Relevant bibliographies by topics / Scheduling simulation

Contents

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

Academic literature on the topic 'Scheduling simulation'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Scheduling simulation"

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Simeonov, S., and J. Simeonovová. "Simulation scheduling in food industry application." Czech Journal of Food Sciences 20, No. 1 (November 18, 2011): 31–37. http://dx.doi.org/10.17221/3506-cjfs.

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Nowadays manufacturers are facing rapid and fundamental changes in the ways business is done. Producers are looking for simulation systems increasing throughput and profit, reducing cycle time, improving due-date performance, reducing WIP, providing plant-wide synchronization, etc. Planning and scheduling of coffee production is important for the manufacturer to synchronize production capacity and material inputs to meet the delivery date promised to the customer. A simulation model of coffee production was compiled. It includes roasting, grinding and packaging processes. Using this model the basic features of the coffee production system are obtained. An optimization module of the simulation SW is used for improving the current structure of the production system. Gantt charts and reports are applied for scheduling. Capacity planning problems related to coffee production are discussed.  
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MOHAMAD, Effendi bin, and Teruaki ITO. "S141011 Simulation-based CAI System for Kanban Scheduling." Proceedings of Mechanical Engineering Congress, Japan 2011 (2011): _S141011–1—_S141011–4. http://dx.doi.org/10.1299/jsmemecj.2011._s141011-1.

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Ettema, Dick, Aloys Borgers, and Harry Timmermans. "SMASH (Simulation Model of Activity Scheduling Heuristics): Some Simulations." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (January 1996): 88–94. http://dx.doi.org/10.1177/0361198196155100112.

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Travel decision making is increasingly regarded as a highly complex process in which individuals not only decide about frequency of trips, travel modes, and routes, but also about activity participation and sequencing and timing and duration of activities and trips. This raises the question of whether or not traditional discrete-choice models still provide the best starting point for realistically modeling such a process. Some scholars consider computational process models (CPMs) a promising approach because they allow for heuristic search and suboptimal reasoning processes, which are typical for complex decision making. A model of activity scheduling, SMASH (Simulation Model of Activity Scheduling Heuristics), which incorporates aspects of discrete-choice modeling and CPMs, has been proposed. The model describes the pretrip planning phase, in which individuals decide which activities to perform, at what locations, at what times, in which sequence, and how to travel to the various activity sites. The calibration of this model, using data collected with the interactive computerized procedure MAGIC, has been described in the literature. The results indicated that when scheduling their activities, subjects seem to trade off attributes of activities (time constraints, duration), attributes of the schedule (time spent on activities, overall travel time, realism) and characteristics of the scheduling process (amount of effort already involved in the scheduling process) to obtain feasible schedules. More extensive tests, using simulation experiments, of the model's internal, predictive, and face validity are described. SMASH was used to predict subjects' activity schedules based on their activity agenda and information about their spatio-temporal circumstances. The predicted schedules were then compared with the activity schedules conceived by the subjects themselves under different circumstances, to assess the model's validity. The tests indicated that the model provided satisfactory results with respect to the reproduction of the observed activity schedules. The results of the validity test warrant the use of the model for assessing the effects of various policy measures such as time policies, land use policies, and travel demand management.
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McGinnis, Michael L., and Robert G. Phelan. "Scheduling Simulation-Based Training." Military Operations Research 4, no. 1 (December 1, 1999): 35–49. http://dx.doi.org/10.5711/morj.4.1.35.

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Tian, Xiang Ke, and Jian Wang. "The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm." Applied Mechanics and Materials 217-219 (November 2012): 1444–48. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.1444.

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The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.
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Horng, Horng-Chyi, and Kuo-Kai Chien. "A Simulation Study on the Flow Effectiveness of DBR Scheduling." International Journal of Materials, Mechanics and Manufacturing 6, no. 2 (April 2018): 109–13. http://dx.doi.org/10.18178/ijmmm.2018.6.2.357.

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Lim, Tae-Kyung, Chang-Yong Yi, Dong-Eun Lee, and David Arditi. "Concurrent Construction Scheduling Simulation Algorithm." Computer-Aided Civil and Infrastructure Engineering 29, no. 6 (March 5, 2014): 449–63. http://dx.doi.org/10.1111/mice.12073.

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Hui, Ji Zhuang, Xiang Ding, and Kai Gao. "A FMS Dynamic Scheduling Optimization Strategy and Simulation Research." Applied Mechanics and Materials 389 (August 2013): 692–97. http://dx.doi.org/10.4028/www.scientific.net/amm.389.692.

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This paper studied the FMS dynamic scheduling problem which was based on Petri net FMS static scheduling optimization algorithm, which in accorder to solve the FMS actual production scheduling problems. A rolling window dynamic re-scheduling strategy was proposed which based on event driven and cycle driven. Then take the emergency machine failure often appearing in the actual workshop for example, this scheduling strategy was analyzed and applied to dynamic simulation and finally the effectiveness of the dynamic scheduling strategy was verified.
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An, Qian. "Simulation Analysis of Optimized Construction Project Scheduling Model." Applied Mechanics and Materials 644-650 (September 2014): 5652–54. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.5652.

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This study focus on the application of traditional scheduling model’s algorithm, algorithm’s mechanisms and solving effect of project scheduling problem solution. An improved project resource scheduling model based on Particle Swarm Optimization (PSO) is presented in this paper. Experimental results show that the algorithm has better performance in terms of scheduling success rate and convergence rate, better solution quality and has more advantages in the convergence rate.
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SAKAGUCHI, Tatsuhiko, Hidetoshi KAMIMURA, and Keiichi SHIRASE. "Computational Simulation for Decision of Scheduling Period in Reactive Scheduling." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2007.4 (2007): 8F618. http://dx.doi.org/10.1299/jsmelem.2007.4.8f618.

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Dissertations / Theses on the topic "Scheduling simulation"

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Ghodsi, Reza. "Project scheduling using simulation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0001/MQ35014.pdf.

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Ying, Chen-Tsau Chris. "Backward simulation for production scheduling purposes /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487861396025914.

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Frantze´n, Marcus. "A real-time simulation-based optimisation environment for industrial scheduling." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/9032.

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In order to cope with the challenges in industry today, such as changes in product diversity and production volume, manufacturing companies are forced to react more flexibly and swiftly. Furthermore, in order for them to survive in an ever-changing market, they also need to be highly competitive by achieving near optimal efficiency in their operations. Production scheduling is vital to the success of manufacturing systems in industry today, because the near optimal allocation of resources is essential in remaining highly competitive. The overall aim of this study is the advancement of research in manufacturing scheduling through the exploration of more effective approaches to address complex, real-world manufacturing flow shop problems. The methodology used in the thesis is in essence a combination of systems engineering, algorithmic design and empirical experiments using real-world scenarios and data. Particularly, it proposes a new, web services-based, industrial scheduling system framework, called OPTIMISE Scheduling System (OSS), for solving real-world complex scheduling problems. OSS, as implemented on top of a generic web services-based simulation-based optimisation (SBO) platform called OPTIMISE, can support near optimal and real-time production scheduling in a distributed and parallel computing environment. Discrete-event simulation (DES) is used to represent and flexibly cope with complex scheduling problems without making unrealistic assumptions which are the major limitations of existing scheduling methods proposed in the literature. At the same time, the research has gone beyond existing studies of simulation-based scheduling applications, because the OSS has been implemented in a real-world industrial environment at an automotive manufacturer, so that qualitative evaluations and quantitative comparisons of scheduling methods and algorithms can be made with the same framework. Furthermore, in order to be able to adapt to and handle many different types of real-world scheduling problems, a new hybrid meta-heuristic scheduling algorithm that combines priority dispatching rules and genetic encoding is proposed. This combination is demonstrated to be able to handle a wider range of problems or a current scheduling problem that may change over time, due to the flexibility requirements in the real-world. The novel hybrid genetic representation has been demonstrated effective through the evaluation in the real-world scheduling problem using real-world data.
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White, David Elliot. "Simulation-based Optimization of Coal Barge Scheduling." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-01082008-080147/.

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In an attempt to improve the process of supplying coal by way of water to Progress Energyâs Crystal River power plant, a simulation-based technique was developed to find the best schedule of coal barges. The technique uses discrete event simulation principles to find the best solution based on two criteria: minimal demurrage cost and maximal coal tons moved. Many factors are taken into account including channel capacity, tide dependencies, weather delays, periods of scheduled down time, and percentage of trips to each coal terminal. The same technique is also used for long range planning in the decisions of capital allocation of equipment, barge contracts, and coal supplier contracts. A Graphical User Interface coupled with Visual Basic .Net (VB .Net) code is used to implement the approach in a user-friendly and maintainable environment.
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Deschamps, Henrick. "Scheduling of a Cyber-Physical Sytem Simulation." Thesis, Toulouse, ISAE, 2019. http://www.theses.fr/2019ESAE0024/document.

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Les travaux menés dans cette thèse de doctorat s’inscrivent dans le cadre d’un effort pluslarge d’automatisation des systèmes de simulation industriels. Dans l’industrie aéronautique,et plus particulièrement au sein d’Airbus, l’application historique de la simulation est laformation des pilotes. Il existe aussi des utilisations plus récentes dans la conception desystèmes, ainsi que dans l’intégration de ces systèmes. Ces dernières utilisations exigent untrès haut degré de représentativité, là où historiquement le plus important était le ressenti dupilote. Les systèmes sont aujourd’hui divisés en plusieurs sous-systèmes qui sont conçus, implémentéset validés indépendamment, afin de maintenir leur contrôle malgré l’augmentationde leurs complexités et la réduction des temps de mise sur le marché. Airbus maîtrise déjà lasimulation de ces sous-systèmes, ainsi que leurs intégrations en simulation. Cettemaîtriseest empirique, les spécialistes de la simulation reprennent l’ordonnancement d’intégrationsprécédentes, et l’adaptent à une nouvelle intégration. C’est un processus qui peut parfois êtrechronophage, et qui peut introduire des erreurs. Les tendances actuelles de l’industrie sont à la flexibilité des moyens de production, àl’intégration d’outils logistiques permettant le suivi, à l’utilisation d’outils de simulation enproduction, et à l’optimisation des ressources. Les produits sont de plus en plus souvent desitérations d’anciens produits améliorés, et les tests et simulations intégrés à leurs cycles de vie.Travailler de manière empirique dans une industrie qui nécessite de la flexibilité estune contrainte, et il est aujourd’hui important de facilement modifier des simulations. Laproblématique est donc de mettre en place des méthodes et outils permettant a priori degénérer des ordonnancements de simulations représentatifs.Afin de répondre à ce problème, nous avons mis en place une méthode permettant de décrireles composants d’une simulation, la manière dont cette simulation pourra être exécutée,ainsi que des fonctions permettant de générer des ordonnancements. Par la suite, nous avonsimplémenté un outil afin d’automatiser la recherche d’ordonnancement, en se basant sur desheuristiques. Enfin nous avons testé et vérifié notre méthode et outils sur des cas d’étudesacadémiques et industriels
The work carried out in this Ph.D. thesis is part of a broader effort to automate industrialsimulation systems. In the aeronautics industry, and more especially within Airbus, thehistorical application of simulation is pilot training. There are also more recent uses in thedesign of systems, as well as in the integration of these systems. These latter applicationsrequire a very high degree of representativeness, where historically the most important factorhas been the pilot’s feeling. Systems are now divided into several subsystems that are designed, implemented andvalidated independently, in order tomaintain their control despite the increase in their complexity,and the reduction in time-to-market. Airbus already has expertise in the simulationof these subsystems, as well as their integration into a simulation. This expertise is empirical;simulation specialists use the previous integrations schedulings and adapt it to a newintegration. This is a process that can sometimes be time-consuming and can introduce errors.The current trends in the industry are towards flexible production methods, integrationof logistics tools for tracking, use of simulation tools in production, as well as resourcesoptimization. Products are increasingly iterations of older, improved products, and tests andsimulations are increasingly integrated into their life cycles. Working empirically in an industry that requires flexibility is a constraint, and nowadays itis essential to facilitate the modification of simulations. The problem is, therefore, to set upmethods and tools allowing a priori to generate representative simulation schedules.In order to solve this problem, we have developed a method to describe the elementsof a simulation, as well as how this simulation can be executed, and functions to generateschedules. Subsequently, we implemented a tool to automate the scheduling search, based onheuristics. Finally, we tested and verified our method and tools in academic and industrialcase studies
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Liu, Kuo-Ching. "Stochastic simulation-based finite capacity scheduling systems /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487946776022111.

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Matondang, Abdul Rahim. "Evaluation of real time scheduling policies using simulation." Thesis, Cranfield University, 1988. http://hdl.handle.net/1826/3704.

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The purpose of this research is to investigate the effects of different scheduling operating policies in real- time scheduling. The scheduling policies include due date assignment method, priority rules, process batch method and operator reassignment policy. A specific case of production scheduling-in a hypothetical assembled product manufacturing system was investigated in this research. The simulated production system encompasses fifteen work centres, each containing one to three identical machines and each machine requires one operator with all operations being perfectly efficient on all machine types. The production -system produces finished products as well as components and sub-assemblies. Orders from outside the system arrive for service generated according to the exponential distribution. The orders coming to the system were classified into "priority" and "standard" orders. Processing times at each work centre are statistically independent and uniformly distributed. A computer simulation technique was chosen as the approach method. A computer simulation written in DBASE III PLUS was used to generate the data for analysis. In order to analyse the behaviour of the simulated production system with respect to the different performance criteria, a number of performance measures were selected. These are, mean tardiness, percent tardy, mean work in progress, mean machine utilisation, and mean operator utilisation. The 2x6x2x2 complete factorial is analysed by the analysis of variance (ANOVA) procedure to statistically determine whether due date assignment method, priority rules, process batch method, and operator reassignment policy or their interaction significantly affect the performance criteria considered. Further analysis to identify where significant differences in performance occur is conducted via Duncan multiple comparison test. Based upon the statistical analysis it was found that the relative impact of due date assignment method, priority rule, process batch method, and operator reassignment policy or their interaction for scheduling policies in real time scheduling to be dependent upon the measure of performance considered. In respect of root mean square of tardiness, the scheduling policies involving the slack time remaining (STR) priority rule are the most important of scheduling policies in minimising the tardiness of customer orders produced by the company. In respect to minimising the work in progress, there is no dominant level of due date assignment method, or priority rule or process batch method or operator reassignment policy. However, the scheduling policies involving the variable process batch (VPB) method produce the best result. The scheduling policies involving the variable process batch (VPB) method are the best performers in maximising the utilisation of machine and operator.
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Cedrini, Luca. "Time Sensitive Networks: analysis, testing, scheduling and simulation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22305/.

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The industrial automation world is an extremely complex framework, where state-of-the-art cutting edge technologies are continuously being replaced in order to achieve the best possible performances. The criterion guiding this change has always been the productivity. This term has, however, a broad meaning and there are many ways to improve the productivity, that go beyond the simplistic products/min ratio. One concept that has been increasingly emerging in the last years is the idea of interoperability: a flexible environment, where products of different and diverse vendors can be easily integrated togheter, would increase the productivity by simplifying the design and installation of any automatic system. Connected to this concept of interoperability is the Industrial Internet of Things (IIoT), which is one of the main sources of the industrial innovation at the moment: the idea of a huge network connecting every computer, sensor or generic device so as to allow seamless data exchange, status updates and information passing. It is in this framework that Time Sensitive Networks are placed: it is a new, work-in-progress set of communication standards whose goal is to provide a common infrastructure where all kinds of important data for an industrial automation environment, namely deterministic and non deterministic data, can flow. This work aims to be an initial step towards the actual implementation of the above-mentioned technology. The focus will therefore be not only on the theoretical aspects, but also on a set of practical tests that have been carried out in order to evaluate the performances, the required hardware and software features, advantages and drawbacks of such an application.
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Park, Seong Jae. "Bus network scheduling with genetic algorithms and simulation." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2496.

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Thesis (M.S.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Dept. of Civil and Environmental Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Wang, R. "Berth scheduling at seaports : meta-heuristics and simulation." Thesis, Liverpool John Moores University, 2018. http://researchonline.ljmu.ac.uk/9652/.

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This research aims to develop realistic solutions to enhance the efficiency of port operations. By conducting a comprehensive literature review on logistic problems at seaports, some important gaps have been identified for the first time. The following contributions are made in order to close some of the existing gaps. Firstly, this thesis identifies important realistic features which have not been well-studied in current academic research of berth planning. This thesis then aims to solve a discrete dynamic Berth allocation problem (BAP) while taking tidal constraints into account. As an important feature when dealing with realistic scheduling, changing tides have not been well-considered in BAPs. To the best of our knowledge, there is no existing work using meta-heuristics to tackle the BAP with multiple tides that can provide feasible solutions for all the test cases. We propose one single-point meta-heuristic and one population-based meta-heuristic. With our algorithms, we meet the following goals: (i) to minimise the cost of all vessels while staying in the port, and (ii) to schedule available berths for the arriving vessels taking into account a multi-tidal planning horizon. Comprehensive experiments are conducted in order to analyse the strengths and weaknesses of the algorithms and compare with both exact and approximate methods. Furthermore, lacking tools for examining existing algorithms for different optimisation problems and simulating real-world scenarios is identified as another gap in this study. This thesis develops a discrete-event simulation framework. The framework is able to generate test cases for different problems and provide visualisations. With this framework, contributions include assessing the performance of different algorithms for optimisation problems and benchmarking optimisation problems.
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Books on the topic "Scheduling simulation"

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Ward, Timothy. Scheduling through simulation. Salford: University of Salford, 1991.

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Elderen, E. Van. Scheduling farm operations: A simulation model. Wageningen, Netherlands: Pudoc, 1987.

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Burdorf, Christopher. Non-preemptive time-warp scheduling algorithms. Santa Monica, CA: Rand Corp., 1990.

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Bagchi, Tapan P. Multiobjective scheduling by genetic algorithms. Boston: Kluwer Academic Publishers, 1999.

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Lemanski, Michael J. Simulation for Smartnet scheduling of asynchronous transfer mode virtual channels. Monterey, Calif: Naval Postgraduate School, 1997.

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Chaturvedi, Alok R. FMS scheduling: A synergistic approach using conceptual aggregation and simulation. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1990.

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Manabe, Ryutarō. Scheduling buses by the network flow model on the personal computer. Tarumi, Kobe, Japan: Institute of Economic Research, Kobe University of Commerce, 1986.

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Wang, Zhongliang. Trace data analysis and job scheduling simulation for large-scale distributed systems. Ottawa: National Library of Canada, 1994.

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Morecroft, John D. W. A behavioral simulation model of forecasting and production scheduling in a datacommunications company. Cambridge, Mass: Sloan School of Management, Massachusetts Institute of Technology, 1986.

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Goodall, William Richard. Scheduling and control in the batch process industry using hybrid knowledge based simulation. [s.l.]: typescript, 1993.

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Book chapters on the topic "Scheduling simulation"

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Doug, Carmichael, Siletti Charles, Koulouris Alexandros, and Petrides Demetri. "Bioprocess Simulation and Scheduling." In Emerging Areas in Bioengineering, 723–60. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2018. http://dx.doi.org/10.1002/9783527803293.ch42.

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Sedighi, Art, and Milton Smith. "Simulation Scenarios." In Fair Scheduling in High Performance Computing Environments, 51–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14568-2_8.

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Tyszer, Jerzy. "Event scheduling." In Object-Oriented Computer Simulation of Discrete-Event Systems, 17–27. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5033-4_2.

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Li, Wenkai, Yong Wu, and Mark Goh. "Validation via Simulation." In Planning and Scheduling for Maritime Container Yards, 97–108. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17025-1_6.

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Sedighi, Art, and Milton Smith. "Simulation and Methodology." In Fair Scheduling in High Performance Computing Environments, 39–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14568-2_6.

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Rodrigo, Gonzalo P., Erik Elmroth, Per-Olov Östberg, and Lavanya Ramakrishnan. "ScSF: A Scheduling Simulation Framework." In Job Scheduling Strategies for Parallel Processing, 152–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77398-8_9.

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Karatza, Helen D. "Simulation of Parallel and Distributed Systems Scheduling." In Applied System Simulation, 61–80. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9218-5_4.

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Dekleva, Janez, Matjaž Gaberc, and Emil Zavadlav. "Scheduling and Simulation of FMS." In Operations Research Proceedings, 539–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74862-2_141.

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Xing, Chi, Xudong Chai, Qi Wang, Yang Chen, and Li Tan. "Simulation Job Scheduling on Clusters with Heterogeneous Scheduling Systems." In Communications in Computer and Information Science, 403–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45037-2_42.

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Riezebos, Jan, and Jannes Slomp. "The Shop Floor Scheduling Game." In Simulation Games and Learning in Production Management, 117–24. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-5041-2870-4_11.

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Conference papers on the topic "Scheduling simulation"

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Farris, Allen R. "Scheduling simulation in ALMA." In SPIE Astronomical Telescopes + Instrumentation. SPIE, 2004. http://dx.doi.org/10.1117/12.549382.

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Weigl, K. Heinz. "Combining simulation and scheduling." In the 27th conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/224401.224760.

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Rattanatamrong, Prapaporn, and Jose A. B. Fortes. "Improved real-time scheduling for periodic tasks on multiprocessors." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999810.

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Zinnen, Andreas, and Thomas Engel. "Deadline constrained scheduling in hybrid clouds with Gaussian processes." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999837.

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Diaz, Cesar O., Mateusz Guzek, Johnatan E. Pecero, Gregoire Danoy, Pascal Bouvry, and Samee U. Khan. "Energy-aware fast scheduling heuristics in heterogeneous computing systems." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999863.

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Zavoral, F., D. Bednárek, J. Yaghob, and J. Dokulil. "Automatic Extraction of Navigation Structure in Scheduling Tools." In Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.696-095.

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Etinski, Maja, Julita Corbalan, Jesus Labarta, and Mateo Valero. "Linear programming based parallel job scheduling for power constrained systems." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999809.

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Nadeem, M. Faisal, S. Arash Ostadzadeh, Stephan Wong, and Koen Bertels. "Task scheduling strategies for dynamic reconfigurable processors in distributed systems." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999811.

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Yin, Dengpan, and Tevfik Kosar. "A data-aware workflow scheduling algorithm for heterogeneous distributed systems." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999814.

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Sarikhani, Mehdi, Bahman Javadi, and Askari Parichehre. "Fault-aware scheduling in Grid environment based on linear programming." In Simulation (HPCS). IEEE, 2010. http://dx.doi.org/10.1109/hpcs.2010.5547058.

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Reports on the topic "Scheduling simulation"

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Davis, Wayne J., and Albert T. Jones. On-line concurrent simulation in production scheduling. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.88-3837.

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Davis, Wayne J., and Albert T. Jones. Real-time simulation and production scheduling systems. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.89-4070.

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Rhodes, David L., and Apostolos Gerasoulis. A Scheduling Approach To Parallel Harmonic Balance Simulation. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada502507.

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Davis, Wayne, and Albert Jones. Mathematical decomposition and simulation in real-time production scheduling. Gaithersburg, MD: error:, January 1987. http://dx.doi.org/10.6028/nbs.ir.87-3639.

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Rodin, Ervin Y., and Allen McCoy. Simulation and Optimization Methodologies and Military Transportation Network Routing and Scheduling. Fort Belvoir, VA: Defense Technical Information Center, August 2008. http://dx.doi.org/10.21236/ada499000.

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Amela, R., R. Badia, S. Böhm, R. Tosi, C. Soriano, and R. Rossi. D4.2 Profiling report of the partner’s tools, complete with performance suggestions. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.023.

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Abstract:
This deliverable focuses on the proling activities developed in the project with the partner's applications. To perform this proling activities, a couple of benchmarks were dened in collaboration with WP5. The rst benchmark is an embarrassingly parallel benchmark that performs a read and then multiple writes of the same object, with the objective of stressing the memory and storage systems and evaluate the overhead when these reads and writes are performed in parallel. A second benchmark is dened based on the Continuation Multi Level Monte Carlo (C-MLMC) algorithm. While this algorithm is normally executed using multiple levels, for the proling and performance analysis objectives, the execution of a single level was enough since the forthcoming levels have similar performance characteristics. Additionally, while the simulation tasks can be executed as parallel (multi-threaded tasks), in the benchmark, single threaded tasks were executed to increase the number of simulations to be scheduled and stress the scheduling engines. A set of experiments based on these two benchmarks have been executed in the MareNostrum 4 supercomputer and using PyCOMPSs as underlying programming model and dynamic scheduler of the tasks involved in the executions. While the rst benchmark was executed several times in a single iteration, the second benchmark was executed in an iterative manner, with cycles of 1) Execution and trace generation; 2) Performance analysis; 3) Improvements. This had enabled to perform several improvements in the benchmark and in the scheduler of PyCOMPSs. The initial iterations focused on the C-MLMC structure itself, performing re-factors of the code to remove ne grain and sequential tasks and merging them in larger granularity tasks. The next iterations focused on improving the PyCOMPSs scheduler, removing existent bottlenecks and increasing its performance by making the scheduler a multithreaded engine. While the results can still be improved, we are satised with the results since the granularity of the simulations run in this evaluation step are much ner than the one that will be used for the real scenarios. The deliverable nishes with some recommendations that should be followed along the project in order to obtain good performance in the execution of the project codes.
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