Showing 1-25 of 34,766 resultsfor "Index Terms":scheduling optimization
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Short-term hydro-thermal scheduling using an improved bacterial foraging algorithm
2009 IEEE Electrical Power & Energy Conference (EPEC)
Year: 2009 | Conference Paper |
Cited by: Papers (22)
This paper presents an improved bacterial foraging algorithm (IBFA) to find the optimal short-term hydro-thermal generation scheduling (STHTS). The STHTS problem is a dynamic large-scale nonlinear optimization problem which requires solving the unit commitment and economic power load dispatch problems. The bacterial foraging algorithm (BFA) is a recently developed evolutionary optimization techniq...Show More
This paper presents heuristic algorithms for solving the three-way joint optimization of Performance, Energy and temperature (PET) in scheduling tasks to multi-core processors. The problem, called as PET optimized scheduling (PETOS) problem is a high-complexity problem due to conflicting objectives. While solutions to the PETOS problem can be obtained by using conventional multi-objective optimiza...Show More
Distributed scheduling problems in intelligent manufacturing systems
Tsinghua Science and Technology
Cited by: Papers (122)
Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations. Nowadays, distributed manufacturing systems have been widely adopted in industr...Show More
Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Optimization (ACO) Algorithm
2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)
Year: 2024 | Conference Paper |
The rapid development of cloud computing technology has spawned a plethora of complex applications, leading to an increasing demand for efficient task scheduling algorithms. Traditional task scheduling algorithms have certain limitations in resource allocation and load balancing, making it difficult to fully adapt to the dynamic and evolving cloud computing environment. In order to enhance the eff...Show More
Multi-objective flexible job shop scheduling based on Pareto quantum particle swarm optimization algorithm
Li Guan;Yajie Ma;Bin Jiang;Yuying Huang;Jiangnan Zhou;Haoyang Zhang
2024 36th Chinese Control and Decision Conference (CCDC)
Year: 2024 | Conference Paper |
The single particle swarm algorithm exhibits deficiencies in optimality, diversity, and convergence speed when addressing the multi-objective optimal scheduling problem in flexible job shops. In this research, we introduce a multi-objective Pareto quantum particle swarm algorithm. To aviod the algorithm falling into the problem of local convergence, three initialization strategies are proposed to ...Show More
Multisensor scheduling, a multi-objective optimization problem, simultaneously aims to find the optimal matching between sensors and targets in a certain period, that is, to generate the optimal multisensor scheduling scheme. Several sensor scheduling literatures proposed multisensor scheduling schemes incorporating various algorithms, but the majority of them dealt with sensors to targets in two ...Show More
Research on Virtual Resource Scheduling Algorithm Based on Multi-Objective Optimization
2024 IEEE Cyber Science and Technology Congress (CyberSciTech)
Year: 2024 | Conference Paper |
In order to solve the resource management challenge of cloud service center when the user demand peaks periodically, reduce resource waste and energy consumption, improve resource utilization and system performance, a scheduling algorithm based on multi-objective optimization is proposed in this paper. Firstly, this paper analyzes the main problems of current cloud computing resource scheduling, c...Show More
Adaptive task scheduling based on Multi Criterion Ant Colony Optimization in computational grids
2012 International Conference on Recent Trends in Information Technology
Year: 2012 | Conference Paper |
Cited by: Papers (2)
Grid Computing is a form of distributed computing environment that allows sharing, selection and coordinated use of diverse resources owned by different organizations. Effective and efficient scheduling of resources in grid is fundamentally important. To optimize the scheduling of tasks to suitable resources in computational grids, a Multi Criterion Ant Colony Optimization (MCACO) Algorithm is pro...Show More
Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks
Tsinghua Science and Technology
Cited by: Papers (83)
This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) by considering two objectives simultaneously, i.e., makespan and total energy consumption. It consists of three sub-problems, i.e., job assignment between factories, job sequence in each factory, and machine allocation for each job. We present a mixed inter linear programming...Show More
Optimal Utility-Based Multi-User Scheduling and Low-Complexity Alternatives
SCC 2013; 9th International ITG Conference on Systems, Communication and Coding
Year: 2013 | Conference Paper |
In this work the problem of utility-based multiuser scheduling is considered in a fading environment. The goal is to make use of the multi-user diversity while still guaranteeing short-term fairness. The performance of a scheduler is captured by a utility function. Based on the utility function and assuming statistical knowledge of the time-varying channel the optimization problem for the optimal ...Show More
Cloud Computing Task Scheduling Policy Based on Improved Particle Swarm Optimization
2018 International Conference on Virtual Reality and Intelligent Systems (ICVRIS)
Year: 2018 | Conference Paper |
Cited by: Papers (22)
The task scheduling policy is the important factors for achieving efficient calculation in a cloud computing environment. This article put forwards a task scheduling method based on improved particle swarm algorithm against the present inefficiency. Particle Swarm Optimization (PSO) algorithm is used to solve task scheduling optimization by introducing the iterative selection operator. Improved pa...Show More
The research on job shop on-line scheduling with random release-date
2009 IEEE International Conference on Industrial Engineering and Engineering Management
Year: 2009 | Conference Paper |
For the job shop on-line scheduling in which the release dates of jobs are stochastic distributed, the hybrid scheduling strategy that integrates the interval rolling mechanism with the key event-driven is introduced. The satisfactory solution can be obtained through iterative optimization by embedding the neighborhood search into the constraint satisfaction solving procedure. The simulation and c...Show More
Simulation Modeling and Optimization for Equipment Scheduling in Container Terminals
2006 5th IEEE International Conference on Cognitive Informatics
Year: 2006 | Conference Paper |
Faced with increasing growth of container throughput and more large ships, it is necessary to improve efficiency of container terminals. This paper applies simulation and optimization technology to mimic and optimize equipment scheduling tasks in container terminals. Firstly, we propose a two-layer embedded framework, which can avoid the separation of simulation and optimization. In addition, we a...Show More
Elitist Genetic Algorithm and Elitist Ant Colony Optimization on Resource Scheduling in Field Cloud Manufacturing
2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
There have been several studies on the scheduling mechanism in cloud manufacture in on-factory manufacturing situations. However, scheduling mechanism in cloud manufacture in an off-factory situation (field cloud manufacturing) has not been widely studied. Even though there are many manufacturing tasks that need to be implemented using field manufacturing scheme. So in this study, a research on sc...Show More
Particle Swarm optimization with velocity restriction and evolutionary parameters selection for scheduling problem
2015 International Siberian Conference on Control and Communications (SIBCON)
Year: 2015 | Conference Paper |
Cited by: Papers (7)
The article presents a study of the Particle Swarm optimization method for scheduling problem. To improve the method's performance a restriction of particles' velocity and an evolutionary meta-optimization were realized. The approach proposed uses the Genetic algorithms for selection of the parameters of Particle Swarm optimization. Experiments were carried out on test tasks of the job-shop schedu...Show More
Research on Resource Scheduling Algorithms for Satellite Earth Stations
2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
Year: 2021 | Conference Paper |
Resource scheduling has always been a hot and difficult problem in systems engineering and management at home and abroad, and it is also very important in the research of satellite earth station system. Due to the limited resources in the satellite earth station system, the rationality of resource allocation directly affects the overall performance of the multi-satellite system. The resource sched...Show More
Application in Grid Workflow Scheduling Based on DMOPSO Algorithm
2009 Third International Conference on Genetic and Evolutionary Computing
Year: 2009 | Conference Paper |
A discrete multi-objective particle swarm optimization (DMOPSO) algorithm is proposed in this paper. The algorithm adopts two different discretized strategies: directly rounding and redefining based on multi-objective particle swarm optimization with crowding distance (MOPSO_CD) and according to the characteristic of discrete variable. The crowding distance mechanism together with a mutation opera...Show More
Real-Time Task Scheduling using Bio-Inspired Algorithms: A Comparative Study of ACO, PSO, and GWO
2024 First International Conference on Software, Systems and Information Technology (SSITCON)
Year: 2024 | Conference Paper |
This work investigates the use of bio-inspired algorithms in real-time task scheduling, whereby their capabilities are explored in intelligent computing to enhance performance optimization. Three influential bio-inspired algorithms, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO), are employed in the real-time task scheduling process. Th...Show More
Resource Scheduling Algorithm in Embedded Cloud Computing and Application
2014 IIAI 3rd International Conference on Advanced Applied Informatics
Year: 2014 | Conference Paper |
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 a...Show More
A hybrid particle swarm optimization algorithm for bi-criteria flexible job-shop scheduling problem
Junqing Li;Quanke Pan;Shengxian Xie;Jing Liang;Liping Zheng;Kaizhou Gao
This paper presents a hybrid particle swarm optimization algorithm (HPSO) for solving the bi-criteria flexible job shop scheduling problem. Two minimization objectives- the maximum completion time (makespan) and the total workload of all machines are considered simultaneously. In this study, a novel discrete particle swarm optimization (PSO) algorithm was proposed, which incorporates well-designed...Show More
A job shop scheduling system based on Parallel Control Mechanism
The 26th Chinese Control and Decision Conference (2014 CCDC)
Year: 2014 | Conference Paper |
Cited by: Papers (1)
Job shop scheduling has been an active area of research for several decades. However, there seems to have been a significant gap between the theoretical research in academia and practical application in industry. The scheduling system, which are developed aiming at the vast library of benchmark problems, usually faces some challenges when applying in the real manufacturing environment to support t...Show More
An Improved Multi-Objective Particle Swarm Optimization Algorithm for Solving Multi-mode Resource-constrained Multi-project Scheduling Problem
2023 4th International Conference on Information Science, Parallel and Distributed Systems (ISPDS)
Year: 2023 | Conference Paper |
To address the challenging Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP) that involves scheduling multiple instances of projects with various execution modes, subject to resource constraints and precedence relations, this paper proposes the Delay Parallel Decoupled Schedule Generation Scheme (DPDSGS) and the Dynamic-Cluster Dynamic-Weight Multi-Objective Particle Swarm...Show More
Reactive scheduling of multiple EOSs under cloud uncertainties: Model and algorithms
Most earth observation satellites (EOSs) are low-orbit satellites equipped with optical sensors that cannot see through clouds. Hence, cloud coverage, high dynamics, and cloud uncertainties are important issues in the scheduling of EOSs. The proactive-reactive scheduling framework has been proven to be effective and efficient for the uncertain scheduling problem and has been extensively employed. ...Show More
Ant colony optimization for solving combinatorial fuzzy Job Shop Scheduling Problems
2010 International Conference on Communication and Computational Intelligence (INCOCCI)
Year: 2010 | Conference Paper |
In this paper, we present an ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing t...Show More
Brain Storm Optimization Algorithm based on adaptive inertial selection strategy for the RCPSP
2019 Chinese Automation Congress (CAC)
Year: 2019 | Conference Paper |
RCPSP(Resource-constrained Project Scheduling Problem) that has been proved to be a NP-hard problem is a kind of combination optimization problem. With the problem scale becoming larger and larger, the problem will become more difficult to solve. A discussion mechanism based on brain storm optimization (DMBSO) for the RCPSP was presented. According to the characteristics of the RCPSP and the probl...Show More
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