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Journal articles on the topic 'Dynamic scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Pan, Ying, Dong Juan Xue, Tian Yi Gao, Li Bin Zhou, and Xiao Yu Xie. "Modeling and Scheme Generation of Dynamic Flexible Job-Shop Scheduling." Applied Mechanics and Materials 423-426 (September 2013): 2232–36. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2232.

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Aiming at uncertain information and dynamic characteristic during flexible job-shop scheduling process, some kind of dynamic scheduling method for flexible job-shop scheduling problem (FJSP) is put forward based on real-time adjustment. A dynamic simulation solution mode framework is presented for FJSP. This framework is inspired by adaptive control, combined with the robust scheduling and foreseeing scheduling. It has both advantages of such two scheduling methods, and its stable and highly efficient. Preliminary scheme generation method based on foreseeing dynamics scheduling is introduced then. Foreseeing function is realized by fault-handling algorithm and dynamic simulation solver on the basis of Adaptive Genetic Algorithm (AGA).

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2

Jiang, Hai, and Cynthia Barnhart. "Dynamic Airline Scheduling." Transportation Science 43, no. 3 (August 2009): 336–54. http://dx.doi.org/10.1287/trsc.1090.0269.

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3

Yau, C. "Dynamic flight scheduling." Omega 17, no. 6 (January 1989): 533–42. http://dx.doi.org/10.1016/0305-0483(89)90057-1.

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4

Stillwell, Mark, Frederic Vivien, and Henri Casanova. "Dynamic Fractional Resource Scheduling versus Batch Scheduling." IEEE Transactions on Parallel and Distributed Systems 23, no. 3 (March 2012): 521–29. http://dx.doi.org/10.1109/tpds.2011.183.

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5

Huang, Yuan, Xiu Tian Yan, and Jing Yao Li. "Dual Resource Constrained Job Shop Dynamic Scheduling with Local Time Window." Advanced Materials Research 933 (May 2014): 708–13. http://dx.doi.org/10.4028/www.scientific.net/amr.933.708.

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To solve the Dual Resource Constrained Job Shop Dynamic Scheduling Problem with heterogeneous workers (DRCJDSP-HW),a three-tier dynamic scheduling system including dynamic disturbance analysis, dynamic disturbance evaluation and dynamic scheduling method was constructed based on the idea of scheduling with local time window. To minimize the revision of old scheduling scheme in DRCJDSP-HW, a hybrid dynamic scheduling algorithm was proposed based on the chromosome reduction mechanism and the scheduling optimization of local job in stages. The outstanding application effect of the dynamic scheduling system and the hybrid dynamic scheduling algorithm were validated reducing the influence of dynamic disturbances effectively by simulation experiment at last.

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6

Cheng, Mingang, Hiromi Itoh Ozaku, Noriaki Kuwahara, Kiyoshi Kogure, and Jun Ota. "Dynamic Scheduling in Inpatient Nursing." International Journal of Automation Technology 3, no. 2 (March 5, 2009): 174–84. http://dx.doi.org/10.20965/ijat.2009.p0174.

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To shorten the notoriously long waits for service in hospitals in Japan and to improve efficiency, we propose a scheduling algorithm with a 2-layer local search based on simulated annealing -- permutating (switching) (i) tasks among nurses and (ii) subtasks on each nurse. The scheduling algorithm generates a solution initializing our proposed dynamic scheduling to iteratively generate new, feasible schedules based on the scheduling algorithm to accommodate interruptions while preventing nurses' work hours from increasing. To verify the effectiveness of our proposed scheduling, we executed a set of nursing scheduling problems taken from those actually observed and focused on those that featuring frequent interruptions.

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7

Ivanov, D., and B. Sokolov. "Dynamic supply chain scheduling." Journal of Scheduling 15, no. 2 (August 11, 2010): 201–16. http://dx.doi.org/10.1007/s10951-010-0189-6.

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8

Agnetis, Alessandro, Stanisław Gawiejnowicz, Bertrand Miao-Tsong Lin, and Gur Mosheiov. "Editorial: Dynamic Scheduling Problems." Journal of Scheduling 23, no. 6 (October 24, 2020): 629–30. http://dx.doi.org/10.1007/s10951-020-00673-4.

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9

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

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10

Serpanos, Dimitrios N., Asser N. Tantawi, and Ahmed N. Tantawy. "Credit scheduling: adaptive scheduling with dynamic service quota." Computer Communications 21, no. 10 (July 1998): 889–97. http://dx.doi.org/10.1016/s0140-3664(98)00141-8.

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11

Zhang, Shi Hai, Zi Miao Zhang, and Zhong Min Wang. "Research on Gain Scheduling Control of Online Dynamic Balance for Flexible Spindle." Applied Mechanics and Materials 488-489 (January 2014): 1083–86. http://dx.doi.org/10.4028/www.scientific.net/amm.488-489.1083.

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The gain scheduling control is widely used in the system, whose dynamics parameters change with the operating conditions. In order to adapt the dynamic parameter variety of the high-speed and flexible spindle, the gain scheduling control method is applied to control the unbalance vectors of the correcting faces. The algorithm principle of gain scheduling control and its control method are given in the paper. The flexible spindle experiment system is designed to test the dynamic balance effect of gain scheduling control method. The experiment result indicates that the unbalance vectors of the correcting faces tend to be stable and close to the actual demand based on the gain scheduling control, and the residual unbalance vibration of the two monitoring points tend to be average and small.

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12

He, Dong Feng, Ai Jun Xu, Gang Yu, and Nai Yuan Tian. "Dynamic Scheduling Method for Steelmaking-Continuous Casting." Applied Mechanics and Materials 44-47 (December 2010): 2162–67. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2162.

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A method of dynamic scheduling of steelmaking-continuous casting is proposed, which includes static scheduling based on genetic algorithm and dynamic scheduling based on scheduling rules, mathematical model and complete rescheduling utilizing genetic algorithm. The simulation with eight hours’ production data in S steel plant showed that the method could draw quickly a high quality and performable dynamic scheduling plan according to random production disturbance. The average utilization rate of converter could reach 95% and the generation period of initial scheduling is less than 3 minutes. The max dynamic scheduling adjustment period did not exceed 1 minute.

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13

Khamooshi, Homayoun. "Dynamic Priority–Dynamic Programming Scheduling Method (DP)2SM: a dynamic approach to resource constraint project scheduling." International Journal of Project Management 17, no. 6 (December 1999): 383–91. http://dx.doi.org/10.1016/s0263-7863(98)00052-0.

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14

Ahmad, Ishfaq. "Editorial: Resource management in parallel and distributed systems with dynamic scheduling: Dynamic scheduling." Concurrency: Practice and Experience 7, no. 7 (October 1995): 587–90. http://dx.doi.org/10.1002/cpe.4330070702.

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15

PALIS, MICHAEL A., JING-CHIOU LIOU, SANGUTHEVAR RAJASEKARAN, SUNIL SHENDE, and DAVID S. L. WEI. "ONLINE SCHEDULING OF DYNAMIC TREES." Parallel Processing Letters 05, no. 04 (December 1995): 635–46. http://dx.doi.org/10.1142/s0129626495000564.

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The scheduling problem for dynamic tree-structured task graphs is studied and is shown to be inherently more difficult than the static case. It is shown that any online scheduling algorithm, deterministic or randomized, has competitive ratio Ω((1/g)/ log d(1/g)) for trees with granularity g and degree at most d. On the other hand, it is known that static trees with arbitrary granularity can be scheduled to within twice the optimal schedule. It is also shown that the lower bound is tight: there is a deterministic online tree scheduling algorithm that has competitive ratio O((1/g)/ log d(1/g)). Thus, randomization does not help.

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16

Chen, Zai Ping, and Hong Qiao Xu. "Research on Network Control System Using Improved EDF Dynamic Scheduling Algorithm." Advanced Materials Research 403-408 (November 2011): 2420–23. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2420.

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In this paper classic static and dynamic scheduling strategy is analyzed first, and then communication network of schedule ability judgment basis is given. An improved dynamic EDF scheduling algorithm is proposed in order to improve the scheduling task of real-time. The scheduling strategy is to change task priority according to the transmission error over deadline task when applying dynamic EDF scheduling strategy. True Time tool is used to build CAN network control system simulation platform. Dynamic EDF scheduling algorithm and improved scheduling algorithm are simulated respectively. The effectiveness of improved scheduling algorithm is verified by the simulation Keywords-Network control system; Scheduling Algorithm; True Time toolbox result.

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17

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 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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18

Hui, Ji Zhuang, Xiang Ding, and Kai Gao. "A FMS Dynamic Scheduling Optimization Strategy and Simulation Research." Applied Mechanics and Materials 389 (August 2013): 692–97. http://dx.doi.org/10.4028/www.scientific.net/amm.389.692.

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This paper studied the FMS dynamic scheduling problem which was based on Petri net FMS static scheduling optimization algorithm, which in accorder to solve the FMS actual production scheduling problems. A rolling window dynamic re-scheduling strategy was proposed which based on event driven and cycle driven. Then take the emergency machine failure often appearing in the actual workshop for example, this scheduling strategy was analyzed and applied to dynamic simulation and finally the effectiveness of the dynamic scheduling strategy was verified.

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19

Pan, Ying, Dong Juan Xue, Tian Yi Gao, Li Bin Zhou, and Xiao Yu Xie. "Research on Strategy of Dynamic Flexible Job-Shop Scheduling." Applied Mechanics and Materials 423-426 (September 2013): 2237–43. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2237.

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A dynamic scheduling method for FJSP is put forward based on real-time adjustment of controllable margin, aiming at uncertain information and dynamic characteristic during practical job-shop scheduling process. Strategy and methods of dynamic scheduling adjustment are presented, which respond to real-time event. In view of practical operation condition and accuracy demands, two methods are proposed. One is rescheduling that condenses domain, the other is scheduling updating based on adjustment. Such strategy obtains dynamic robust scheduling of FJSP to ensure the real-time of algorithm, continuity and stability of scheduling schemes.

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20

Zhuang, Shufeng, Zhendong Yin, Zhilu Wu, and Xiaoguang Chen. "Dynamic Relay Satellite Scheduling Based on ABC-TOPSIS Algorithm." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3161069.

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Tracking and Data Relay Satellite System (TDRSS) is a space-based telemetry, tracking, and command system, which represents a research field of the international communication. The issue of the dynamic relay satellite scheduling, which focuses on assigning time resource to user tasks, has been an important concern in the TDRSS system. In this paper, the focus of study is on the dynamic relay satellite scheduling, whose detailed process consists of two steps: the initial relay satellite scheduling and the selection of dynamic scheduling schemes. To solve the dynamic scheduling problem, a new scheduling algorithm ABC-TOPSIS is proposed, which combines artificial bee colony (ABC) and technique for order preference by similarity to ideal solution (TOPSIS). The artificial bee colony algorithm is performed to solve the initial relay satellite scheduling. In addition, the technique for order preference by similarity to ideal solution is adopted for the selection of dynamic scheduling schemes. Plenty of simulation results are presented. The simulation results demonstrate that the proposed method provides better performance in solving the dynamic relay satellite scheduling problem in the TDRSS system.

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21

Terekhov, D., T. T. Tran, D. G. Down, and J. C. Beck. "Integrating Queueing Theory and Scheduling for Dynamic Scheduling Problems." Journal of Artificial Intelligence Research 50 (July 22, 2014): 535–72. http://dx.doi.org/10.1613/jair.4278.

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Dynamic scheduling problems consist of both challenging combinatorics, as found in classical scheduling problems, and stochastics due to uncertainty about the arrival times, resource requirements, and processing times of jobs. To address these two challenges, we investigate the integration of queueing theory and scheduling. The former reasons about long-run stochastic system characteristics, whereas the latter typically deals with short-term combinatorics. We investigate two simple problems to isolate the core differences and potential synergies between the two approaches: a two-machine dynamic flowshop and a flexible queueing network. We show for the first time that stability, a fundamental characteristic in queueing theory, can be applied to approaches that periodically solve combinatorial scheduling problems. We empirically demonstrate that for a dynamic flowshop, the use of combinatorial reasoning has little impact on schedule quality beyond queueing approaches. In contrast, for the more complicated flexible queueing network, a novel algorithm that combines long-term guidance from queueing theory with short-term combinatorial decision making outperforms all other tested approaches. To our knowledge, this is the first time that such a hybrid of queueing theory and scheduling techniques has been proposed and evaluated.

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22

Li, Wei, Furong Tian, and Ke Li. "Fitness Distance Correlation Strategy for Solving the RGV Dynamic Scheduling Problem." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 3 (July 2020): 20–40. http://dx.doi.org/10.4018/ijcini.2020070102.

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Rail guide vehicle (RGV) problems have the characteristics of fast running, stable performance, and high automation. RGV dynamic scheduling has a great impact on the working efficiency of an entire automated warehouse. However, the relative intelligent optimization research of different workshop components for RGV dynamic scheduling problems are insufficient scheduling in the previous works. They appear idle when waiting, resulting in reduced operating efficiency during operation. This article proposes a new distance landscape strategy for the RGV dynamic scheduling problems. In order to solve the RGV dynamic scheduling problem more effectively, experiments are conducted based on the type of computer numerical controller (CNC) with two different procedures programming model in solving the RGV dynamic scheduling problems. The experiment results reveal that this new distance landscape strategy can provide promising results and solves the considered RGV dynamic scheduling problem effectively.

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23

Najam, Aaima, Faizan Ahmad, and Zeeshan Ahmed. "Dynamic-Model Assembly Line Scheduling." Research Journal of Applied Sciences, Engineering and Technology 6, no. 8 (July 10, 2013): 1446–49. http://dx.doi.org/10.19026/rjaset.6.3968.

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24

Farach-Colton, Martín, Katia Leal, Miguel A. Mosteiro, and Christopher Thraves Caro. "Dynamic Windows Scheduling with Reallocation." ACM Journal of Experimental Algorithmics 26 (July 8, 2021): 1–19. http://dx.doi.org/10.1145/3462208.

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We consider the Windows Scheduling (WS) problem, which is a restricted version of Unit-Fractions Bin Packing, and it is also called Inventory Replenishment in the context of Supply Chain. In brief, WS problem is to schedule the use of communication channels to clients. Each client c i is characterized by an active cycle and a window w i . During the period of time that any given client c i is active, there must be at least one transmission from c i scheduled in any w i consecutive time slots, but at most one transmission can be carried out in each channel per time slot. The goal is to minimize the number of channels used. We extend previous online models, where decisions are permanent, assuming that clients may be reallocated at some cost. We assume that such cost is a constant amount paid per reallocation. That is, we aim to minimize also the number of reallocations. We present three online reallocation algorithms for Windows Scheduling. We evaluate experimentally multiple variants of these protocols showing that, in practice, all three achieve constant amortized reallocations with close to optimal channel usage. Our simulations also expose interesting tradeoffs between reallocations and channel usage. We introduce a new objective function for WS with reallocations that can be also applied to models where reallocations are not possible. We analyze this metric for one of the algorithms that, to the best of our knowledge, is the first online WS protocol with theoretical guarantees that applies to scenarios where clients may leave and the analysis is against current load rather than peak load. Using previous results, we also observe bounds on channel usage for one of the algorithms.

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25

Rennie, R. M., and E. J. Jumper. "Dynamic Leading-Edge Flap Scheduling." Journal of Aircraft 34, no. 5 (September 1997): 606–11. http://dx.doi.org/10.2514/2.2236.

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26

Allahverdi, Ali, and G. M. Koole. "Stochastic Scheduling and Dynamic Programming." Journal of the Operational Research Society 47, no. 10 (October 1996): 1313. http://dx.doi.org/10.2307/3010046.

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27

Mauergauz, Yuri. "Dynamic group job shop scheduling." International Journal of Management Science and Engineering Management 10, no. 1 (September 9, 2014): 41–49. http://dx.doi.org/10.1080/17509653.2014.952360.

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28

Bolychevsky, A., C. R. Jesshope, and V. B. Muchnick. "Dynamic scheduling in RISC architectures." IEE Proceedings - Computers and Digital Techniques 143, no. 5 (1996): 309. http://dx.doi.org/10.1049/ip-cdt:19960788.

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29

Jin, Xin, Hongqiang Harry Liu, Rohan Gandhi, Srikanth Kandula, Ratul Mahajan, Ming Zhang, Jennifer Rexford, and Roger Wattenhofer. "Dynamic scheduling of network updates." ACM SIGCOMM Computer Communication Review 44, no. 4 (February 25, 2015): 539–50. http://dx.doi.org/10.1145/2740070.2626307.

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30

Allahverdi, Ali. "Stochastic Scheduling and Dynamic Programming." Journal of the Operational Research Society 47, no. 10 (October 1996): 1313–14. http://dx.doi.org/10.1057/jors.1996.161.

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31

Almeida,, Ana, Carlos Ramos,, and Silvio do Carmo Silva,. "Toward Dynamic Scheduling of Manufacturing." Journal for Manufacturing Science and Production 4, no. 3 (March 2002): 169–79. http://dx.doi.org/10.1515/ijmsp.2002.4.3.169.

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32

Liu, Zhen. "Dynamic scheduling of parallel computations." Theoretical Computer Science 246, no. 1-2 (September 2000): 239–52. http://dx.doi.org/10.1016/s0304-3975(99)00081-x.

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33

Celik, G., S. C. Borst, P. A. Whiting, and E. Modiano. "Dynamic scheduling with reconfiguration delays." Queueing Systems 83, no. 1-2 (January 23, 2016): 87–129. http://dx.doi.org/10.1007/s11134-016-9471-4.

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34

Bennell, Julia A., Mohammad Mesgarpour, and Chris N. Potts. "Dynamic scheduling of aircraft landings." European Journal of Operational Research 258, no. 1 (April 2017): 315–27. http://dx.doi.org/10.1016/j.ejor.2016.08.015.

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35

Thakur, Shivani, Rupinder Singh, and Sugandha Sharma. "Dynamic Capacity Scheduling in Hadoop." International Journal of Computer Applications 125, no. 15 (September 17, 2015): 25–28. http://dx.doi.org/10.5120/ijca2015906178.

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36

钟, 卓辉. "Intelligent RGV Dynamic Scheduling Strategy." Computer Science and Application 09, no. 01 (2019): 89–95. http://dx.doi.org/10.12677/csa.2019.91011.

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37

Allen, R. B., and S. G. Bridgeman. "Dynamic Programming in Hydropower Scheduling." Journal of Water Resources Planning and Management 112, no. 3 (July 1986): 339–53. http://dx.doi.org/10.1061/(asce)0733-9496(1986)112:3(339).

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38

WANG, KUEI YU, DAN C. MARINESCU, and OCTAVIAN F. CARBUNAR. "Dynamic scheduling of process groups." Concurrency: Practice and Experience 10, no. 4 (April 10, 1998): 265–83. http://dx.doi.org/10.1002/(sici)1096-9128(19980410)10:4<265::aid-cpe314>3.0.co;2-u.

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39

Feldmann, Anja, Jiří Sgall, and Shang-Hua Teng. "Dynamic scheduling on parallel machines." Theoretical Computer Science 130, no. 1 (August 1994): 49–72. http://dx.doi.org/10.1016/0304-3975(94)90152-x.

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40

Fang, Jie, and Hong Shan Xia. "Aircraft Dynamic Scheduling Algorithm Research Based on Heuristic Column Generation Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 2364–68. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2364.

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Aircraft dynamic scheduling affects the operation efficiency and flight benefits. Airlines make a rational organization of surplus aircraft to generate the best scheduling solution. The present research constructs an aircraft dynamic scheduling network diagram, define the surplus aircrafts available routings and create aircraft dynamic scheduling mathematical model. Through a mixture column generation algorithm with heuristic methods, the research find the routings in the optimal aircraft dynamic scheduling program. The given instance verifies the model and algorithm generate reasonable and practical solution for airlines in the effective time.

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41

Black, Gary W., Kenneth N. McKay, and Sherri L. Messimer. "Predictive, Stochastic and Dynamic Extensions to Aversion Dynamics Scheduling." Journal of Scheduling 7, no. 4 (July 2004): 277–92. http://dx.doi.org/10.1023/b:josh.0000031422.02125.97.

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42

颛, 孙盈. "Dynamic Scheduling Strategy Based on Dynamic Priority Algorithm." Computer Science and Application 09, no. 06 (2019): 1126–33. http://dx.doi.org/10.12677/csa.2019.96127.

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43

Miao, Weiwei, Zeng Zeng, Rui Zhang, and Zesheng Xi. "A Dynamic Scheduling Algorithm Based on Endogenous Security." Journal of Physics: Conference Series 2306, no. 1 (November 1, 2022): 012004. http://dx.doi.org/10.1088/1742-6596/2306/1/012004.

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Abstract Endogenous security enables the network to have its own immunity against various known and unknown attacks. The redundant executive scheduling strategy is an important link, which determines the security and reliability of the architecture. The existing scheduling strategy lacks consideration of scheduling time and the number of executors, and does not flexibly utilize attack feedback information. They are relatively simple and lack of security to a certain extent. Based on this, this paper proposes a dynamic scheduling algorithm by introducing the consideration of scheduling attributes and historical attack information. Firstly, a scheduling trigger based on the joint control of working time and abnormal feedback is designed. Then, the dynamic calculation formulas under different attack scenarios are constructed by using historical attack feedback information. Finally, a complete scheduling scheme is proposed. Experiments show that the dynamic performance of the algorithm is better than the fixed strategy. In addition, the algorithm improves the utilization of the actuator and achieves a good balance between reliability and efficiency.

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44

K.T., Vinod, S. Prabagaran, and O. A. Joseph. "Dynamic due date assignment method." Journal of Manufacturing Technology Management 30, no. 6 (October 21, 2019): 987–1003. http://dx.doi.org/10.1108/jmtm-06-2017-0112.

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Purpose The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness. Design/methodology/approach A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation. Findings The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop. Research limitations/implications Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines. Practical implications The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error. Originality/value Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

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45

Hikida, Takuma, Hiroki Nishikawa, and Hiroyuki Tomiyama. "Heuristic algorithms for dynamic scheduling of moldable tasks in multicore embedded systems." International Journal of Reconfigurable and Embedded Systems (IJRES) 10, no. 3 (November 1, 2021): 157. http://dx.doi.org/10.11591/ijres.v10.i3.pp157-167.

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Dynamic scheduling of parallel tasks is one of the efficient techniques to achieve high performance in multicore systems. Most existing algorithms for dynamic task scheduling assume that a task runs on one of the multiple cores or a fixed number of cores. Existing researches on dynamic task scheduling methods have evaluated their methods in different experimental environments and models. In this paper, the dynamic task scheduling methods are systematically rearranged and evaluated.

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46

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 (December 10, 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, including machine breakdown, worker sickness, and electricity power cut. Finally, generated reactive schedules are completed later than the baseline schedule.

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47

Sharma, Ashish, and Anjani Rai. "An Energy Efficient Multi-Objective Optimization based Dynamic Scheduling for Flexible Job Shop Scheduling Problem with Transportation Constraints." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (October 31, 2019): 1437–46. http://dx.doi.org/10.5373/jardcs/v11sp10/20192989.

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48

Stolyar, Alexander L. "Dynamic Distributed Scheduling in Random Access Networks." Journal of Applied Probability 45, no. 2 (June 2008): 297–313. http://dx.doi.org/10.1239/jap/1214950349.

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We consider a model of random access (slotted-aloha-type) communication networks of general topology. Assuming that network links receive exogenous arrivals of packets for transmission, we seek dynamic distributed random access strategies whose goal is to keep all network queues stable. We prove that two dynamic strategies, which we collectively call queue length based random access (QRA), ensure stability as long as the rates of exogenous arrival flows are within the network saturation rate region. The first strategy, QRA-I, can be viewed as a random-access-model counterpart of the max-weight scheduling rule, while the second strategy, QRA-II, is a counterpart of the exponential (EXP) rule. The two strategies induce different dynamics of the queues in the fluid scaling limit, which can be exploited for the quality-of-service control in applications.

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Stolyar, Alexander L. "Dynamic Distributed Scheduling in Random Access Networks." Journal of Applied Probability 45, no. 02 (June 2008): 297–313. http://dx.doi.org/10.1017/s0021900200004241.

Full text

Abstract:

We consider a model of random access (slotted-aloha-type) communication networks of general topology. Assuming that network links receive exogenous arrivals of packets for transmission, we seek dynamic distributed random access strategies whose goal is to keep all network queues stable. We prove that two dynamic strategies, which we collectively call queue length based random access (QRA), ensure stability as long as the rates of exogenous arrival flows are within the network saturation rate region. The first strategy, QRA-I, can be viewed as a random-access-model counterpart of the max-weight scheduling rule, while the second strategy, QRA-II, is a counterpart of the exponential (EXP) rule. The two strategies induce different dynamics of the queues in the fluid scaling limit, which can be exploited for the quality-of-service control in applications.

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50

Yang, Shengluo, Zhigang Xu, and Junyi Wang. "Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning." Sensors 21, no. 3 (February 2, 2021): 1019. http://dx.doi.org/10.3390/s21031019.

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Abstract:

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

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