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Relevant bibliographies by topics / Production scheduling. Flexible manufacturing systems

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Academic literature on the topic 'Production scheduling. Flexible manufacturing systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Production scheduling. Flexible manufacturing systems"

1

Tomastik, R. N., P. B. Luh, and Guandong Liu. "Scheduling flexible manufacturing systems for apparel production." IEEE Transactions on Robotics and Automation 12, no. 5 (1996): 789–99. http://dx.doi.org/10.1109/70.538983.

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Bonfioli, M., M. Garetti, and A. Pozzetti. "Production scheduling and operational control of flexible manufacturing systems." Robotica 3, no. 4 (1985): 233–44. http://dx.doi.org/10.1017/s0263574700002332.

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SUMMARYOnly hardware and software flexibility combined can yield the overall system flexibility required in newly designed FMSs in which the expected part mix is quite large and continually changing.The paper shows how modular design and integration are fundamental steps in software design for the management and control of FMSs. The main subsystems of a control system, built up by putting together a number of standardized modules requiring little or no customization, are also described. An experimental control system designed according to these criteria is also presented.

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Fontes, Dalila B. M. M., and Seyed Mahdi Homayouni. "Joint production and transportation scheduling in flexible manufacturing systems." Journal of Global Optimization 74, no. 4 (2018): 879–908. http://dx.doi.org/10.1007/s10898-018-0681-7.

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Zeng, Bin, Rui Wang, and Hong Yu Chen. "Design of Simulation Model for Production Scheduling in Flexible Manufacturing Systems." Advanced Materials Research 230-232 (May 2011): 814–18. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.814.

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The complex interaction and the high costs of modern manufacturing systems make it necessary to evaluate their use performance. Production scheduling problem is one of the key problems of research of manufacturing systems since with a proper scheduling, the utilization of resources is optimized and orders are produced on time which improves the shop performance and associated cost benefits. However the complexity of modern production systems makes the use of analytical tools more difficult. Thus a computer simulation model of the existing computer integrated manufacturing system based on the control logic that describes the operation of the system is developed to test the performance of different scheduling rules with respect to mean flow time, machine efficiency and total run time as performance measures. According to the results of experiments, the model agrees with the real system.

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Wenzelburger, Philipp, and Frank Allgöwer. "Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0." Applied Sciences 11, no. 17 (2021): 8145. http://dx.doi.org/10.3390/app11178145.

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In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem.

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Belkahal Driss, Olfa, Ouadji Korbaa, Khaled Ghedira, and Pascal Yim. "A distributed transient inter-production scheduling for flexible manufacturing systems." Journal Européen des Systèmes Automatisés 41, no. 1 (2007): 101–23. http://dx.doi.org/10.3166/jesa.41.101-123.

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Homayouni, Seyed Mahdi, and Dalila B. M. M. Fontes. "Production and transport scheduling in flexible job shop manufacturing systems." Journal of Global Optimization 79, no. 2 (2021): 463–502. http://dx.doi.org/10.1007/s10898-021-00992-6.

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Archimède, Bernard, and Thierry Coudert. "A Multi-Agent Scheduling Approach for the Flexible Manufacturing Production Systems." IFAC Proceedings Volumes 31, no. 32 (1998): 143–48. http://dx.doi.org/10.1016/s1474-6670(17)36348-6.

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Sharafali, Moosa, Henry C. Co, and Mark Goh. "Production scheduling in a flexible manufacturing system under random demand." European Journal of Operational Research 158, no. 1 (2004): 89–102. http://dx.doi.org/10.1016/s0377-2217(03)00300-x.

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Ghosh, Soumen, and Cheryl Gaimon. "Routing flexibility and production scheduling in a flexible manufacturing system." European Journal of Operational Research 60, no. 3 (1992): 344–64. http://dx.doi.org/10.1016/0377-2217(92)90086-o.

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Dissertations / Theses on the topic "Production scheduling. Flexible manufacturing systems"

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Doulgeri, Z. "Production scheduling policy for flexible manufacturing systems." Thesis, Imperial College London, 1987. http://hdl.handle.net/10044/1/38292.

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Hsu, Chih-hua. "Dynamic scheduling of manufacturing systems /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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丘杰 and Jie Qiu. "Scheduling flexible manufacturing systems using fuzzy heuristics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31244671.

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Ghosh, Soumen. "Production planning and scheduling in a flexible manufacturing system environment." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu1272384308.

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Fenchel, Juergen. "Stable, distributed real-time scheduling of flexible manufacturing systems : an energy approach." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/16774.

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Hassanzadeh, Mostafai Pejman. "Rescheduling point determination in dynamic FMS using a flexibility metric methodology /." View online ; access limited to URI, 2007. http://0-digitalcommons.uri.edu.helin.uri.edu/dissertations/AAI3298369.

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Stallworth, Thomas Earl. "A forward scheduling heuristic for real time control of a flexible manufacturing system." Thesis, Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/24095.

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Chung, Sai-ho. "A genetic algorithm approach in distributed scheduling in multi-factory production networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37826773.

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Quadt, Daniel. "Lot-sizing and scheduling for flexible flow lines /." Berlin : Springer, 2004. http://www.loc.gov/catdir/toc/fy0602/2004109270.html.

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Chen, Chin-Sheng. "Methodologies for manufacturing system selection and for planning and operation of a flexible manufacturing system." Diss., Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/54242.

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A hierarchical methodology is developed for the overall design of manufacturing systems. The methodology consists of solutions to four levels of problems, namely, (1) manufacturing system selection, (2) shop loading, (3) machine loading and tool allocation, and (4) testing the feasibility of a schedule and determining strategies for the operational control of the system. Although these problem levels are developed in a hierarchical sense, they can be applied independently by assuming appropriate inputs to the problem level under consideration. The third and the fourth level problems are addressed in this research for the flexible manufacturing system. The first level of the hierarchical methodology addresses the problem of manufacturing system selection. The mathematical 4 model formulated for this problem captures the basic and integrated relationships among the systems and system components. This model provides a practical approach and a precise tool to determine an optimal mix of systems, to assign appropriate machines to each system, and to select the best material handling system for each system to best suit long-term production requirements at minimum costs. The second level of the hierarchical methodology addresses the shop loading problem. A mathematical model is developed for partitioning parts among the manufacturing systems selected at the first level to minimize total operating costs. For the third level problem, a mathematical model is formulated to obtain routings of parts through an FMS and to assign appropriate cutting tools to each machine in the system to minimize total machining cost. For the fourth level problem, a simulation model is developed for testing the feasibility of the solution obtained at the third level. It also helps to determine strategies for the operational control of the system. The computational experience with the mathematical models is presented using the MPSX-MIP/370 package. Sensitivity analysis is also performed to further understand system behavior under various operating conditions. Several new findings of the research are reported. Because of the special structure of the mathematical models, a computational refinement for their solution is also proposed based on Lagrangian relaxation.<br>Ph. D.

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Books on the topic "Production scheduling. Flexible manufacturing systems"

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Lee, Anita. Knowledge-based flexible manufacturing systems (FMS) scheduling. Garland, 1994.

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Production planning and scheduling in flexible assembly systems. Springer, 1999.

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Raman, Narayan. A survey of the literature on production scheduling as it pertains to flexible manufacturing systems. 6th ed. U.S. Dept. of Commerce, National Bureau of Standards, National Engineering Laboratory, Center for Applied Mathematics, 1985.

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Chaturvedi, Alok R. FMS scheduling using goal directed-conceptual aggregation. Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1990.

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Raman, Narayan. Real-time scheduling problems in a general flexible manufacturing system. Garland Pub., 1994.

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Quadt, Daniel. Lot-sizing and scheduling for flexible flow lines. Springer, 2004.

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

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8

CAM: Algorithmen und Decision Support für die Fertigungssteuerung. Springer-Verlag, 1989.

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9

Rachamadugu, Ram V. Due-date based scheduling in a flexible manufacturing system: (the ATS). U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Rachamadugu, Ram V. Due-date based scheduling in a flexible manufacturing system: (the ATS). U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Book chapters on the topic "Production scheduling. Flexible manufacturing systems"

1

Stecke, Kathryn E. "Production Planning and Scheduling in Flexible Manufacturing Systems." In Computer-Aided Production Management. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73318-5_18.

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Slomp, J. "The design and operation of flexible manufacturing shops." In The Planning and Scheduling of Production Systems. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4613-1195-9_7.

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Yih, Yuehwern, and Albert T. Jones. "Candidate Rule Selection to Develop Intelligent Scheduling Aids for Flexible Manufacturing Systems (FMS)." In Operations Research in Production Planning and Control. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78063-9_13.

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Ivanov, Dmitry, Alexandre Dolgui, and Boris Sokolov. "A Dynamic Approach to Multi-stage Job Shop Scheduling in an Industry 4.0-Based Flexible Assembly System." In Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66923-6_56.

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Blazewicz, Jacek, Klaus H. Ecker, Erwin Pesch, Günter Schmidt, Malgorzata Sterna, and Jan Weglarz. "Scheduling in Flexible Manufacturing Systems." In Handbook on Scheduling. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99849-7_17.

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Błażewicz, Jacek, Klaus H. Ecker, Erwin Pesch, Günter Schmidt, and Jan Węglarz. "Scheduling in Flexible Manufacturing Systems." In Scheduling Computer and Manufacturing Processes. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-03217-6_10.

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Błazewicz, Jacek, Klaus H. Ecker, Günter Schmidt, and Jan Węglarz. "Scheduling in Flexible Manufacturing Systems." In Scheduling in Computer and Manufacturing Systems. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79034-8_8.

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Blazewicz, Jacek, Klaus Ecker, Günter Schmidt, and Jan Wȩglarz. "Scheduling in Flexible Manufacturing Systems." In Scheduling in Computer and Manufacturing Systems. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-662-00074-8_8.

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Błażewicz, Jacek, Klaus H. Ecker, Erwin Pesch, Günter Schmidt, and Jan Węglarz. "Scheduling in Flexible Manufacturing Systems." In Scheduling Computer and Manufacturing Processes. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04363-9_10.

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Nakamura, Nobuto, and Tetsuro Shingu. "Scheduling of Flexible Manufacturing Systems." In Toward the Factory of the Future. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82580-4_27.

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Conference papers on the topic "Production scheduling. Flexible manufacturing systems"

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Celen, Merve, and Dragan Djurdjanovic. "Joint Maintenance and Production Operations Decision Making in Flexible Manufacturing Systems." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7258.

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In highly flexible and integrated manufacturing systems such as those in semiconductor manufacturing, there exist strong dynamic interactions between the equipment condition, operations executed on the equipment and the resulting product quality. These interactions necessitate a methodology that integrates the decisions of maintenance scheduling and production operations. Currently, maintenance and production operations decision-making are two decoupled processes. In this paper we aim to devise an integrated decision making policy for maintenance scheduling and production sequencing with the objective of maximizing an adaptive profit function, while taking into account operation-dependent degradation models and a production target. In order to obtain the optimal decision policy, a metaheuristic method based on the results of discrete-event simulations of the target manufacturing system is used. The new approach is demonstrated in simulations of a generic cluster tool routinely used in semiconductor manufacturing. The results show that jointly making maintenance and production sequencing decisions consistently outperforms the current practice of making these decisions separately.

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Iwamura, Koji, Hitoshi Hayashi, Yoshitaka Tanimizu, and Nobuhiro Sugimura. "A Human Oriented Real-Time Scheduling for Autonomous Distributed Manufacturing System: Control of Preferences of Human Operators Aimed at Improving Productivity." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7183.

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A real-time scheduling method considering human operators has been proposed to generate suitable production schedules for human operators, manufacturing equipment and jobs real-timely in the autonomous distributed manufacturing systems (ADMSs). The proposed scheduling method gives the higher priorities to the human operators, and generates the production schedules based on preference values, in order to improve the human operator’s motivation. It was shown, through case studies, that the proposed real-time scheduling method is effective to satisfy the preferences of the human operators. However, the make span is deteriorated especially for the cases where the human operators have similar preferences. Therefore, a new method is proposed to calculate the preference values considering not only the preferences of human operators but also improvement of productivity.

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Yan Cao, Xu Xu, and Fengru Sun. "Data mining for the optimization of production scheduling in flexible Manufacturing System." In 2008 3rd International Conference on Intelligent System and Knowledge Engineering (ISKE 2008). IEEE, 2008. http://dx.doi.org/10.1109/iske.2008.4730936.

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Homayouni, Seyed Mahdi, and Dalila B. M. M. Fontes. "Joint scheduling of production and transport with alternative job routing in flexible manufacturing systems." In INTERNATIONAL CONFERENCE ON “MULTIDIMENSIONAL ROLE OF BASIC SCIENCE IN ADVANCED TECHNOLOGY” ICMBAT 2018. Author(s), 2019. http://dx.doi.org/10.1063/1.5090012.

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Sudo, Yasuhiro, Keisuke Kasiwase, and Michiko Matsuda. "Verification of Scheduling Efficiency of an Autonomous Assembly System Using the Multi-Agent Manufacturing Simulator." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7231.

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This study is an examination of the effect of agent-based autonomous production scheduling, using the virtual factory on multi-agent simulation system. In the autonomous manufacturing system, a production plan is generated autonomously and dynamically, using communication and negotiation between agents that correspond to factory components. As infrastructure software for agent based manufacturing, the artisoc(c) is used as multi-agent simulator system. In this virtual factory, three types of agents are equipped. Users can operate a configuration such as input new jobs, adjusting a machine setting, etc, with monitoring conditions of agents. Additionally, this simulator has capability of input and output files such as assembly process schedules and logs of practical operations. As a result, by adjustment of the agent’s behavior with factory floor detail, the assembly schedule becomes more effective. The experiment was carried out to show that local negotiations contribute to global optimization when resources in the factory are effectively distributed and shared.

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Liu, Qiong, Youquan Tian, Chao Wang, Freddy O. Chekem, and John W. Sutherland. "Flexible Job-Shop Scheduling for Reduced Manufacturing Carbon Footprint." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2630.

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In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed model manufacturing system, a product carbon footprint oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system, is proposed to calculate the product carbon footprint in the mixed model manufacturing system. Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.

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Mahalingam, Vijay Kumar. "A Simulation Study of Flexible Manufacturing System Using Dynamic Scheduling Approach." In ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/esda2014-20484.

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This paper presents a simulation study aimed at evaluating the performances of a typical Flexible manufacturing system (FMS) problems in terms of make span, average flow time, average delay time at local buffers and average machine utilization, subject to different control strategies which include routing flexibilities and dispatching rules. The routing strategies under evaluation are ‘No Alternative Routings (NAR)’; ‘Alternative Routings Dynamic (ARD)’; and ‘Alternative Routings Planned (ARP)’. The ARP rule was introduced into the FMS and coded with C++ program. The above routing strategies are combined with five dispatching rules and studied in different production volumes. Since an FMS usually deals with a variety of products, effects of changing the part mix ratio are also discussed. Simulation study was performed by using ARENA software. Finally results indicate that the ‘alternative routings planned’ strategy outperforms other routing strategies in general.

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Sadeh, Norman M., David W. Hildum, Stephen F. Smith, Dag Kjenstad, Thomas J. Laliberty, and John McA’Nulty. "Integration of Process Planning and Production Scheduling for Agile Manufacturing: A Case Study." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/dfm-4330.

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Abstract As companies increasingly customize their products, move towards smaller lot production and experiment with more flexible customer/supplier arrangements, such as those made possible by Electronic Data Interchange (EDI), they increasingly require the ability to (1) respond quickly, accurately and competitively to customer requests for bids on new products and (2) efficiently work out supplier/subcontractor arrangements for these new products. This in turn requires the ability to (1) rapidly convert standard-based product specifications into process plans and (2) quickly integrate process plans for new orders into the existing production schedule to best accommodate the current state of the manufacturing enterprise. This paper describes IP3S, an Integrated Process Planning/Production Scheduling shell for agile manufacturing. IP3S utilizes a blackboard architecture that supports (1) concurrent development and dynamic revision of integrated process planning/production scheduling solutions, (2) maintenance of multiple problem instances and solutions, (3) flexible user-oriented decision making, (4) declarative representation of control information, (5) the use of a common representation for exchanging information, (6) coordination with outside information sources and (7) portability and ease of integration with legacy systems. IP3S has been validated in the context of a large and highly dynamic machine shop at Raytheon’s Andover manufacturing facility. Empirical results show an average performance improvement of 23% in solution quality over a decoupled approach to building process planning/production scheduling solutions.

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Laliberty, Thomas J., David W. Hildum, Norman M. Sadeh, et al. "A Blackboard Architecture for Integrated Process Planning/Production Scheduling." In ASME 1996 Design Engineering Technical Conferences and Computers in Engineering Conference. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/96-detc/dfm-1291.

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Abstract As companies increase the level of customization in their products, move towards smaller lot production and experiment with more flexible customer/supplier arrangements such as those made possible by Electronic Data Interchange (EDI), they increasingly require the ability to quickly, accurately and competitively respond to customer requests for bids on new products and efficiently work out supplier/subcontractor arrangements for these new products. This in turn requires the ability to rapidly convert standard-based product specifications into process plans and quickly integrate process plans for new orders into the existing production schedule to best accommodate the current state of the manufacturing enterprise. This paper describes IP3S, an Integrated Process Planning/Production Scheduling (IP3S) Shell for Agile Manufacturing. The IP3S Shell is designed around a blackboard architecture that emphasizes (1) concurrent development and dynamic revision of integrated process planning/production scheduling solutions, (2) the use of a common representation for exchanging process planning and production scheduling information, (3) coordination with outside information sources such as customer and supplier sites, (4) mixed initiative decision support, enabling the user to interactively explore a number of tradeoffs, and (5) portability and ease of integration with legacy systems. The system is scheduled for initial evaluation in a large and highly dynamic machine shop at Raytheon’s Andover manufacturing facility.

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Zou, Yun, and Guo-fu Yin. "Solution of Co-evolution Self-adaptive Genetic Algorithm to Production Scheduling Problem of Flexible Manufacturing System." In 2016 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA-16). Atlantis Press, 2016. http://dx.doi.org/10.2991/wartia-16.2016.172.

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