Zeitschriftenartikel: „Production scheduling“

1

Li, Yingying. „Research on game scheduling of galvanizing pipe production“. Functional materials 24, Nr. 3 (29.09.2017): 005–495. http://dx.doi.org/10.15407/fm24.03.490.

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2

Fithri, Prima, und 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, Nr. 2 (25.04.2016): 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

3

Mauergauz, Yuri. „Scheduling for production teams“. International Journal of Industrial Engineering Computations 6, Nr. 3 (2015): 339–50. http://dx.doi.org/10.5267/j.ijiec.2015.3.001.

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4

Zaremba, Marek B. „Scheduling of production processes“. Control Engineering Practice 4, Nr. 1 (Januar 1996): 141–42. http://dx.doi.org/10.1016/s0967-0661(96)90035-0.

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5

Kolisch, Rainer, Marcus Brandenburg und Claus Krüger. „Numetrix/3 Production Scheduling“. OR Spektrum 22, Nr. 3 (August 2000): 307–12. http://dx.doi.org/10.1007/pl00013336.

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6

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

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7

Nussbaum, Miguel, und Eduardo A. Parra. „A Production Scheduling System“. ORSA Journal on Computing 5, Nr. 2 (Mai 1993): 168–81. http://dx.doi.org/10.1287/ijoc.5.2.168.

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8

Taunton, J. C., und C. M. Ready. „Intelligent dynamic production scheduling“. Food Research International 27, Nr. 2 (Januar 1994): 111–16. http://dx.doi.org/10.1016/0963-9969(94)90151-1.

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9

Foote, B. L., A. Ravindran und S. Lashine. „Production planning & scheduling“. Computers & Industrial Engineering 15, Nr. 1-4 (Januar 1988): 129–38. http://dx.doi.org/10.1016/0360-8352(88)90075-7.

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10

Hruby, HF, und DM Panton. „Scheduling transfer champagne production“. Omega 21, Nr. 6 (November 1993): 691–97. http://dx.doi.org/10.1016/0305-0483(93)90010-i.

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11

Hastings, N. A. J., und C. H. Yeh. „Job oriented production scheduling“. European Journal of Operational Research 47, Nr. 1 (Juli 1990): 35–48. http://dx.doi.org/10.1016/0377-2217(90)90087-r.

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12

Kromodihardjo, Sudiyono, und Ergo Swasono Kromodihardjo. „Modeling of Well Service and Workover to Optimize Scheduling of Oil Well Maintenance“. Applied Mechanics and Materials 836 (Juni 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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Paprocka, Iwona, und Bożena Skołud. „Robust Scheduling, a Production Scheduling Model of Failures“. Applied Mechanics and Materials 307 (Februar 2013): 443–46. http://dx.doi.org/10.4028/www.scientific.net/amm.307.443.

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In the paper a production model with failures is presented where successive failure-free times are supposed to have normal distributions and are followed by normally distributed times of repairs. Unknown parameters of the distribution are estimated using e.g. empirical moments approach. Predictions of unknown parameters are done using classical regression method. Having Mean Time To First Failure, and Mean Time of Repair a disturbance robust predictive schedule is generated using an immune algorithm and rule Minimal Impact of Disturbed Operation on the Schedule.

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Oike, Shunsuke, Tomohisa Tanaka, Jiang Zhu und Yoshio Saito. „Robust Production Scheduling Using Autonomous Distributed Systems“. Key Engineering Materials 516 (Juni 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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Duan, Jing, Jianjun Yu und Guangwen Liang. „Food Production Enterprise Production Planning and Scheduling“. Advance Journal of Food Science and Technology 10, Nr. 8 (15.03.2016): 558–62. http://dx.doi.org/10.19026/ajfst.10.2183.

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16

Brown, J. R., und C. O. Ozgur. „Priority class scheduling: Production scheduling for multi-objective environments“. Production Planning & Control 8, Nr. 8 (Januar 1997): 762–70. http://dx.doi.org/10.1080/095372897234650.

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17

Dani, YUNIAWAN, und ITO Teruaki. „411 Production Scheduling of Central Kitchen for Bakso Restaurant Chain“. Proceedings of Conference of Chugoku-Shikoku Branch 2012.50 (2012): 41101–2. http://dx.doi.org/10.1299/jsmecs.2012.50.41101.

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18

Simeonov, S., und J. Simeonovová. „Simulation scheduling in food industry application“. Czech Journal of Food Sciences 20, No. 1 (18.11.2011): 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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Bhosale, Kailash Changdeorao, und Padmakar Jagannath Pawar. „Production planning and scheduling problem of continuous parallel lines with demand uncertainty and different production capacities“. Journal of Computational Design and Engineering 7, Nr. 6 (14.07.2020): 761–74. http://dx.doi.org/10.1093/jcde/qwaa055.

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Abstract Production planning and scheduling problems are highly interdependent as scheduling provides optimum allocation of resources and planning is an optimum utilization of these allocated resources to serve multiple customers. Researchers have solved production planning and scheduling problems by the sequential method. But, in this case, the solution obtained by the production planning problem may not be feasible for scheduling method. Hence, production planning and scheduling problems must be solved simultaneously. Therefore, in this work, a mathematical model is developed to integrate production planning and scheduling problems. The solution to this integrated planning and scheduling problem is attempted by using a discrete artificial bee colony (DABC) algorithm. To speed up the DABC algorithm, a k-means clustering algorithm is used in the initial population generation phase. This k-means clustering algorithm will help to converge the algorithm in lesser time. A real-life case study of a soap manufacturing industry is presented to demonstrate the effectiveness of the proposed approach. An objective function to minimize overall cost, which comprises the processing cost, material cost, utility cost, and changeover cost, is considered. The results obtained by using DABC algorithm are compared with those obtained by CPLEX software. There is a saving of ₹2 23 324 for weeks 1–4 in overall cost compared with the results obtained by using CPLEX software.

20

Qin, Ling, und Shu Lin Kan. „Production Dynamic Scheduling among Factories Based on Multi-Agent“. Advanced Materials Research 466-467 (Februar 2012): 1386–91. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1386.

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To solve fluctuation problem in production plan and scheduling among factories, a logic framework of production dynamic scheduling among factories based on multi-agent technology was constructed. In this framework, the production dynamic scheduling multi-agent negotiation rules and mechanism among factories were established. Furthermore, the production dynamic scheduling multi-agent negotiation procedure among factories was investigated. Finally, the simulation system of production dynamic scheduling among factories based on multi-agent is demonstrated and validated by Flexsim software. It has shown the proposed method can improve the adaptability and stability of production plan and scheduling, and provide a support for optimal and dynamic production plan and scheduling among factories.

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Wiens, Tobias, und Christian A. Ullrich. „Scheduling with team production effects“. International Journal of Operational Research 1, Nr. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10043215.

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22

Metaxiotis, Kostas S., John E. Psarras und Kostas A. Ergazakis. „Production scheduling in ERP systems“. Business Process Management Journal 9, Nr. 2 (April 2003): 221–47. http://dx.doi.org/10.1108/14637150310468416.

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23

Kumral, Mustafa. „Robust stochastic mine production scheduling“. Engineering Optimization 42, Nr. 6 (Juni 2010): 567–79. http://dx.doi.org/10.1080/03052150903353336.

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24

Chryssolouris, G., N. Giannelos, N. Papakostas und D. Mourtzis. „Chaos Theory in Production Scheduling“. CIRP Annals 53, Nr. 1 (2004): 381–83. http://dx.doi.org/10.1016/s0007-8506(07)60721-5.

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25

Matsumoto, Kazuki, Hiroyoshi Miwa und Toshihide Ibaraki. „Scheduling of corrugated paper production“. European Journal of Operational Research 192, Nr. 3 (Februar 2009): 782–92. http://dx.doi.org/10.1016/j.ejor.2007.10.019.

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26

Trebuňa, Peter, und Miriam Pekarč’ková. „APP Method of Production Scheduling“. Procedia Engineering 48 (2012): 679–83. http://dx.doi.org/10.1016/j.proeng.2012.09.570.

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27

Schulte, J. W., und B. D. Becker. „Production Scheduling Using Genetic Algorithms“. IFAC Proceedings Volumes 25, Nr. 7 (Mai 1992): 367–72. http://dx.doi.org/10.1016/s1474-6670(17)52393-9.

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28

Rodríguez-Somoza, B., R. Galán und E. A. Puente. „Production Scheduling Using AI Techniques“. IFAC Proceedings Volumes 23, Nr. 3 (September 1990): 387–92. http://dx.doi.org/10.1016/s1474-6670(17)52588-4.

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29

Lane, Robin, und Stephen Evans. „Solving problems in production scheduling“. Computer Integrated Manufacturing Systems 8, Nr. 2 (Mai 1995): 117–24. http://dx.doi.org/10.1016/0951-5240(95)00005-e.

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30

Kanet, John J., und Heimo H. Adelsberger. „Expert systems in production scheduling“. European Journal of Operational Research 29, Nr. 1 (April 1987): 51–59. http://dx.doi.org/10.1016/0377-2217(87)90192-5.

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31

Buxey, Geoff. „Production scheduling: Practice and theory“. European Journal of Operational Research 39, Nr. 1 (März 1989): 17–31. http://dx.doi.org/10.1016/0377-2217(89)90349-4.

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32

Водянова, Vyera Vodyanova, Ненашев und Oleg Nenashev. „Basic Models of Production Process Scheduling“. Administration 3, Nr. 2 (17.06.2015): 16–21. http://dx.doi.org/10.12737/11505.

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Paper has been devoted to methodological and theoretical ideas related to formation of hierarchy for basic dynamic models of production process scheduling which is an important component of quality control. However, the production process scheduling cannot be provided using universal modern simulation packages and requires the creation of special program, in which developed original algorithms for control of material and orders flows, and original scheduling algorithms are realized. For development of such original algorithms the hierarchy of material flows’ basic models may be useful. This hierarchy is based on hydraulic KT-model of elementary production link. In the paper the main directions of restrictions into the KT-model that allow expand basic models’ variety and create a flexible tool for production process scheduling model building have been presented.

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Zhang, Guohua, Xiang Li, Yang Yang und Minghuang Chen. „Study on Loading-Machine Production Scheduling Algorithm of Forest-Pulp-Paper Enterprise“. Computer and Information Science 10, Nr. 2 (09.03.2017): 25. http://dx.doi.org/10.5539/cis.v10n2p25.

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Through the needs of Loading-Machine storage scheduling in forestry-pulp-paper production logistics intelligent distribution system, analysis the Loading-Machine scheduling model of problems and improvement measures, put forward Loading-Machine intelligent production scheduling algorithm, based on ensuring the feasibility of scheduling, scheduling to rationalization, equalization, execute only, production process optimization, and realize the Loading-Machine intelligent production scheduling through the computer programming.

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Zhang, Lie Ping, und Yun Sheng Zhang. „Research on Production Scheduling Problems in Process Industry Based on Ant Colony System“. Advanced Materials Research 108-111 (Mai 2010): 519–24. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.519.

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In order to improve the production of process industry, the ant colony system(ACS) was applied to the production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model was established, whose goal was to obtain the shortest total process time. The search strategy, heuristic information rules, pheromone updating mechanism, process step starting time and detailed algorithm implementation of ACS were discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm were applied to implement the scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than the stochastic scheduling method, and can be applied to solve practical production scheduling problem for process industry.

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Lai, Ling Hong. „Mixed-Model Flow Production Scheduling Method Based on Multi-Agent and Hybrid Genetic Algorithm“. Applied Mechanics and Materials 63-64 (Juni 2011): 399–402. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.399.

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To solve the dynamic and complex problem of production scheduling, depending on the introduction between multi-agent and hybrid genetic algorithm in mixed-model flow production scheduling, this paper proposed a mixed-model flow production scheduling method based on multi-agent and hybrid genetic algorithm. On the basis of this model, the mixed-model flow production scheduling procedure and strategy based on multi-agent and hybrid genetic algorithm were established. Finally, mixed-model flow production scheduling simulation system based on multi-agent and hybrid genetic algorithm was demonstrated and validated by QUEST software. It has shown the proposed method can improve the benefit of production scheduling, and provide a support for adapting to complex and dynamic production scheduling in mixed-model flow production.

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Huang, Min, Ruixian Huang, Bo Sun und Linrong Li. „Research on the Production Scheduling Optimization for Virtual Enterprises“. Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/492158.

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Production scheduling is a rather difficult problem in virtual enterprises (VE) for the tasks of production which would be executed by some distributed and independent members. Factors such as the timing constraints of task and ability restrictions of the members are considered comprehensibly to solve the global scheduling optimization problem. This paper establishes a partner selection model based on an improved ant colony algorithm at first, then presents a production scheduling framework with two layers as global scheduling and local scheduling for virtual enterprise, and gives a global scheduling mathematical model with the smallest total production time based on it. An improved genetic algorithm is proposed in the model to solve the time complexity of virtual enterprise production scheduling. The presented experimental results validate the optimization of the model and the efficiency of the algorithm.

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Bożek, Andrzej, und Marian Wysocki. „Off-Line and Dynamic Production Scheduling – A Comparative Case Study“. Management and Production Engineering Review 7, Nr. 1 (01.03.2016): 21–32. http://dx.doi.org/10.1515/mper-2016-0003.

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Abstract A comprehensive case study of manufacturing scheduling solutions development is given. It includes highly generalized scheduling problem as well as a few scheduling modes, methods and problem models. The considered problem combines flexible job shop structure, lot streaming with variable sublots, transport times, setup times, and machine calendars. Tabu search metaheuristic and constraint programming methods have been used for the off-line scheduling. Two dynamic scheduling methods have also been implemented, i.e., dispatching rules for the completely reactive scheduling and a multi-agent system for the predictivereactive scheduling. In these implementations three distinct models of the problem have been used, based on: graph representation, optimal constraint satisfaction, and Petri net formalism. Each of these solutions has been verified in computational experiments. The results are compared and some findings about advantages, disadvantages, and suggestions on using the solutions are formulated.

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Pan, Feng Shan, Chun Ming Ye und Ji Hua Zhou. „Re-Entrant Production Scheduling Problem under Uncertainty Based on QPSO Algorithm“. Applied Mechanics and Materials 66-68 (Juli 2011): 1061–66. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1061.

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Production scheduling problem is the one of the most basic, important and difficult theoretical research in a manufacturing system. In the past decades of research, the classical scheduling theory has made signficant progress, but the actual scheduling problems are much more complicated than the classical theory. In order to study, the actual scheduling problems are often made much simpler. Therefore, it is difficult for the results of the classical scheduling theory to been put into practice. The considerable gap also exists between the classical scheduling theory and the actual scheduling problem. But with the increasingly fierce market competition, customers have become increasingly demanding product diversification, product life cycle is shorter and more sophisticated structure of the product makes the actual scheduling a large number of uncertainties, the traditional model has become difficult to obtain satisfactory results. This paper makes a analysis on the uncertainties of production scheduling,and tries to solve re-entrant production scheduling problem based on QPSO. It introduces the mathematical model and the solving process based on QPSO algorithm. Then it makes construction strategies to solve it. The paper simulates with Visual C. The results show that this algorithm is feasibility.

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S, Saravanakumar. „Simultaneous Scheduling of Assembly and Production Shops Using GA based Heuristic“. International Journal of Psychosocial Rehabilitation 24, Nr. 4 (30.04.2020): 6128–39. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020423.

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40

Gu, Jian, und Wei Min Mao. „A Production Scheduling Framework Integrated with Simulation Module“. Advanced Materials Research 602-604 (Dezember 2012): 1831–34. http://dx.doi.org/10.4028/www.scientific.net/amr.602-604.1831.

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It is necessary to achieve high system performances in terms of throughput rate and service level in today’s business environment. This can be achieved by implementing efficient and effective production planning methods complemented with precise and fast scheduling predictions. A framework is proposed to integrate real-time production data, scheduling mechanisms and simulation for providing realistic scheduling policies that could be used for operational and tactical decision-making. The focus is on the use of discrete event simulation utilizing relevant shop floor data, provided by an ERP system. A primary objective is to evaluate and characterize scheduling policies in a discrete manufacturing environment.

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Chen, Xiaowu, Guozhang Jiang, Yongmao Xiao, Gongfa Li und Feng Xiang. „A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED“. Mathematics 9, Nr. 18 (14.09.2021): 2256. http://dx.doi.org/10.3390/math9182256.

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Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.

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Zhu, Haihua, Yi Zhang, Changchun Liu und Wei Shi. „An Adaptive Reinforcement Learning-Based Scheduling Approach with Combination Rules for Mixed-Line Job Shop Production“. Mathematical Problems in Engineering 2022 (05.09.2022): 1–14. http://dx.doi.org/10.1155/2022/1672166.

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Affected by economic globalization and market diversification, more manufacturing enterprises realize that large-scale production cannot adapt to the current market environment. The new trend of multivariety customized mixed-line production brings a higher level of disturbances and uncertainties to production planning. Traditional methods cannot be directly applied to the classic flexible job shop scheduling problem (FJSP). Therefore, this paper presents an adaptive scheduling method for mixed-line job shop scheduling. First, the scheduling problem caused by combined processing constraints is studied and transformed by introducing the definition of virtual operation. According to the situation of the coexistence of trial-production and batch production, the disturbance processing mechanism is established. And a scheduling decision model is established based on contextual bands (CBs) in reinforcement learning to overcome the shortcoming of poor performance of traditional single dispatching rule strategy. Through continuous trial and error learning, each scheduler can select the most suitable scheduling rules according to the environment state. Finally, we benchmark the performance of the scheduling algorithm with scheduling methods based on a variety of single scheduling rules. The results show that the proposed algorithm not only improves the performance in the mixed production scheduling problem but also effectively copes with emergency trial-production orders.

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Bierwirth, Christian, und Dirk C. Mattfeld. „Production Scheduling and Rescheduling with Genetic Algorithms“. Evolutionary Computation 7, Nr. 1 (März 1999): 1–17. http://dx.doi.org/10.1162/evco.1999.7.1.1.

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A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed atreasonable runtime costs.

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KUSWANDI, IMRON. „MINIMASI MASKEPAN DENGAN PENJADWALAN PRODUKSI PADA TIPE PRODUKSI BERULANG“. Jurnal Teknik Industri 11, Nr. 1 (18.02.2012): 84. http://dx.doi.org/10.22219/jtiumm.vol11.no1.84-93.

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Methodologically there are some problems in the methods of scheduling production whichhave been available. In the methods of scheduling production which have been available, it isoften less capable for giving the real condition images from the real systems. It is indicated bythe given assumption that each operation should be finished previously before the other operationsare done. This case is inappropriate if applied in the repetitive production types as happenedin X Gresik, Co. Ltd. Because methodologically there are some problems in the methodsof scheduling conventional production, so in this research the methods of scheduling conventionalproduction are modified by using Microsoft excel application software, so it enables inthis method to handle the case of scheduling production in the types of repetitive production.urthermore, by using the methods of scheduling production modified by using Microsoft excelapplication software, the scheduling can be achieved by the better makespan (makespan =471,17 hours), so the production facility utilities are also more optimal compared to productionscheduling results by conventional approach (makespan = 893,7 hours).

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Xiao, Can Jun, Jin Ming Li und Jin Yao. „Semiconductor Assembly and Test Production Line Simulation Technology“. Advanced Materials Research 490-495 (März 2012): 3562–67. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.3562.

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The technology to simulate the semiconductor production line, consider main factors that will impact the production, and get the output from the simulation system as the reference for production scheduling is presented in this paper. By initiating the simulation model, include the die delivery information, equipment occupation information and etc. base on the FIFO (first in first out) and other principles, simulated the discrete event with four categories and also for two basic actions. The prediction result aligned with the actual production by T test. Changing the scheduling schemes at the same initial state of product line, engineer could obtain the optimal scheduling scheme by the comparing different simulation results. This study is not only an efficient, visual scheduling method, but also it is the basis for product re-scheduling. And this technology has been deployed in one ATM (Assembly and Test Manufacturing factory) factory in Chengdu and gets positive feedback.

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Yura, K. „A Revised Cyclic Production Method“. Journal of Engineering for Industry 113, Nr. 3 (01.08.1991): 328–34. http://dx.doi.org/10.1115/1.2899704.

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The objective of the cyclic scheduling is the minimization of the job output interval between cycles from a manufacturing system in a steady state. A revised cyclic production method was developed to minimize the job output interval. The job output interval was obtained by analyzing the workflow network and the process network. An optimum scheduling procedure was constructed and a numerical example was presented.

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Asmawar, Mellysa. „USULAN PENJADWALAN PRODUKSI PRODUK ST 37777 PT EBAKO NUSANTARA PADA DEPARTEMEN SMOOTHMILLING UNTUK MEMINIMASI MAKESPAN“. J@ti Undip : Jurnal Teknik Industri 13, Nr. 1 (31.03.2018): 61. http://dx.doi.org/10.14710/jati.13.1.61-66.

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AbstrakProses produksi ST 37777 di PT Ebako Nusantara menggunakan jadwal yang didasarkan oleh proses-proses yang dilakukan dengan menggunakan data historis yang telah ada dari proses produksi yang telah dilakukan. PT Ebako Nusantara merupakan industri manufaktur yang bergerak di bidang furnitur yang berlokasi di Terboyo, Semarang, Jawa Tengah. Dalam proses produksi ST 37777, terdapat 11 mesin dan 16 job dimana setiap job memiliki urutan mesin yang berbeda. Penjadwalan yang ada untuk produk tipe ST 37777 dengan tipe jobshop belum menerapkan suatu ketetapan dalam penentuan waktu dan urutan pengerjaan mesin yang efektif sehingga masih banyak job yang selesai terlambat. Untuk itu diperlukan suatu penjadwalan mesin yang efektif sehingga dapat memenuhi waktu produksi pesanan sesuai dengan yang telah disepakati. Penjadwalan jobshop diperlukan untuk memaksimumkan efisiensi dan utilitas sumber daya di lantai produksi. Penentuan jadwal mesin ini bertujuan meminimasi makespan dengan menggunakan Software WINQSB modul job schedulling. Metode yang digunakan adalah metode Short Processing Time. Hasil penjadwalan menggunakan Software WINQSB diperoleh makespan menjadi 15 jam dengan hasil penjadwalan tersebut tidak ada job yang terlambat dan semua job dikerjakan berurutan. AbstractThe production process of ST 37777 in PT Ebako Nusantara uses a schedule based on the processes performed using existing historical data from the production process that has been done. PT Ebako Nusantara is a manufacturing industry engaged in furnitur located in Terboyo, Semarang, Central Java. In the production process ST 37777, there are 11 machines and 16 jobs where each job has a different sequence of machines. The existing scheduling for ST 37777 type product with jobshop type has not been applied a determination in the timing and sequence of effective machine work so that many jobs are finished too late. For that required an effective engine scheduling so that it can meet the production time of orders in accordance with the agreed. Jobshop scheduling is needed to maximize efficiency and resource utilities on the production floor. Determination of this machine schedule aims to minimize the makespan using WINQSB Software job scheduling module. The method used is the method of Short Processing Time. The scheduling result using WINQSB software obtained makespan to 15 hours with scheduling result no job is late and all job done in sequence. Keywords: Jobshop Scheduling; Short Processing Time; Makespan Minimization

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Yu, Jian Guo, Mei Lin Feng, Yu Wu und Peng Peng Huang. „Research on Design and Development for Integrated Production Planning and Scheduling System of Rare Metal Manufacturing Based on WEB Platform“. Applied Mechanics and Materials 271-272 (Dezember 2012): 1490–94. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1490.

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Aiming at the design and development of integrated production planning and scheduling system(PPSS) of rare metal manufacturing, the goals and main functions of the integrated production planning and scheduling system were analyzed and designed based on the analysis of rare metal manufacturing enterprise’s main characteristics and management status. Through analysis of the present situation and trend of production planning and scheduling, an integrated system architecture was proposed. Key technologies of system integration and system developing were delivered, and some key functions of production planning and scheduling system which include annual plans management, monthly plans management, MRP management, production scheduling management and production Kanban management were developed based on JSP technology and WEB platform. The practical applications in some rare metal manufacturing enterprises were proved that the integrated production planning and scheduling system could enhance the manufacturing management level effectively.

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Zhu, Bao Lin, und Shou Feng Ji. „Steelmaking-Hot Rolling Scheduling Model and Method for Integrated Management in Iron and Steel Enterprises“. Advanced Materials Research 860-863 (Dezember 2013): 3094–99. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.3094.

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Iron and steel production scheduling problems are different from general production scheduling in machine industry. They have to meet special demands of steel production process. The CCR production manner dramatically promotes the revolution in technology and management, especially to planning and scheduling. In this paper, a scheduling model is presented to integrate the three working procedures and the lagrangian relaxation technology is proposed to get the optimal solution of the scheduling model. Finally, numerical examples are given to demonstrate the effectiveness of the integrated model and method.

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HE, ZHENGWEI, und RENZHONG TANG. „NUMERICAL SIMULATION OF DYNAMIC PRODUCTION SCHEDULING BASED ON ICAM“. Journal of Advanced Manufacturing Systems 07, Nr. 01 (Juni 2008): 55–58. http://dx.doi.org/10.1142/s0219686708001085.

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In order to resolve the problem of unreasonably allocating resources during the production process in discrete manufacturing enterprises, a method of dynamic production scheduling based on Information Coordinates Analysis Method (ICAM) was put forward. The communication modes of manufacturing equipment and the characteristic of the manufacturing process were analyzed, and the real-time data acquisition system for production field was established. The production information repository was built by analyzing the collected real-time data. With abstracting the correlative information, the information nodes were produced and the information coordinates were established. With the support of real-time information repository, the information in the production field could be available at any time by analyzing the typical information nodes. The feedback information could affect the original scheduling, thus the dynamic production scheduling was realized. Comparing with the classical analysis methods, ICAM can reflect the utilization state of the resources more directly during the process of the production scheduling, the scheduling rules can be set down more conveniently, and it is easier to apply the Object-Oriented method into the numerical simulation of production scheduling. The result of the numerical simulation proves that ICAM can effectively optimize the process of the typical production scheduling.

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