Thematische Bibliographien / Production scheduling

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Production scheduling“

Autor: Grafiati
Veröffentlicht am 4. Juni 2021
Zuletzt aktualisiert am 9. Juni 2025

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Zeitschriftenartikel zum Thema "Production scheduling"

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Fithri, Prima, and Fitri Ramawinta. "Penjadwalan Mesin dengan Menggunakan Algoritma Pembangkitan Jadwal Aktif dan Algoritma Penjadwalan Non-Delay untuk Produk Hydrotiller dan Hammermil pada CV. Cherry Sarana Agro." Jurnal Optimasi Sistem Industri 12, no. 2 (April 25, 2016): 377. http://dx.doi.org/10.25077/josi.v12.n2.p377-399.2013.

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Fulfillment of all demands of consumers who come to the product is one thing that always wanted to be achieved by a company. These requests are not independent of the company's ability to manufacture certain products. CV Cherry Sarana Agro manufactures a wide range of agricultural equipment, one of which is the product hydrotiller and hammermil. Demand for both products are always in large numbers for each period, however, the company could not meet the entire demand. One of the main factors that led this small company's production capacity for these two products is not optimal scheduling of machines made by companies, causing many to be a queue on a particular machine so that the total process operating time becomes very large. Scheduling method is used to optimize the scheduling of machines working on the report of this practice is actively scheduling method and the method of non-delay scheduling. The data needed to perform scheduling with both of these methods is the data used machines, data processing operations and data processing time of operation. With these three data, can be compared to the actual scheduling done by the company with the scheduling is done using active scheduling method and the method of non-delay scheduling. The most optimal scheduling is obtained after comparing the three methods used are scheduling using the non-delay scheduling. This method was chosen because the resulting make span is much smaller than the two other methods. This method is well applied in the company because in addition to reducing the total processing time, can also increase production capacity, so that all requests can be met.Keywords: Active schedulling method, non-delay schedulling method, makespan, hydrotiller, hammermil
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Kromodihardjo, Sudiyono, and Ergo Swasono Kromodihardjo. "Modeling of Well Service and Workover to Optimize Scheduling of Oil Well Maintenance." Applied Mechanics and Materials 836 (June 2016): 311–16. http://dx.doi.org/10.4028/www.scientific.net/amm.836.311.

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Well maintenance (well service and workover) is an operation needed by oil company to guarantee the optimum productionof its oil well.Well maintenance is performed using large equipment called hydraulic workover unit (HWU-Rig) which is available in limited number. Scheduling sequence of the HWU-Rig to do well service must meet the goal of the maintenance that is to minimize the loss of oil well production due to well breakdown. Thus minimizing breakdown time of well with high rate production is a priority. However, scheduling secuence of the HWU-Rig to perform its task for few days ahead become complicated due to the numerous alternatives of secuence to choose. Each alternatives of sequence yields a certain production loss. Arbitrarily scheduling sequence may not yield the goal og minimizing the loss of well production. This research was done by analyzing workover scheduling system and data from Kondur Petroleum such as well location, well production rate, and service time needed to be performed on wells. Algorithm to create schedulling sequence was developed in the research. The algorithm was then implemented in discrete simulation software, and yield the result of absolute global optimal solution, near optimal solution and local optimal solution of the HWU scheduling problem.
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Chuan-Kai Wu, Chuan-Kai Wu, Xiang-Yun Yi Chuan-Kai Wu, Zhi-Fei Guo Xiang-Yun Yi, Guang-Yu Chu Zhi-Fei Guo, and Ya-Min Wang Guang-Yu Chu. "Application of Deep Learning in Parameter Optimization of Automatic Production Process in Hot Rolling Production Line." 電腦學刊 35, no. 6 (December 2024): 123–36. https://doi.org/10.53106/199115992024123506010.

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<p>China is a major producer of steel and a pillar industry of the national economy, with huge coal consumption. With the increasingly prominent problem of energy shortage, traditional industrial models are constantly being transformed and upgraded using information technology, and a comprehensive energy information management system is being constructed. This article focuses on the production scheduling optimization problem of steel hot rolling production process. Firstly, based on the hot rolling process flow, the operation and maintenance time consumption of hot rolling equipment and the conversion time between hot rolling equipment are fully considered. The mathematical model of production scheduling for the hot rolling production process is established with the goals of minimizing work order completion time and balancing equipment working hours. Then, the classic NSGA-II algorithm is used as the basis for multi-objective solving. To solve the problems of the algorithm being prone to falling into local optima, insufficient distribution, and long solving time, the algorithm is improved by combining deep reinforcement learning ideas. Finally, through simulation experiments, the superiority of the improved algorithm in the convergence process is verified. At the same time, real hot rolling cases are used as scheduling objects to complete scheduling optimization and provide scheduling solutions.</p> <p> </p>
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Li, Yingying. "Research on game scheduling of galvanizing pipe production." Functional materials 24, no. 3 (September 29, 2017): 005–495. http://dx.doi.org/10.15407/fm24.03.490.

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Oike, Shunsuke, Tomohisa Tanaka, Jiang Zhu, and Yoshio Saito. "Robust Production Scheduling Using Autonomous Distributed Systems." Key Engineering Materials 516 (June 2012): 166–69. http://dx.doi.org/10.4028/www.scientific.net/kem.516.166.

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This research proposes a method of production scheduling using autonomous distributed systems. A concrete message protocol is proposed to realize the production scheduling which includes not only Machine but also Human and AGV scheduling. Moreover this method realizes real time scheduling and parallel scheduling. Therefore, a new structure of production scheduling is proposed, which can realize a change of the type of production scheduler to correspond with a type of production system.
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Mauergauz, Yuri. "Scheduling for production teams." International Journal of Industrial Engineering Computations 6, no. 3 (2015): 339–50. http://dx.doi.org/10.5267/j.ijiec.2015.3.001.

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Zaremba, Marek B. "Scheduling of production processes." Control Engineering Practice 4, no. 1 (January 1996): 141–42. http://dx.doi.org/10.1016/s0967-0661(96)90035-0.

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Kolisch, Rainer, Marcus Brandenburg, and Claus Krüger. "Numetrix/3 Production Scheduling." OR Spektrum 22, no. 3 (August 2000): 307–12. http://dx.doi.org/10.1007/pl00013336.

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Schmidt, Günter. "Modelling production scheduling systems." International Journal of Production Economics 46-47 (December 1996): 109–18. http://dx.doi.org/10.1016/0925-5273(95)00019-4.

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Nussbaum, Miguel, and Eduardo A. Parra. "A Production Scheduling System." ORSA Journal on Computing 5, no. 2 (May 1993): 168–81. http://dx.doi.org/10.1287/ijoc.5.2.168.

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Dissertationen zum Thema "Production scheduling"

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Stephenson, Paul A. "New dominance orders for scheduling problems /." *McMaster only, 1999.

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Huang, Chueng-Chiu S. "Stochastic scheduling." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/24834.

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Moghaddam, Atefeh. "Production scheduling : unavailabitlity of resources." Troyes, 2012. http://www.theses.fr/2012TROY0025.

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Dans le marché d’aujourd’hui, l’un des facteurs de survie les plus importants pour une entreprise est la garantie de la satisfaction des clients. Parfois, une entreprise ne peut pas satisfaire les exigences de ses clients à cause de contraintes telles que la capacité limitée ou les dates de fin strictes. Elle est donc obligée d’ignorer certaines commandes et de les rejeter ou encore de sous-traiter ces commandes. Dans les deux cas, la société est soumise à des frais. Dans cette thèse, nous étudions les différents types de ligne de production dans lesquels le rejet des commandes est possible. D’abord, nous avons étudié le problème d’ordonnancement bi-objectif sur une seule machine. Nous avons proposé une méthode exacte et plusieurs méta-heuristiques et heuristique efficaces pour trouver une bonne estimation de solutions non-dominées. Ainsi, nous avons étudié les différents concepts de dominance afin de limiter notre recherche dans la région la plus prometteuse du front de Pareto. Par la suite, nous avons étudié un problème d’ordonnancement réentrant à deux machines. Dans ce cas, le rejet des tâches a lieu en fonction de leurs dates de fin strictes. Nous avons considéré deux décisions simultanées : l’acceptation des tâches et leur ordonnancement. Un modèle de programmation linéaire mixte en nombres entiers et différents algorithmes génétiques multi-objectifs, ainsi que l’intégration des concepts de dominance divers, ont été proposés. Enfin, un problème plus général d’ordonnancement flow-shop de permutation réentrant avec dates de fin strictes et le rejet autorisé a été abordé<br>In today’s competitive market, one of the most important survival factors for a company is the achievement of customers’ satisfaction. Sometimes, a company can not fulfill its customers’ requirements due to the constraints such as limited capacity or tight due dates so it is obliged to ignore some orders and reject them or find another supplier to out-source those orders. In both cases, the company is charged a fee. In this thesis we investigate different types of production line in which rejection of the jobs is possible. At the beginning, we studied a variant of the problem as bi-objective single-machine scheduling problem with rejection. We proposed an exact method and some efficient metaheuristics and a heuristic to find a good estimation of non-dominated solution set. We found that in industrial case, most of the time the decision maker preferred the non-dominated solutions placed in the middle part of Pareto front. So we investigated different dominance concepts apart from Pareto in order to limit our research into the most promising region of Pareto front. Then we studied a two-machine re-entrant scheduling problem with rejection. In this case, the rejection of jobs occurred due to their strict due dates. So we examined simultaneous job acceptance and scheduling decisions. A mixed integer linear programming model and different multi-objective genetic-based algorithms were proposed by integrating various dominance concepts. Finally, a more general problem of re-entrant permutation flow-shop scheduling problem with strict due dates and rejection was tackled
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Wong, Yat-sing. "Production scheduling for virtual cellular manufacturing systems /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20720841.

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Yang, Bibo. "Models and algorithms for operations scheduling problems with resource flexibility and schedule disruptions." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0006344.

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Cross, Fionnuala Mary. "A model for loading and sequencing a flexible manufacturing cell." Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/25656.

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Wang, Jiaxin. "Modelling and solving for production scheduling." Doctoral thesis, Universite Libre de Bruxelles, 1996. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/212411.

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Lin, Neng-pai. "Master production scheduling in uncertain environments /." Connect to resource, 1989. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1262714277.

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JARDIM, LEANDRO SCHOEMER. "UNDERSTANDING COMPETENCE IN PRODUCTION SCHEDULING ROUTINES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34032@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO<br>Nas organizações industriais contemporâneas, a competição global e o avanço das tecnologias de informação tornam os produtos e processos cada vez mais semelhantes. O desempenho operacional passa, então, a ser um dos principais mecanismos de diferenciação. Nesse cenário, a área de planejamento e programação da produção ganha um inédito papel estratégico. O presente estudo se propôs a investigar esse novo papel com um olhar centrado nas rotinas organizacionais e nas pessoas que exercem a função. Mais especificamente, a partir de uma abordagem qualitativa e interpretativa, o trabalho utilizou o método fenomenográfico para compreender como os programadores concebem a competência nas suas rotinas organizacionais. A análise das entrevistas identificou três diferentes concepções. A primeira está associada à tarefa de alocação eficiente de recursos e materiais. A segunda vê a programação como um processo de mediação entre as diferentes áreas da empresa. E a terceira atribui ao programador de produção a reponsabilidade de fazer a gestão estratégica da operação. Além disso, foram identificadas seis dimensões do fenômeno que explicam e diferenciam as concepções, são elas: Raciocínio lógico, Conhecimento técnico (do processo produtivo), Conhecimento sistêmico (de outras áreas), Estratégia de negociação, Aspecto manifesto da rotina e Envolvimento com a estratégia corporativa. Os achados sugerem, por fim, que a dimensão estratégica da programação é verificável, em diferentes graus, nas suas rotinas. Além disso, ao revelar a concepção competência como sendo a mediação entre as diferentes áreas da empresa, o estudo traz as estratégias de negociação para o centro do debate sobre a realização competente das rotinas de programação da produção.<br>In contemporary industrial organizations, global competition and the advancement of information technologies make products and processes increasingly similar. Operational performance then becomes one of the main differentiation mechanisms. In this scenario, the area of production planning and scheduling gains an unprecedented strategic role. The present study aimed to investigate this new role with a focus on the organizational routines and the people who perform them. More specifically, from a qualitative and interpretative approach, this study used phenomenography method to understand how schedulers conceive competence in their organizational routines. The analysis of the interviews identified three different conceptions. The first is associated with the task of efficient allocation of resources and materials. The second sees scheduling as a process of mediation between the different areas of the company. And the third assigns to the production planner the responsibility to do the strategic management of the enterprise operations. In addition, six dimensions of the phenomenon that explain and differentiate the conceptions are identified: Logical reasoning, Technical knowledge (of the productive process), Systemic knowledge (of other areas), Negotiation strategy, Manifested aspect of the routine and Involvement with corporate strategy. The findings suggest that the strategic dimension of scheduling is verifiable, to different degrees, in production planning routines. In addition, by revealing the concept of competence as being the mediation between the different areas of the company, the study brings negotiation strategies to the center of the debate on the competent realization of production planning routines.
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Lin, Neng-Pai. "Master production scheduling in uncertain environments." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262714277.

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Bücher zum Thema "Production scheduling"

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Lopez, Pierre, and Franois Roubellat, eds. Production Scheduling. London, UK: ISTE, 2008. http://dx.doi.org/10.1002/9780470611050.

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Pierre, Lopez, and Roubellat Francois, eds. Production scheduling. Hoboken, NJ: John Wiley & Sons, 2008.

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Sarin, Subhash Chander. Stochastic scheduling. Cambridge: Cambridge University Press, 2010.

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Sarin, Subhash Chander. Stochastic scheduling. Cambridge: Cambridge University Press, 2010.

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Jarboui, Bassem, Patrick Siarry, and Jacques Teghem, eds. Metaheuristics for Production Scheduling. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118731598.

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Herrmann, Jeffrey W., ed. Handbook of Production Scheduling. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-33117-4.

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Herbert, Jane. Production scheduling: Module 40. 2nd ed. Rossendale: Footwear OPEN TECH Unit, 1989.

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W, Bronn John, and Humphreys Gary C, eds. Project and production scheduling. Chicago, Ill: Probus Pub. Co., 1987.

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Merit, Don. Print production scheduling primer. Pittsburgh: GATFPress, 2001.

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Jurgen, Dorn, and Froeschl Karl, eds. Scheduling of production processes. New York: Ellis Horwood, 1993.

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Buchteile zum Thema "Production scheduling"

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King, John R. "Production Scheduling." In Computer-Aided Production Management, 267–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73318-5_17.

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Stecca, Giuseppe. "Scheduling." In CIRP Encyclopedia of Production Engineering, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-35950-7_6571-4.

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Stecca, Giuseppe. "Scheduling." In CIRP Encyclopedia of Production Engineering, 1–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-642-35950-7_6571-5.

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Stecca, Giuseppe. "Scheduling." In CIRP Encyclopedia of Production Engineering, 1513–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-53120-4_6571.

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Stecca, Giuseppe. "Scheduling." In CIRP Encyclopedia of Production Engineering, 1092–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-20617-7_6571.

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Blazewicz, Jacek, Klaus H. Ecker, Erwin Pesch, Günter Schmidt, Malgorzata Sterna, and Jan Weglarz. "Computer Integrated Production Scheduling." In Handbook on Scheduling, 713–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99849-7_18.

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Toomey, John W. "Master Production Scheduling." In Materials Management / Logistics Series, 107–21. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4363-3_8.

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Ulfsby, Stig. "Dynamic production scheduling." In Computer Integrated Manufacturing, 153–69. London: Springer London, 1990. http://dx.doi.org/10.1007/978-1-4471-1786-5_13.

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Ray, Tapabrata, and Ruhul Sarker. "Optimum Oil Production Planning using an Evolutionary Approach." In Evolutionary Scheduling, 273–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48584-1_10.

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Gillett, Cary, and Jay Sheehan. "Planning and Scheduling." In The Production Manager's Toolkit, 49–64. 2nd ed. New York: Routledge, 2023. http://dx.doi.org/10.4324/9780367808174-8.

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Konferenzberichte zum Thema "Production scheduling"

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Renteria-Marquez, Ivan Arturo, Jose Carlos Garcia Marquez Basaldua, Oswaldo Aguirre, Bryan Eduardo Lara-Medrano, and Tzu-Liang Bill Tseng. "Cyber-Physical Production System Framework for Production Scheduling in Smart Factories." In 2024 Winter Simulation Conference (WSC), 1740–48. IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838901.

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Li, Yi, Yuhang Zhou, and Yijia Wang. "Optimization of Production Scheduling for Turbocharger Manufacturing Reprocessing." In 2024 International Conference on Energy and Electrical Engineering (EEE), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/eee59956.2024.10709701.

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Han, Xiao, Anqi Zhang, Beina Du, and Shengpeng Zhang. "Integrated Production Scheduling Based on Manufacturing System Resilience." In 2023 14th International Conference on Reliability, Maintainability and Safety (ICRMS), 193–99. IEEE, 2023. http://dx.doi.org/10.1109/icrms59672.2023.00044.

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Kovalyov, Mikhail Y., Xavier Delorme, and Alexandre Dolgui. "Workforce planning for cyclic production of multiple parts." In Workshop on dynamic scheduling problems. Polish Mathematical Society, 2016. http://dx.doi.org/10.14708/isbn.978-83-937220-7-5p47-50.

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Kovalyov, Mikhail Y., Xavier Delorme, and Alexandre Dolgui. "Workforce planning for cyclic production of multiple parts." In Workshop on dynamic scheduling problems. Polish Mathematical Society, 2016. http://dx.doi.org/10.14708/isbn.978-83-937220-7-p47-50.

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Li, Q. Y., L. Wang, and J. J. Xu. "Production data analytics for production scheduling." In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2015. http://dx.doi.org/10.1109/ieem.2015.7385838.

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Dongni Li and Yan Wang. "Production scheduling in intercell cooperative production mode." In 2012 24th Chinese Control and Decision Conference (CCDC). IEEE, 2012. http://dx.doi.org/10.1109/ccdc.2012.6244076.

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Kamara Esteban, Oihane, and Yoseba K. Penya. "Energy-aware foundry production scheduling." In IECON 2012 - 38th Annual Conference of IEEE Industrial Electronics. IEEE, 2012. http://dx.doi.org/10.1109/iecon.2012.6389447.

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Fujimura, Shigeru, and Wei Weng. "Spiral-evolutional production scheduling system." In 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2015. http://dx.doi.org/10.1109/ieem.2015.7385886.

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Zhang, Xuerui, Xue Hai, Yasuhiro Shimizu, and Shigeru Fujimura. "Self-construction Production Scheduling System." In 2006 IEEE International Conference on Automation Science and Engineering. IEEE, 2006. http://dx.doi.org/10.1109/coase.2006.326935.

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Berichte der Organisationen zum Thema "Production scheduling"

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Taber, Henry G. Tomato Irrigation Scheduling for Optimum Production. Ames: Iowa State University, Digital Repository, 2008. http://dx.doi.org/10.31274/farmprogressreports-180814-488.

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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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Davis, Wayne J., and Albert T. Jones. Artificial intelligence techniques in real-time production scheduling. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.88-3891.

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Smith, Parker. Production Scheduling at Ardent Mills in Alton, IL. Ames (Iowa): Iowa State University, January 2019. http://dx.doi.org/10.31274/cc-20240624-1159.

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6

TODD PACIFIC SHIPYARDS CORP SEATTLE WA. Flexible Production Scheduling System (The National Shipbuilding Research Program). Fort Belvoir, VA: Defense Technical Information Center, April 1986. http://dx.doi.org/10.21236/ada445649.

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7

Jarvis, James P., and Melissa R. Bowers. Hierarchical Production Planning and Scheduling in the Apparel Industry. Fort Belvoir, VA: Defense Technical Information Center, June 1995. http://dx.doi.org/10.21236/ada298601.

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8

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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9

Sadeh, Norman M., Thomas J. Laliberty, Robert V. Bryant, and Stephen F. Smith. Development of an Integrated Process Planning/Production Scheduling Shell for Agile Manufacturing. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada310806.

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10

Parwani, Viren. Improving Manufacturing Supply Chains by Integrating Lean Six Sigma and Production Scheduling. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-887.

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