The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Load Balancing (LB) of tasks in cloud is an NP hard problem. NP hard class of problems poses the challenge of proving that a solution's value is near optimum without knowing what the optimum value is. Hence applying heuristic techniques to solve the load balancing problem becomes imperative. Reaching a satisfactory solution to the task load balancing problem is a major research area in cloud environment...
Healthcare staff routing to provide healthcare service to the patients is one of the real-world scheduling problems similar to multiple travelling salesman problems (MTSP). Healthcare staff members provide daily medical services at patients' homes. The service provider authority has to schedule these staff in an effective and efficient way so that it achieves the minimum total cost. The aim of this...
The flexible job shop problem (FJSP) is a generalization of the classical job shop scheduling problem. The meta-heuristic particles swarm optimization (PSO) is well suited to solve the FJSP but it might be time consuming specially on monocore platforms. In this paper, we propose some PSO-FJSP variants that aim to improve the performance in term of CPU time.
The Square Kilometer Array (SKA) under construction aims to be the world's largest telescope. Its Science Data Processing (SDP) is responsible for processing the observed data into science-ready data products. The exa-scale data throughtput calls for scheduling to assign computation tasks to networked computing island to relieve computation pressure. Popular scheduling methods in the classic high...
Many computing systems today are heterogeneous in that they consist of a mix of different types of processing units (e.g., CPUs, GPUs). Each of these processing units has different performance capabilities and energy consumption characteristics. Job mapping and scheduling play a crucial role in such systems as they strongly affect the overall system performance, energy consumption, peak power and...
Constructing a timetable is a widespread problem. Computers can be employed to solve this problem faster and to produce better solutions. Software solutions for this problem already exist and are used by some universities. However, some universities have complex types of constraints that make it hard to use most of the available software solutions. This paper introduces a software solution for the...
As scientific applications become more data intensive, finding an efficient scheduling strategy for massive computing on network-based computing systems has drawn increasingly attention. Most existing scheduling models assume that all processors are idle at the beginning of workload assignment. In fact, in the real distributed computing environments, processors may still be occupied with any previous...
Considering the effects of machine breakdown and preventative maintenance (PM)on production scheduling in flowshop manufacturing cells, this paper focuses on investigating the joint optimization problem of flowshop sequence-dependent manufacturing cell scheduling and PM. A joint model is proposed and it aims to find the optimal production sequence of job families and individual jobs within each family...
Integrated preventive maintenance (PM) and production scheduling problem has become one of research hotspots, but few research discusses the topic under uniform parallel machine system with deterioration effect. In this article, both characteristics of uniform parallel machines, deterioration effect of machine and job are considered, flexible PM strategy is adopted and integrated bi-objective optimization...
This paper mainly focuses on the method of timetable multi-objective optimization considering the randomness of passengers flow. The passenger flow model is established based on the normal distribution of the passenger flow, which is verified through the testing hypotheses for the number of passengers getting on and off the train. In assessment of energy consumption, the train operation between the...
Task scheduling is an important problem of radar resource management. Because of the limited resources constraints, an effective scheduling algorithm is necessary to allocate the resources of radar system. Traditional genetic algorithm (GA) can improve the time utilization rate (TUR) and scheduling success rate (SSR), but the time shifting rate (TSR) will increase at the same time, which goes against...
Satellite observation scheduling is a complex combinational optimization problem. Current researches usually adopt intelligent optimization methods to solve it, ignoring the similar historical scheduling cases. In order to improve algorithm performance, case-based learning method is introduced to the scheduling process. Considering the characteristic of the problem, a method of retrieving, matching...
An alteration of the job shop scheduling problem, concerning advertisement scheduling on digital advertisement spaces, is presented. Dispatching Rules (DR), Iterated Local Search (ILS) and Genetic Algorithms (GA) are discussed and applied to the problem space. The results show that ILS is the best performing heuristic, and surpasses the other heuristics especially in large problem spaces (≥ 100 machines,...
This article concerns the resolution of a hybrid flow shop with dedicated machines, sequence dependent setup and time lags. Such a configuration is encountered in the pasta production industry. The objective is to minimize the maximum completion time of all the jobs (makespan). We first propose a mathematical model for the problem. Given the complexity of the problem, we also developed an upper bound...
Energy consumers oftentimes suffer some element of discomfort associated with the implementation of demand response programs as they aim to follow a suggested energy consumption profile generated from scheduling algorithms for the purpose of optimizing grid performance. This is because people naturally do not like to be told what to do or when to use their appliances. Although advances in renewable...
Workflow scheduling deals with the mapping of interdependent and compute intensives tasks to the system resources considering all application's requirements. Due to its elastic capabilities, the cloud has been instrumental in effective scheduling of workflow activities. This paper presents a genetic algorithm based metaheuristics to schedule workflow applications on cloud resources with an objective...
This paper presents a GISMOO algorithm adaptation to solve a multi-objective permutation flowshop with sequence-dependent setup times. The makespan and the total tardiness are the two objectives studied. Numerical experiments on various benchmarks from the literature were performed, to compare the performance of the adapted GISMOO algorithm with the NSGA-II algorithm. The results indicate that our...
The Gravitational Search Algorithm (GSA) is a nature inspired optimization algorithm which is based on Newton's law of gravity and law of motion. Biogeography Based Optimization (BBO) is also another nature inspired optimization algorithm based on the concept of biogeography (migration and mutation among population). Both of these optimization technique are population based and individually have been...
Codelet model is a fine-grained, event-driven hybrid parallel model inspired by dataflow, whose performance depends on the scheduling policy. How to design optimal codelet scheduling policy based on the features of tasks is important to the codelet-based system performance. In this paper, we propose an adaptive codelet scheduling policy by combing "pure" genetic algorithm for tasks with...
Task scheduling is ones of the most important issues in cloud computing environment, which directly affects the overall performance of the cloud platform. QoS-aware Task scheduling in cloud computing is NP-hard problem. There is no efficient method to solve it, and most of current task scheduling algorithms bias total task completion time than single task completion time. This paper proposes a template-based...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.