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Journal articles on the topic 'Production scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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 (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 m

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2

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 becom

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3

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 (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

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4

Li, Yingying. "Research on game scheduling of galvanizing pipe production." Functional materials 24, no. 3 (2017): 005–495. http://dx.doi.org/10.15407/fm24.03.490.

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5

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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6

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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7

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

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8

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

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9

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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10

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

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11

Taunton, J. C., and C. M. Ready. "Intelligent dynamic production scheduling." Food Research International 27, no. 2 (1994): 111–16. http://dx.doi.org/10.1016/0963-9969(94)90151-1.

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12

Foote, B. L., A. Ravindran, and S. Lashine. "Production planning & scheduling." Computers & Industrial Engineering 15, no. 1-4 (1988): 129–38. http://dx.doi.org/10.1016/0360-8352(88)90075-7.

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13

Hruby, HF, and DM Panton. "Scheduling transfer champagne production." Omega 21, no. 6 (1993): 691–97. http://dx.doi.org/10.1016/0305-0483(93)90010-i.

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14

Hastings, N. A. J., and C. H. Yeh. "Job oriented production scheduling." European Journal of Operational Research 47, no. 1 (1990): 35–48. http://dx.doi.org/10.1016/0377-2217(90)90087-r.

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15

Paprocka, Iwona, and Bożena Skołud. "Robust Scheduling, a Production Scheduling Model of Failures." Applied Mechanics and Materials 307 (February 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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16

Qin, Ling, and Shu Lin Kan. "Production Dynamic Scheduling among Factories Based on Multi-Agent." Advanced Materials Research 466-467 (February 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

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17

Zhang, Guohua, Xiang Li, Yang Yang, and Minghuang Chen. "Study on Loading-Machine Production Scheduling Algorithm of Forest-Pulp-Paper Enterprise." Computer and Information Science 10, no. 2 (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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18

Zhang, Lie Ping, and Yun Sheng Zhang. "Research on Production Scheduling Problems in Process Industry Based on Ant Colony System." Advanced Materials Research 108-111 (May 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

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19

Duan, Jing, Jianjun Yu, and Guangwen Liang. "Food Production Enterprise Production Planning and Scheduling." Advance Journal of Food Science and Technology 10, no. 8 (2016): 558–62. http://dx.doi.org/10.19026/ajfst.10.2183.

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20

Deb, Indranil, and Ranjan Kumar Gupta. "Application of Heuristics in Production Planning and Job Scheduling." International Journal of Research Publication and Reviews 4, no. 6 (2023): 2531–40. http://dx.doi.org/10.55248/gengpi.4.623.45166.

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21

Lai, Ling Hong. "Mixed-Model Flow Production Scheduling Method Based on Multi-Agent and Hybrid Genetic Algorithm." Applied Mechanics and Materials 63-64 (June 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 demonstrat

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22

Norita, Defi, Ririn Regiana Dwi Satya, Andary Asvaroza Munita, and Asep Endih Nurhidayat. "Production Scheduling Model and Machine Assignment to Automotive Parts Manufacturing for Optimum Production." International Journal of Engineering Research and Advanced Technology 09, no. 07 (2023): 20–34. http://dx.doi.org/10.31695/ijerat.2023.9.7.3.

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PT. XYZ is a company engaged in the field of Automotive Parts Manufacturer. In carrying out its production this company uses make to order. The company carries out simple planning and scheduling, namely FCFS (First Come First Serve). The company has a problem, namely there is no production scheduling with the appropriate machine assignment. This causes the company to sometimes experience delays in completing the product. The purpose of this study is to overcome scheduling problems in companies to minimize order completion time (makespan). The method used in this study is the Heuristic Pour met

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23

Bhosale, Kailash Changdeorao, and 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, no. 6 (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 pr

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24

Liu, Junli, Deyu Zhang, Zhongpeng Liu, Tianyu Guo, and Yanyan Yan. "Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout." Sustainability 16, no. 20 (2024): 8851. http://dx.doi.org/10.3390/su16208851.

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Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system

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25

Simeonov, S., and J. Simeonovová. "Simulation scheduling in food industry application." Czech Journal of Food Sciences 20, No. 1 (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

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26

Zhu, Changbiao, Chongxin Wang, Zhonghua Ni, Xiaojun Liu, and Abbas Raza. "Modeling and Optimization of Cable Production Scheduling by Incorporating an Ant Colony Algorithm." Mathematics 13, no. 8 (2025): 1235. https://doi.org/10.3390/math13081235.

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With the development of small batch and multi-batch service production mode, manual scheduling by hand has been difficult to adapt to the production of a large number of complex orders. This work proposed a cable production scheduling optimization method based on an ant colony algorithm, aiming at solving the problems of the inefficiency and underutilization of resources in the process of traditional cable scheduling. Applying an ant colony (ACO) algorithm to solve the production scheduling problem achieved the intelligent scheduling and optimization of production tasks. The method utilizes th

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27

Brown, J. R., and C. O. Ozgur. "Priority class scheduling: Production scheduling for multi-objective environments." Production Planning & Control 8, no. 8 (1997): 762–70. http://dx.doi.org/10.1080/095372897234650.

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28

Huang, Min, Ruixian Huang, Bo Sun, and 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 sch

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29

Zhou, Qian, Xupeng Hu, Siyuan Peng, Yonghui Li, Tao Zhu, and Hongyan Shi. "Scheduling Optimization of Printed Circuit Board Micro-Hole Drilling Production Line Based on Complex Events." Processes 11, no. 11 (2023): 3073. http://dx.doi.org/10.3390/pr11113073.

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The interdependence between the scheduling method and the production efficiency of a micro-hole drilling production line for printed circuit boards (PCBs) holds significant importance, necessitating the optimization of such a production line’s scheduling. Consequently, this research paper presents a scheduling optimization approach for the micro-hole drilling production line of a PCB, utilizing complex events as its foundation. Initially, a complex event model was constructed to establish correlations among extensive production line data. Subsequently, the typical complex events associated wit

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30

Charles-Owaba, O. E., V. Oladokun, and O. Okunade. "Development of the Contiguous-cells Transportation Problem." Engineering, Technology & Applied Science Research 5, no. 4 (2015): 825–31. https://doi.org/10.5281/zenodo.28397.

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The issue of scheduling a long string of multi-period activities which have to be completed without interruption has always been an industrial challenge. The existing production/maintenance scheduling algorithms can only handle situations where activities can be split into two or more sets of activities carried out in non-contiguous sets of work periods. This study proposes a contiguous-periods production/maintenance scheduling approach using the Transportation Model. Relevant variables and parameters of contiguous-cells scheduling problem were taken from the literature. A scheduling optimizat

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31

Chen, Xiaowu, Guozhang Jiang, Yongmao Xiao, Gongfa Li, and Feng Xiang. "A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED." Mathematics 9, no. 18 (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

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32

Zhu, Haihua, Yi Zhang, Changchun Liu, and Wei Shi. "An Adaptive Reinforcement Learning-Based Scheduling Approach with Combination Rules for Mixed-Line Job Shop Production." Mathematical Problems in Engineering 2022 (September 5, 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

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33

Sharma, Pravesh. "Optimizing Production Scheduling in Manufacturing: Strategies and Technologies for Efficiency." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32497.

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This is manufacturing, efficient production scheduling is crucial for maximizing output and reducing costs. This abstract describes strategies and technologies to achieve this efficiency. It covers aspects like demand forecasting, resource allocation, optimizing task sequencing, and leveraging real-time tracking. By implementing these methods, manufacturers can streamline workflow, minimize idle time, and ensure timely deliveries. This report explores the challenges faced in production scheduling in manufacturing and examines strategies and technologies that can be used to optimize scheduling

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34

KUSWANDI, IMRON. "MINIMASI MASKEPAN DENGAN PENJADWALAN PRODUKSI PADA TIPE PRODUKSI BERULANG." Jurnal Teknik Industri 11, no. 1 (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 th

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35

Wiens, Tobias, and Christian A. Ullrich. "Scheduling with team production effects." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10043215.

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36

Metaxiotis, Kostas S., John E. Psarras, and Kostas A. Ergazakis. "Production scheduling in ERP systems." Business Process Management Journal 9, no. 2 (2003): 221–47. http://dx.doi.org/10.1108/14637150310468416.

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37

Kumral, Mustafa. "Robust stochastic mine production scheduling." Engineering Optimization 42, no. 6 (2010): 567–79. http://dx.doi.org/10.1080/03052150903353336.

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38

Chryssolouris, G., N. Giannelos, N. Papakostas, and D. Mourtzis. "Chaos Theory in Production Scheduling." CIRP Annals 53, no. 1 (2004): 381–83. http://dx.doi.org/10.1016/s0007-8506(07)60721-5.

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39

Matsumoto, Kazuki, Hiroyoshi Miwa, and Toshihide Ibaraki. "Scheduling of corrugated paper production." European Journal of Operational Research 192, no. 3 (2009): 782–92. http://dx.doi.org/10.1016/j.ejor.2007.10.019.

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40

Trebuňa, Peter, and 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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41

Schulte, J. W., and B. D. Becker. "Production Scheduling Using Genetic Algorithms." IFAC Proceedings Volumes 25, no. 7 (1992): 367–72. http://dx.doi.org/10.1016/s1474-6670(17)52393-9.

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42

Rodríguez-Somoza, B., R. Galán, and E. A. Puente. "Production Scheduling Using AI Techniques." IFAC Proceedings Volumes 23, no. 3 (1990): 387–92. http://dx.doi.org/10.1016/s1474-6670(17)52588-4.

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43

Lane, Robin, and Stephen Evans. "Solving problems in production scheduling." Computer Integrated Manufacturing Systems 8, no. 2 (1995): 117–24. http://dx.doi.org/10.1016/0951-5240(95)00005-e.

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44

Kanet, John J., and Heimo H. Adelsberger. "Expert systems in production scheduling." European Journal of Operational Research 29, no. 1 (1987): 51–59. http://dx.doi.org/10.1016/0377-2217(87)90192-5.

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45

Buxey, Geoff. "Production scheduling: Practice and theory." European Journal of Operational Research 39, no. 1 (1989): 17–31. http://dx.doi.org/10.1016/0377-2217(89)90349-4.

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46

Wiens, Tobias, and Christian A. Ullrich. "Scheduling with team production effects." International Journal of Operational Research 48, no. 3 (2023): 281–315. http://dx.doi.org/10.1504/ijor.2023.134783.

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47

Yu, Jian Guo, Mei Lin Feng, Yu Wu, and 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 (December 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 product

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48

Wang, Hao, Xiaoxia Zhang, Hui Yuan, Zhiguang Wu, and Ming Zhou. "A Lean Scheduling Framework for Underground Mines Based on Short Interval Control." Sustainability 15, no. 12 (2023): 9195. http://dx.doi.org/10.3390/su15129195.

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Production scheduling management is crucial for optimizing mine productivity. With the trend towards intelligent mines, a lean scheduling management mode is required to align with intelligent conditions. This paper proposes a lean scheduling framework, based on short interval control as an effective tool to adapt intelligent scheduling in underground mines. The framework shortens the production monitoring and adjustment cycle to near-real-time, enabling timely corrective measures to minimize schedule deviations and improve overall production efficiency. An intelligent scheduling platform is im

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49

Водянова, Vyera Vodyanova, Ненашев, and Oleg Nenashev. "Basic Models of Production Process Scheduling." Administration 3, no. 2 (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

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50

Zhu, Bao Lin, and Shou Feng Ji. "Steelmaking-Hot Rolling Scheduling Model and Method for Integrated Management in Iron and Steel Enterprises." Advanced Materials Research 860-863 (December 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

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