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Journal articles on the topic 'Reactive scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

SAKAGUCHI, Tatsuhiko, Yoshitaka TANIMIZU, Tsuyoshi MIYAMAE, Yasuhiro MAEDA, Keiichi SHIRASE, and Nobuhiro SUGIMURA. "Improvement of crossover operator in genetic algorithm for reactive scheduling(Manufacturing systems and Scheduling)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.2 (2005): 427–32. http://dx.doi.org/10.1299/jsmelem.2005.2.427.

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2

SPRATT, BELINDA. "REACTIVE OPERATING THEATRE SCHEDULING." Bulletin of the Australian Mathematical Society 98, no. 3 (2018): 520–21. http://dx.doi.org/10.1017/s0004972718000631.

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3

SZELKE, ELIZABETH, and ROGER M. KERR. "Knowledge-based reactive scheduling." Production Planning & Control 5, no. 2 (1994): 124–45. http://dx.doi.org/10.1080/09537289408919480.

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4

Wang, Jun Liang, Jie Zhang, and Wei Qin. "GA-Based Proactive-Reactive Scheduling Mechanism for a Flexible Job Shop Problem under Multi-Uncertainty." Key Engineering Materials 620 (August 2014): 668–75. http://dx.doi.org/10.4028/www.scientific.net/kem.620.668.

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This article investigates the flexible job-shop scheduling problem (FJSP) with multi-uncertain. A proactive-reactive scheduling mechanism is put forward to against the fluctuating process time and equipment breakdowns. This mechanism consists of two stages, including proactive scheduling stage and reactive scheduling stage. In the proactive scheduling stage, the redundancy-based technique is used to generate robust baseline schedules; in the reactive scheduling stage, a reactive scheduling is adopted to rectify the predictive scheduling to adapt to the occurrence of machine failures. Based on

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5

Gokila, L., V. Poongodi, and Dr K. Thangadurai. "Multi Scheduling Reactive Resource Sharing for Dynamic Dataflow in Cloud Environment." Bonfring International Journal of Data Mining 6, no. 4 (2016): 46–52. http://dx.doi.org/10.9756/bijdm.8307.

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6

Bharadwaj, Anandhi, Ajay S. Vinze, and Arun Sen. "Blackboard architecture for reactive scheduling." Expert Systems with Applications 7, no. 1 (1994): 55–65. http://dx.doi.org/10.1016/0957-4174(94)90026-4.

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7

Li, Zukui, and Marianthi G. Ierapetritou. "Reactive scheduling using parametric programming." AIChE Journal 54, no. 10 (2008): 2610–23. http://dx.doi.org/10.1002/aic.11593.

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8

SAKAGUCHI, Tatsuhiko, Hidetoshi KAMIMURA, and Keiichi SHIRASE. "Computational Simulation for Decision of Scheduling Period in Reactive Scheduling." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2007.4 (2007): 8F618. http://dx.doi.org/10.1299/jsmelem.2007.4.8f618.

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9

SAKAGUCHI, Tatsuhiko, Toshihide KAMIMURA, and Keiichi SHIRASE. "Computational Simulation for Decision of Scheduling Period in Reactive Scheduling." Journal of Advanced Mechanical Design, Systems, and Manufacturing 2, no. 4 (2008): 753–61. http://dx.doi.org/10.1299/jamdsm.2.753.

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10

Dhingra, J. S., K. L. Musser, and G. L. Blankenship. "Reactive Operations Scheduling for Manufacturing Systems." IFAC Proceedings Volumes 25, no. 28 (1992): 271–75. http://dx.doi.org/10.1016/s1474-6670(17)49508-5.

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11

Pai Chou, E. A. Walkup, and G. Borriello. "Scheduling for reactive real-time systems." IEEE Micro 14, no. 4 (1994): 37–47. http://dx.doi.org/10.1109/40.296156.

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12

Van de Vonder, Stijn, Francisco Ballestín, Erik Demeulemeester, and Willy Herroelen. "Heuristic procedures for reactive project scheduling." Computers & Industrial Engineering 52, no. 1 (2007): 11–28. http://dx.doi.org/10.1016/j.cie.2006.10.002.

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13

Sauer, Jürgen. "Vertical Data Integration for Reactive Scheduling." KI - Künstliche Intelligenz 24, no. 2 (2010): 123–29. http://dx.doi.org/10.1007/s13218-010-0025-3.

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14

Wang, Weixin, Jiafu Su, Jin Xu, and Xianlong Ge. "Reactive Strategies in the Multiproject Scheduling with Multifactor Disruptions." Mathematical Problems in Engineering 2020 (May 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/3154047.

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Multiproject scheduling aims at the generation of the baseline schedules, which has been studied for several years with the goal of minimizing the total cost of the project. In this paper, we analyzed the impact of network structure disruption, activity disruption, and resource disruption on scheduling scheme, respectively; this problem is related to the disruptions in scheduling process, which leads to a deviation between actual scheduling and baseline scheduling. The mode of the related activities is changed and the start time is reset. Because it is a NP-hard problem and involves a large nu

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15

Sun, Peng, Ming Wu Luo, and Chao Xia Sun. "Optimization Method of Reactive Power Generation in Wind Plant Based on DE Algorithm." Advanced Materials Research 953-954 (June 2014): 543–51. http://dx.doi.org/10.4028/www.scientific.net/amr.953-954.543.

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Reactive power optimization scheduling problem of wind plant including capacitor and fan is researched in this paper. According to the structure of the wind plant to set different scenarios , aiming at minimizing the reactive power loss , establish the reactive power optimization mathematical model of the wind plant, calculate the optimal reactive power of wind turbine in different positions , minimize the reactive power loss inside wind plant under the constraints of the offset range of node voltage and the reactive power demand of grid. Through the analysis of examples,clear whether a reason

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16

Paprocka, Iwona, Wojciech M. Kempa, Cezary Grabowik, and Krzysztof Kalinowski. "Sensitivity Analysis of Predictive Scheduling Algorithms." Advanced Materials Research 1036 (October 2014): 921–26. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.921.

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In the paper a survey of predictive and reactive scheduling methods is done in order to evaluate how ability of prediction of reliability characteristics influence over robustness criteria. Survey analysis is done for two job shop scheduling problems: 5x8 and 5x10. The paper answers the question: what method generates robust schedules in case of a failure of a bottleneck occurrence before or after maintenance actions? The Hybrid - Multi Objective Immune Algorithm (H-MOIA) is aided with heuristics: Minimal Impact of Disturbed Operation on the Schedule (MIDOS) for predictive scheduling and Minim

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17

KIM, YoungWoo, Akio INABA, Tatsuya SUZUKI, Shigeru OKUMA, Tadanao ZANMA, and Fumiharu FUZIWARA. "Reactive FMS Scheduling with RTA* and Supervisor." Transactions of the Society of Instrument and Control Engineers 37, no. 5 (2001): 446–54. http://dx.doi.org/10.9746/sicetr1965.37.446.

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18

Loader, R. J., N. Nissanke, and P. Elleby. "Reactive scheduling for hard real time systems." IFAC Proceedings Volumes 24, no. 7 (1991): 129–34. http://dx.doi.org/10.1016/b978-0-08-041699-1.50026-4.

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19

Lázaro, J. M. "Reactive Scheduling of a Flexible Manufacturing Cell." IFAC Proceedings Volumes 23, no. 3 (1990): 451–54. http://dx.doi.org/10.1016/s1474-6670(17)52598-7.

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20

Ying-Yi Hong and Csong-Ming Liao. "Short-term scheduling of reactive power controllers." IEEE Transactions on Power Systems 10, no. 2 (1995): 860–68. http://dx.doi.org/10.1109/59.387927.

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21

Joo, Byung Jun, Tay Jin Chua, Tian Xiang Cai, and Ping Chong Chua. "Coordination-based reactive resource-constrained project scheduling." Procedia CIRP 81 (2019): 51–56. http://dx.doi.org/10.1016/j.procir.2019.03.010.

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22

Hauptman, Boštjan, and Vladimir Jovan. "An approach to process production reactive scheduling." ISA Transactions 43, no. 2 (2004): 305–18. http://dx.doi.org/10.1016/s0019-0578(07)60039-1.

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23

Tanimizu, Y., T. Sakaguchi, K. Iwamura, and N. Sugimura. "Evolutional reactive scheduling for agile manufacturing systems." International Journal of Production Research 44, no. 18-19 (2006): 3727–42. http://dx.doi.org/10.1080/00207540600791632.

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24

Fahmy, Sherif A., Subramaniam Balakrishnan, and Tarek Y. ElMekkawy. "A generic deadlock-free reactive scheduling approach." International Journal of Production Research 47, no. 20 (2009): 5657–76. http://dx.doi.org/10.1080/00207540802112652.

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25

TANIMIZU, Yoshitaka, Yusuke KOMATSU, Chisato OZAWA, Koji IWAMURA, and Nobuhiro SUGIMURA. "Co-evolutionary Genetic Algorithms for Reactive Scheduling." Journal of Advanced Mechanical Design, Systems, and Manufacturing 4, no. 3 (2010): 569–77. http://dx.doi.org/10.1299/jamdsm.4.569.

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26

Deblaere, Filip, Erik Demeulemeester, and Willy Herroelen. "Reactive scheduling in the multi-mode RCPSP." Computers & Operations Research 38, no. 1 (2011): 63–74. http://dx.doi.org/10.1016/j.cor.2010.01.001.

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27

Stuart, Kari, and Erhan Kozan. "Reactive scheduling model for the operating theatre." Flexible Services and Manufacturing Journal 24, no. 4 (2011): 400–421. http://dx.doi.org/10.1007/s10696-011-9111-6.

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28

RUHLUSARAÇ, Murat, and Filiz ÇALIŞKAN. "A MATHEMATICAL MODEL FOR DYNAMIC PROJECT SCHEDULING PROBLEM AND REACTIVE SCHEDULING IMPLEMENTATION." Business & Management Studies: An International Journal 8, no. 4 (2020): 83–97. http://dx.doi.org/10.15295/bmij.v8i4.1708.

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In today's real-life implementations, projects are executed under uncertainty in a dynamic environment. In addition to resource constraints, the baseline schedule is affected due to the unpredictability of the dynamic environment. Uncertainty-based dynamic events experienced during project execution may change the baseline schedule partially or substantially and require projects' rescheduling. In this study, a mixed-integer linear programming model is proposed for the dynamic resource-constrained project scheduling problem. Three dynamic situation scenarios are solved with the proposed model,

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29

Janak, Stacy L., Christodoulos A. Floudas, Josef Kallrath, and Norbert Vormbrock. "Production Scheduling of a Large-Scale Industrial Batch Plant. II. Reactive Scheduling." Industrial & Engineering Chemistry Research 45, no. 25 (2006): 8253–69. http://dx.doi.org/10.1021/ie0600590.

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30

Salkuti, Surender Reddy. "Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2349. http://dx.doi.org/10.11591/ijece.v7i5.pp2349-2356.

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<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required t

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31

Kalinowski, Krzysztof, Damian Krenczyk, and Cezary Grabowik. "Predictive - Reactive Strategy for Real Time Scheduling of Manufacturing Systems." Applied Mechanics and Materials 307 (February 2013): 470–73. http://dx.doi.org/10.4028/www.scientific.net/amm.307.470.

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In this paper a solution of soft real time scheduling in manufacturing systems is presented. The basic requirements of scheduling as a real time system are discussed. The proposed rescheduling method uses predictive-reactive strategy and multi thread searching approach with rule-based heuristics, meta-heuristics and random modules.

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32

Mohamad Zamani, Mohamad Khairuzzaman, Ismail Musirin, Halim Hassan, et al. "Active and Reactive Power Scheduling Optimization using Firefly Algorithm to Improve Voltage Stability under Load Demand Variation." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 365. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp365-372.

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This paper presents active and reactive power scheduling using firefly algorithm (FA) to improve voltage stability under load demand variation. The study involves the development of firefly optimization engine for power scheduling process involving the active and reactive power for wind generator. The scheduling optimization of wind generator is tested by using IEEE 30-Bus Reliability Test System (RTS). Voltage stability of the system is assessed based in a pre-developed voltage stability indicator termed as fast voltage stability index (FVSI). This study also considers the effects on the loss

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33

Sawamoto, Jun, Yoshihiko Ohta, Hidekazu Tsuji, and Hisao Koizumi. "A Proposal of a Method of Reactive Scheduling for the Delivery Scheduling Problem." IEEJ Transactions on Electronics, Information and Systems 122, no. 5 (2002): 832–42. http://dx.doi.org/10.1541/ieejeiss1987.122.5_832.

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34

SUWA, Haruhiko, Daisuke FUJIMURAI, and Hiroaki SANDOH. "Cumulative Delay Based Reactive Scheduling Policy-Its Application to Single-Machine Dynamic Scheduling." Transactions of the Institute of Systems, Control and Information Engineers 17, no. 9 (2004): 371–78. http://dx.doi.org/10.5687/iscie.17.371.

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35

TANIMIZU, Yoshitaka, Sosuke UCHINO, Koji IWAMURA, and Nobuhiro SUGIMURA. "3363 Extension of a Reactive Scheduling Method Using Co-evolutionary Genetic Algorithms under Precedence Constraints." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2011.6 (2011): _3363–1_—_3363–6_. http://dx.doi.org/10.1299/jsmelem.2011.6._3363-1_.

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36

Brčić, Mario, and Danijel Mlinarić. "Tracking Predictive Gantt Chart for Proactive Rescheduling in Stochastic Resource Constrained Project Scheduling." Journal of information and organizational sciences 42, no. 2 (2018): 179–92. http://dx.doi.org/10.31341/jios.42.2.2.

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Proactive-reactive scheduling is important in the situations where the project collaborators need to coordinate their efforts. The coordination is mostly achieved through the combination of the shared baseline schedule and the deviation penalties. In this paper, we present an extension of predictive Gantt chart to the proactive-reactive scheduling needs. It can be used to track the evolution of the relationship between dynamic and static elements through the time. The dynamic elements are evolving probability distributions due to the uncertainty and revealed information. The static elements ar

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37

Chové, Etienne, Pierre Castagna, and Rosa Abbou. "Hoist Scheduling Problem: Coupling reactive and predictive approaches." IFAC Proceedings Volumes 42, no. 4 (2009): 2077–82. http://dx.doi.org/10.3182/20090603-3-ru-2001.0412.

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38

Minzu, Viorel, and Jean-Michel Henrioud. "Algorithm for Reactive Scheduling in Flexible Assembly Systems." IFAC Proceedings Volumes 28, no. 24 (1995): 305–9. http://dx.doi.org/10.1016/s1474-6670(17)46567-0.

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39

Odubiyi, Jidé, and David Zoch. "A heuristic approach to incremental and reactive scheduling." Telematics and Informatics 6, no. 3-4 (1989): 171–80. http://dx.doi.org/10.1016/s0736-5853(89)80014-0.

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40

Chun Wang, H. H. Ghenniwa, and Weiming Shen. "Distributed Scheduling for Reactive Maintenance of Complex Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, no. 2 (2008): 269–73. http://dx.doi.org/10.1109/tsmcc.2007.913916.

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41

TANIMIZU, Yoshitaka, Tsuyoshi MIYAMAE, Tatsuhiko SAKAGUCHI, and Nobuhiro SUGIMURA. "Improvement of reactive scheduling system for setup time." Proceedings of the JSME annual meeting 2004.4 (2004): 51–52. http://dx.doi.org/10.1299/jsmemecjo.2004.4.0_51.

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42

Jiang, Jian-Min, Huibiao Zhu, Qin Li, et al. "Analyzing Event-Based Scheduling in Concurrent Reactive Systems." ACM Transactions on Embedded Computing Systems 14, no. 4 (2015): 1–27. http://dx.doi.org/10.1145/2783438.

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43

Huguenard, Brian R., Michael J. Prietula, and F. Javier Lerch. "FRAGILITY IN EXPERTISE: A STUDY IN REACTIVE SCHEDULING." ACM SIGCHI Bulletin 21, no. 3 (1990): 36–40. http://dx.doi.org/10.1145/379088.1046612.

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44

Rodrigues, M. T. M., L. Gimeno, C. A. S. Passos, and M. D. Campos. "Reactive scheduling approach for multipurpose chemical batch plants." Computers & Chemical Engineering 20 (January 1996): S1215—S1220. http://dx.doi.org/10.1016/0098-1354(96)00210-4.

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45

Van de Vonder, Stijn, Erik Demeulemeester, and Willy Herroelen. "A classification of predictive-reactive project scheduling procedures." Journal of Scheduling 10, no. 3 (2007): 195–207. http://dx.doi.org/10.1007/s10951-007-0011-2.

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46

Wu, L. H., Xin Chen, X. D. Chen, and Qing Xin Chen. "The Research on Proactive-Reactive Scheduling Framework Based on Real-Time Manufacturing Information." Materials Science Forum 626-627 (August 2009): 789–94. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.789.

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Because of the dynamic and uncertain conditions of a real manufacturing system, many unforeseen events (e.g., machine breakdown, job revision, urgent jobs arrival, etc.) may lead to numerous schedule disruptions during schedule execution. In this paper, we present a new mixed technique that combines a proactive approach with a reactive approach to deal with scheduling problem under uncertainty. In the proactive phase, we build a robust baseline schedule that minimizes the schedules distance defined as the sum of the absolute deviations between the baseline and expected schedules. The robust ba

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47

Chai, Jia Yee, Tatsuhiko Sakaguchi, and Keiichi Shirase. "Dynamic Controls of Genetic Algorithm Scheduling in Supply Chain." International Journal of Automation Technology 4, no. 2 (2010): 169–77. http://dx.doi.org/10.20965/ijat.2010.p0169.

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In a supply chain environment, reactive scheduling process has to deal with both operational objective (minimizing total tardiness) and business objective (accommodating a new order into the current schedule). Genetic algorithm (GA) optimization has been applied in such reactive scheduling problem for job shop style manufacturer in our previous researches. An algorithm is introduced to dynamically adjust the objective function of GA optimization to minimize cost of delay penalties and to maximize the number of contracts captured by the manufacturer. The effectiveness of the proposed model is d

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48

Kaihara, Toshiya, Susumu Fujii, and Satoshi Kunimasa. "Learning Effects on Reactive Planning Type Self-Organised Scheduling." IEEJ Transactions on Electronics, Information and Systems 119, no. 10 (1999): 1247–54. http://dx.doi.org/10.1541/ieejeiss1987.119.10_1247.

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49

Henning, Gabriela P., and Jaime Cerdá. "Knowledge-based predictive and reactive scheduling in industrial environments." Computers & Chemical Engineering 24, no. 9-10 (2000): 2315–38. http://dx.doi.org/10.1016/s0098-1354(00)00589-5.

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50

Kerr, R. M., and D. Kibira. "Interactive Reactive Scheduling by Human Learning and Machine Induction." IFAC Proceedings Volumes 27, no. 4 (1994): 325–32. http://dx.doi.org/10.1016/s1474-6670(17)46045-9.

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