Relevant bibliographies by topics / Scheduling algorithm

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Scheduling algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Scheduling algorithm"

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Mishra, Abhishek, and Pramod Kumar Mishra. "A Randomized Scheduling Algorithm for Multiprocessor Environments Using Local Search." Parallel Processing Letters 26, no. 01 (2016): 1650002. http://dx.doi.org/10.1142/s012962641650002x.

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The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms (A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC (Dominant Sequence Clustering as algorithm A), and CPPS (Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task sc
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Zouaoui, Sonia, Lotfi Boussaid, and Abdellatif Mtibaa. "Priority based round robin (PBRR) CPU scheduling algorithm." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 190–202. https://doi.org/10.11591/ijece.v9i1.pp190-202.

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This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algorithm canno
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Thirumala Rao, B., M. Susmitha, T. Swathi, and G. Akhil. "Implementation Of Hybrid Scheduler In Hadoop." International Journal of Engineering & Technology 7, no. 2.7 (2018): 868. http://dx.doi.org/10.14419/ijet.v7i2.7.11084.

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The paper focusses on priority based round robin scheduling algorithm for scheduling jobs in Hadoop environment. By Using this Proposed Scheduling Algorithm it reduces the starvation of jobs. And the advantage of priority scheduling is that the process with the highest priority will be executed first. Combining the both strategies of round robin and priority scheduling algorithm a optimized algorithm is to be implemented. Which works more efficiently even after considering all the parameters of scheduling algorithm. This proposed algorithm is also compared with existing round robin and priorit
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Zouaoui, Sonia, Lotfi Boussaid, and Abdellatif Mtibaa. "Priority based round robin (PBRR) CPU scheduling algorithm." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 190. http://dx.doi.org/10.11591/ijece.v9i1.pp190-202.

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<p>This paper introduce a new approach for scheduling algorithms which aim to improve real time operating system CPU performance. This new approach of CPU Scheduling algorithm is based on the combination of round-robin (RR) and Priority based (PB) scheduling algorithms. This solution maintains the advantage of simple round robin scheduling algorithm, which is reducing starvation and integrates the advantage of priority scheduling. The proposed algorithm implements the concept of time quantum and assigning as well priority index to the processes. Existing round robin CPU scheduling algori
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Huang, Xiaohui, Shuxia Zheng, Shilong Li, Jinxiang Wu, and Graham Spence. "Scheduling Problem of Biochemical Analyzer and Exploration of Neural Network-Greedy Algorithm." Journal of Medical Imaging and Health Informatics 10, no. 8 (2020): 1912–18. http://dx.doi.org/10.1166/jmihi.2020.3120.

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The mathematical model of biochemical analysis system was established based on neural network-greedy algorithm. The optimal task scheduling sequence was solved by neural network algorithm. At the same time, the local optimization was obtained by combining greedy algorithm. In this way, the task scheduling problem in biochemical analyzer was transformed into a mathematical problem, and the mathematical model of scheduling algorithm was established. On the platform of MATLAB, eight groups of simulation tests were carried out on the same task scheduling problem using the neural network-greedy sch
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Sreenath, M., and Dr P. A. Vijaya. "Boosted System Performance based on Execution Time Using LiMca Scheduling Algorithm." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1318–29. http://dx.doi.org/10.51201/jusst/21/06449.

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The scheduling algorithms have been examined by the process of task execution in a system to achieve maximum utilization of multiprocessors. Consequently, the research attempted to propose a new real-time scheduling algorithm to support a multiprocessor platform. The proposed scheduling algorithm, List Mcnaughton’s amalgamation (LiMca) scheduling algorithm has been developed for an optimum solution with the features of List and Mcnaughton’s scheduling algorithms to overcome the individual drawbacks (pre-emption and Precedence constraints). In LiMca, Workload has been distributed to the process
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Liu, Yu Jie. "Modeling and Analysis for Optimization Mode of City Bus Operator Scheduling Management." Applied Mechanics and Materials 513-517 (February 2014): 3220–23. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3220.

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This paper proposes a bus route scheduling management model based on Ant Colony Optimization and traditional scheduling model. It optimizes the existing bus route scheduling management model according to Ant Colony algorithm, enhances the performance of both Ant Colony algorithm and the traditional scheduling model, and improves the optimal performance of the combining algorithm. The experiment results show that, the proposed algorithm can effectively deal with the bus route scheduling management, and the optimization result obtained is obviously better than the traditional algorithms. Further
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Wang, Guan, Yuxin Wang, Hui Liu, and He Guo. "HSIP: A Novel Task Scheduling Algorithm for Heterogeneous Computing." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3676149.

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High-performance heterogeneous computing systems are achieved by the use of efficient application scheduling algorithms. However, most of the current algorithms have low efficiency in scheduling. Aiming at solving this problem, we propose a novel task scheduling algorithm for heterogeneous computing named HSIP (heterogeneous scheduling algorithm with improved task priority) whose functionality relies on three pillars: (1) an improved task priority strategy based on standard deviation with improved magnitude as computation weight and communication cost weight to make scheduling priority more re
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Ni, Qiufen, Chuanhe Huang, Panos M. Pardalos, Jia Ye, and Bin Fu. "Different Approximation Algorithms for Channel Scheduling in Wireless Networks." Mobile Information Systems 2020 (November 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/8836517.

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We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters f , g . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side O 1 -IS-approximation is obtained; 2. a greedy O 1 , O 1 -approximation al
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Xue, Sheng Jun, and Fang Fang Liu. "Application of an Improved Immune Evolutionary Algorithm to Grid Task Scheduling." Advanced Materials Research 219-220 (March 2011): 1383–88. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1383.

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Grid task scheduling is an NP problem , performance of scheduling algorithms greatly influences scheduling results. Aiming at the shortages of the existing Evolutionary Algorithm, such as premature convergence, search process easily falling into local optimum, poor scheduling results and so on, this paper proposed an improved immune Evolutionary Algorithm which introduced concentration mechanism in the immune system into Immune Evolutionary Algorithm and adjusted regulator to adaptive function. Simulation experiment shows that, convergence speed and performance of the improved algorithm are si
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Dissertations / Theses on the topic "Scheduling algorithm"

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Chohan, Ossam. "University Scheduling using Genetic Algorithm." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3791.

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The automated timetabling and scheduling is one of the hardest problem areas. This isbecause of constraints and satisfying those constraints to get the feasible and optimizedschedule, and it is already proved as an NP Complete (1) [1]. The basic idea behind this studyis to investigate the performance of Genetic Algorithm on general scheduling problem underpredefined constraints and check the validity of results, and then having comparative analysiswith other available approaches like Tabu search, simulated annealing, direct and indirectheuristics [2] and expert system. It is observed that Gene
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Desai, Neha K. "Scheduling algorithm for flexible manufacturing cells." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23274.pdf.

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Radi, Kamal Mohammed A. R. "Scheduling algorithm including load curtailment costs." Thesis, Queen's University Belfast, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335465.

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Phrueksanant, Janyarat. "Machine scheduling using the Bees Algorithm." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/58594/.

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Single-machine scheduling is the process of assigning a group of jobs to a machine. The jobs are arranged so that a performance measure, such as the total processing time or the due date, may be optimised. Various swarm intelligence techniques as well as other heuristic approaches have been developed for machine scheduling. Previously, the Bees Algorithm, a heuristic optimisation procedure that mimics honeybee foraging, was successfully employed to solve many problems in continuous domains. In this thesis, the Bees Algorithm is presented to solve various single-machine scheduling benchmarks, a
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Goemans, Michel X., Maurice Queyranne, Andreas S. Schulz, Martin Skutella, and Yaoguang Wang. "Single Machine Scheduling with Release Dates." Massachusetts Institute of Technology, Operations Research Center, 1999. http://hdl.handle.net/1721.1/5211.

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We consider the scheduling problem of minimizing the average weighted completion time of n jobs with release dates on a single machine. We first study two linear programming relaxations of the problem, one based on a time-indexed formulation, the other on a completiontime formulation. We show their equivalence by proving that a O(n log n) greedy algorithm leads to optimal solutions to both relaxations. The proof relies on the notion of mean busy times of jobs, a concept which enhances our understanding of these LP relaxations. Based on the greedy solution, we describe two simple randomized app
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Rahman, Mostafizur. "Branch and Bound Algorithm for Multiprocessor Scheduling." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3790.

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The multiprocessor task graph scheduling problem has been extensively studied asacademic optimization problem which occurs in optimizing the execution time of parallelalgorithm with parallel computer. The problem is already being known as one of the NPhardproblems. There are many good approaches made with many optimizing algorithmto find out the optimum solution for this problem with less computational time. One ofthem is branch and bound algorithm.In this paper, we propose a branch and bound algorithm for the multiprocessor schedulingproblem. We investigate the algorithm by comparing two diff
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Edlund, Joakim, and Fredrik Berntsson. "Constructing a Scheduling Algorithm For Multidirectional Elevators." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166594.

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With this thesis we aim to create an efficient scheduling algorithm for elevators that can move in multiple directions and establish if and when the algorithm is efficient in comparison to algorithms constructed for traditional elevator algorithms. To measure efficiency, a simulator is constructed to simulate an elevator system implementing different algorithms. Because of the challenge of constructing a simulator and since we did not find either a simulator nor any algorithms for use in multidirectional elevator systems publicly, we decided to focus on these subjects. The results in this thes
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Doss, Christopher. "Algorithm Partitioning and Scheduling for Adaptive Computers." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010619-175307.

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<p>Adaptive, or reconfigurable, computing has emerged as a viable computing alternative for computationally intense applications. (We use the terms adaptive and reconfigurable interchangeably). Here, an adaptive computer is a computing system that contains a general purpose processor attached to a programmable logic device such as a field programmable gate array (FPGA). These computing systems combine the flexibility of general purpose processors with the speed of application specific processors. The computer system designer can cater the hardware to a specific application by modifying the con
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Wall, Matthew Bartschi. "A genetic algorithm for resource-constrained scheduling." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10259.

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Alapati, Venkata Somi Reddy. "A New Scheduling Algorithm for Multimedia Communication." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277951/.

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The primary purpose of this work is to propose a new scheduling approach of multimedia data streams in real-time communication and also to study and analyze the various existing scheduling approaches.
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Books on the topic "Scheduling algorithm"

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Morton, David P. An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling. Naval Postgraduate School, 1994.

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Koren, Gilad. An optimal on-line scheduling algorithm for overloaded real-time systems. Courant Institute of Mathematical Sciences, New York University, 1992.

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Koren, Gilad. An optimal on-line scheduling algorithm for overloaded real-time systems. Courant Institute of Mathematical Sciences, New York University, 1992.

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Knjazew, Dimitri. OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems. Springer US, 2002.

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Koren, Gilad. An optimal scheduling algorithm with a competitive factor for real-time systems. Courant Institute of Mathematical Sciences, New York University, 1991.

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Koren, Gilad. An optimal scheduling algorithm with a competitive factor for real-time systems. Courant Institute of Mathematical Sciences, New York University, 1991.

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Cervantes, Julian Jaime. An optimal static scheduling algorithm for hard real-time systems specified in prototyping language. Naval Postgraduate School, 1989.

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Zhai, Antonia Bingheng. A compiler algorithm for scheduling non-numeric code with explicitly forwarded data on a speculative multiprocessor. National Library of Canada, 1998.

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Brucker, Peter. Scheduling Algorithms. Springer Berlin Heidelberg, 2001.

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Brucker, Peter. Scheduling Algorithms. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03088-2.

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Book chapters on the topic "Scheduling algorithm"

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Montana, David, Talib Hussain, and Gordon Vidaver. "A Genetic-Algorithm-Based Reconfigurable Scheduler." In Evolutionary Scheduling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_21.

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Kim, Dongmin, and Salim Hariri. "Task-Scheduling Algorithm." In Virtual Computing. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1553-1_7.

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Kumar, Ravin. "Cyclic Scheduling Algorithm." In Emerging Research in Computing, Information, Communication and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5953-8_38.

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Chew, Ek Peng, Loo Hay Lee, and Kanshukan Rajaratnam. "Evolutionary Algorithm for an Inventory Location Problem." In Evolutionary Scheduling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_22.

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Jin, Xi, Changqing Xia, Chi Xu, and Dong Li. "Mixed-Criticality Scheduling for TDMA Networks." In Mixed-Criticality Industrial Wireless Networks. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8922-3_4.

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AbstractTo improve the schedulability of mixed-criticality industrial wireless networks, targeted algorithms should be developed. Therefore, in this chapter, we propose a mixed-criticality scheduling algorithm. The algorithm supports centralized optimization and adaptive adjustment. It can improve both the schedulability and flexibility. We conduct extensive simulations, and the results demonstrate that the proposed scheduling algorithm significantly outperforms existing ones.
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Goh, Chi Keong, Wei Ling Lim, Yong Han Chew, and Kay Chen Tan. "A Multi-Objective Evolutionary Algorithm for Channel Routing Problems." In Evolutionary Scheduling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_15.

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Datta, Dilip, Kalyanmoy Deb, and Carlos M. Fonseca. "Multi-Objective Evolutionary Algorithm for University Class Timetabling Problem." In Evolutionary Scheduling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_8.

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Kadioglu, Serdar, Yuri Malitsky, Ashish Sabharwal, Horst Samulowitz, and Meinolf Sellmann. "Algorithm Selection and Scheduling." In Principles and Practice of Constraint Programming – CP 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23786-7_35.

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Wanderley Góes, Luís Fabrício, and Carlos Augusto Paiva da Silva Martins. "Reconfigurable Gang Scheduling Algorithm." In Job Scheduling Strategies for Parallel Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11407522_5.

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Watanabe, Isamu, Ikuo Kurihara, and Yoshiki Nakachi. "A Hybrid Evolutionary Algorithm for Service Restoration in Power Distribution Systems." In Evolutionary Scheduling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_11.

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Conference papers on the topic "Scheduling algorithm"

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Pasieka, Engelbert, and Sebastian Engell. "Evolutionary Algorithm Based Real-time Scheduling via Simulation-Optimization for Multiproduct Batch Plants." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.183349.

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Production scheduling in the process industry is an area of significant activity in research and of great practical importance for the performance of industrial companies. In the vast majority of research papers, the scheduling problem is formulated as an off-line problem where a number of jobs is scheduled on a number of resources and the efficiency of the formulation and the solution algorithms is discussed. In reality, however, scheduling is a continuous activity that has to react to the arrival of new orders, to variations in processing times, breakdowns, lack of resources etc. This is ter
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Hekal, Omar, Daniel Onwuchekwa, and Roman Obermaisser. "Incremental Scheduling Using Genetic Algorithm." In 2024 International Symposium ELMAR. IEEE, 2024. http://dx.doi.org/10.1109/elmar62909.2024.10694420.

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Bocci, Andrea, Christopher Jones, and Matti Kortelainen. "Performance of Heterogeneous Algorithm Scheduling in CMSSW." In Performance of Heterogeneous Algorithm Scheduling in CMSSW. US DOE, 2023. http://dx.doi.org/10.2172/1973609.

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Yun, Sang-Hyuk, Hyo-Sung Ahn, Sun-Ju Park, Ok-Chul Jung, and Dae-Won Chung. "Ground Antenna Scheduling Algorithm for Multi-Satellite Tracking." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48042.

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In this paper, we address the optimal ground antenna scheduling problem for multiple satellites when multiple satellites have visibility conflicts at a ground station. Visibility conflict occurs when multiple satellites have either overlapping visibilities at a ground station or difference with time of loss of signal (LOS) of a satellite and time of acquisition of signal (AOS) of another satellite is less than reconfiguration time of ground station. Each satellite has a priority value that is a weight function with various factors. Multi-antenna scheduling (MAS) algorithm 1 and Multi-antenna s
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Bampis, Evripidis, Konstantinos Dogeas, Alexander Kononov, Giorgio Lucarelli, and Fanny Pascual. "Scheduling with Untrusted Predictions." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/636.

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Using machine-learned predictions to create algorithms with better approximation guarantees is a very fresh and active field. In this work, we study classic scheduling problems under the learning augmented setting. More specifically, we consider the problem of scheduling jobs with arbitrary release dates on a single machine and the problem of scheduling jobs with a common release date on multiple machines. Our objective is to minimize the sum of completion times. For both problems, we propose algorithms which use predictions for taking their decisions. Our algorithms are consistent -- i.e. whe
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Toledo Junior, Tiago José, and Sarita Bruschi. "EPCSAC - Extensible Platform for Cloud Scheduling Algorithm Comparison." In XXI Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wscad_estendido.2020.14088.

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When developing a new cloud scheduling algorithm, simulation is the most used approach to test the algorithm, mainly due to the impossibility of controlling all the cloud variables and also because of the costs involved. However, setting up the simulation environment can be a difficult task and each environment can be configured its own way, resulting in no easy way of reproducing the results with other published algorithms neither comparing both under the same circumstances. To solve both of these problems, we propose the EPCSAC, an online open-source platform that allows researchers to worry o
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Najafabadi, Mohammad Moghimi, Mustafa Zali, Shamim Taheri, and Fattaneh Taghiyareh. "Static Task Scheduling Using Genetic Algorithm and Reinforcement Learning." In 2007 IEEE Symposium on Computational Intelligence in Scheduling. IEEE, 2007. http://dx.doi.org/10.1109/scis.2007.367694.

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Sun, Fuquan, Jianjian Cao, and Zhenghao Lu. "HEFT-Dynamic Scheduling Algorithm in Workflow Scheduling." In 2022 34th Chinese Control and Decision Conference (CCDC). IEEE, 2022. http://dx.doi.org/10.1109/ccdc55256.2022.10033968.

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Yu, Han. "A Hybrid GA-based Scheduling Algorithm for Heterogeneous Computing Environments." In 2007 IEEE Symposium on Computational Intelligence in Scheduling. IEEE, 2007. http://dx.doi.org/10.1109/scis.2007.367674.

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Sarfati, Hagit, Eitan Bachmat, and Sagit Kedem-Yemini. "Parameter setting and exploration of TAGS using a genetic algorithm." In 2007 IEEE Symposium on Computational Intelligence in Scheduling. IEEE, 2007. http://dx.doi.org/10.1109/scis.2007.367702.

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Reports on the topic "Scheduling algorithm"

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Jones, Christopher D., and Patrick Gartung. Using OpenMP for HEP Framework Algorithm Scheduling. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1623358.

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Bui, Dai. Revision of a Non-Preemptive EDF Packet Scheduling Algorithm. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada538864.

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Hawthorne, J. E., and R. A. McLaren. Scheduling Algorithm for Improving Lift (SAIL): Documentation for initial operating capability. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6951231.

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Balman, Mehmet, and Tevfik Kosar. An Online Scheduling Algorithm with Advance Reservation for Large-Scale Data Transfers. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1050437.

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Bellabai, Robert, Jeen Robert, and Ramasubbu Rajkumar. Multi-objective Optimization Using Hybrid Algorithm and Its Application to Scheduling in Flow Shops. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.01.14.

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Boman, Erik G. LDRD Report: Scheduling Irregular Algorithms. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1172906.

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Bertsekas, D. P., and D. A. Castanon. Rollout Algorithms for Stochastic Scheduling Problems. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada459559.

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Merzlykin, Pavlo, Natalia Kharadzjan, Dmytro Medvedev, Irina Zakarljuka, and Liliia Fadeeva. Scheduling Algorithms Exploring via Robotics Learning. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2877.

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The new approach to schedule-related problems learning with use of robotics is reported. The materials are based on the authors' teaching experience within framework of Robotics School at Kryvyi Rih State Pedagogical University. The proposed learning problem may be used both for scheduling algorithms exploring and robotics competitions.
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Howe, Adele, and L. D. Whitley. Model Justified Search Algorithms for Scheduling Under Uncertainty. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada492118.

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Kohout, Keith A., Kent Meyer, and John B. Goodenough. Ada Validation Tests for Rate Monotonic Scheduling Algorithms. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada253325.

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