, an exploration control probability (ECP) parameter is designed to control the search behaviour of particles...
Modern heterogeneous multiprocessor embedded platforms is important for the high volume markets that have strict performance. However, it presents many challenges that need to be addressed in order to be efficiently utilized for multitask applications. Since mapping and scheduling problems for multi processors belong to the classic of NP-Complete problems, common methods used to solve this kind of...
In this paper we investigate the application of Meta-Heuristic for cloud task scheduling on Hadoop. Hadoop is an open source implementation of MapReduce framework which extensively used for processing computational intensive jobs on huge amount of data over multi-node cluster. In order to achieve an efficient execution schedule, the scheduling algorithm requires to determining the order and the node...
How to optimize and schedule hundreds of traffic lights has become a challenging and pressing problem. The key point lies on how to manage them dynamically and timely. This paper proposes an inner and outer cellular automaton mechanism combined with particle swa445rm optimization (IOCA-PSO) method to achieve a dynamic and real-time optimization scheduling of urban traffic lights. The proposed IOCA-PSO...
This paper presents a new approach based on the combination of the Particle Swarm Optimization and the gradient method to solve the unit commitment (UC) problem. The proposed strategy optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption during a well specific duration. Each production...
Cloud computing offers unprecedented capacity to execute large-scale workflows in the “era of big data”. In 2014, a cost-minimization and deadline-constrained workflow scheduling (CMDCWS) model is firstly proposed by Rodriguez and Buyya, which is applicable for the business need of cloud computing that a workflow task should be finished by minimizing the execute cost within a deadline constraint....
One of the most difficult combinatorial optimization problems in recent studies is job shop scheduling. Job shop scheduling which also holds the key to the company's profitability is a crucial problem faced by many manufacturing companies. Well-structured scheduling has the potential to reduce operating costs and increase profits. Artificial Fish Swarm Algorithm (AFSA) is one of optimization algorithms...
Solving the Unit Commitment problem (UCP) optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed...
In order to make the embedded cloud computing resources to achieve efficient and real-time task scheduling, this paper puts forward a method of resource scheduling that task completion time and resource load balancing degree as the objective function and using multi-objective particle swarm optimization algorithm to optimize the task scheduling. Simulation results verify the effectiveness of the algorithm,...
A Taguchi-based particle swarm optimization (TBPSO) algorithm is proposed for solving multi-objective flowshop scheduling problems (FSPs). The proposed TBPSO integrates particle swarm optimization and Taguchi-based crossover. The proposed TBPSO is the use of a PSO to explore the optimal feasible region and the use of the Taguchi-based crossover to exploit the better solution. As a result, the TBPSO...
In the airline industry, the Aircraft Maintenance Routing (AMR) problem has been one of the great successes of operations research. The AMR problem is to determine a particular route for each aircraft to undergo different levels of maintenance checks. The objective is to minimize the total maintenance costs. In this study, our aim is to present a mathematical formulation for the AMR problem which...
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