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Journal articles on the topic 'Finite Capacity Scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Chotpradit, T., and P. Yenradee. "Finite Capacity Scheduling of Make-Pack Production: Case Study of Adhesive Factory." Journal of Engineering, Project, and Production Management 4, no. 2 (2014): 114–23. http://dx.doi.org/10.32738/jeppm.201407.0006.

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2

Martin, Rodney L. "Finite Capacity Scheduling - Large System Design." Advanced Materials Research 421 (December 2011): 607–16. http://dx.doi.org/10.4028/www.scientific.net/amr.421.607.

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This paper presents the design principles for a large finite capacity scheduling system which takes into account alternative tools and machines. The system will produce tightly packed and stable schedules which in many cases will be close to optimal. The system can then be used as a platform for research into the application of decision making techniques, mathematical optimization, and other modern techniques.

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3

Hall, Nicholas G., Marc E. Posner, and Chris N. Potts. "Scheduling with Finite Capacity Input Buffers." Operations Research 46, no. 3-supplement-3 (1998): S154—S159. http://dx.doi.org/10.1287/opre.46.3.s154.

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4

Hall, Nicholas G., Marc E. Posner, and Chris N. Potts. "Scheduling with Finite Capacity Output Buffers." Operations Research 46, no. 3-supplement-3 (1998): S84—S97. http://dx.doi.org/10.1287/opre.46.3.s84.

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5

Akkan, Can. "Overtime Scheduling: An Application in Finite-Capacity Real-Time Scheduling." Journal of the Operational Research Society 47, no. 9 (1996): 1137. http://dx.doi.org/10.2307/3010373.

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6

Akkan, Can. "Overtime Scheduling: An Application in Finite-capacity Real-time Scheduling." Journal of the Operational Research Society 47, no. 9 (1996): 1137–49. http://dx.doi.org/10.1057/jors.1996.142.

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7

Xuan, Hua. "Hybrid Flowshop Scheduling with Finite Transportation Capacity." Applied Mechanics and Materials 65 (June 2011): 574–78. http://dx.doi.org/10.4028/www.scientific.net/amm.65.574.

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This paper studies a class of hybrid flowshop scheduling problem characterized by release time, transportation time and transportation capacity of one unit for each transporter. This problem is formulated as an integer programming model and a Lagrangian relaxation algorithm is designed to solve it. Testing results on problems of small to medium sizes show that the Lagrangian relaxation algorithm can obtain an acceptable near optimal schedule within a shorter CPU time.

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8

Tardif, Valerie, and Mark L. Spearman. "Diagnostic scheduling in finite-capacity production environments." Computers & Industrial Engineering 32, no. 4 (1997): 867–78. http://dx.doi.org/10.1016/s0360-8352(97)00017-x.

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9

Liu, Kuo-Ching. "Dispatching rules for stochastic finite capacity scheduling." Computers & Industrial Engineering 35, no. 1-2 (1998): 113–16. http://dx.doi.org/10.1016/s0360-8352(98)00033-3.

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10

Martin *, Rodney L., and Christopher White. "Finite capacity scheduling—packed placement sequence schedule." International Journal of Production Research 43, no. 2 (2005): 405–46. http://dx.doi.org/10.1080/0020754042000264545.

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11

Xue, Dongyue, Robert Murawski, and Eylem Ekici. "Capacity Achieving Distributed Scheduling With Finite Buffers." IEEE/ACM Transactions on Networking 23, no. 2 (2015): 519–32. http://dx.doi.org/10.1109/tnet.2014.2303093.

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12

Akkan, Can. "Finite-capacity scheduling-based planning for revenue-based capacity management." European Journal of Operational Research 100, no. 1 (1997): 170–79. http://dx.doi.org/10.1016/s0377-2217(96)00021-5.

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13

Martin, Rodney L., and Christopher White. "Finite capacity scheduling using a predetermined placement sequence." International Journal of Production Research 41, no. 12 (2003): 2851–72. http://dx.doi.org/10.1080/0020754031000090603.

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14

Martin, R. L., and C. White. "Finite capacity scheduling with multiple tools and machines." International Journal of Production Research 42, no. 8 (2004): 1557–97. http://dx.doi.org/10.1080/00207540310001637588.

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15

Martin, Rodney L. "Finite capacity scheduling with mixed duration tooling trees." International Journal of Production Research 46, no. 6 (2008): 1539–65. http://dx.doi.org/10.1080/00207540600919365.

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16

HINO, Rei. "Job-shop Scheduling Problem with Finite Capacity Buffers." Proceedings of Manufacturing Systems Division Conference 2004 (2004): 29–30. http://dx.doi.org/10.1299/jsmemsd.2004.29.

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17

(Van) Enns, S. T. "Finite capacity scheduling systems: Performance issues and comparisons." Computers & Industrial Engineering 30, no. 4 (1996): 727–39. http://dx.doi.org/10.1016/0360-8352(96)00025-3.

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18

Horng, Sophea, and Pisal Yenradee. "Multi-Objectives Finite Capacity Scheduling of Make-and-Pack Production with Options to Adjust Processing Time." Journal of Engineering, Project, and Production Management 5, no. 1 (2015): 48–58. http://dx.doi.org/10.32738/jeppm.201501.0006.

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19

O'Donoghue, Diarmuid, Eoin Healy, and Humphrey Sorensen. "Development of a rule-based finite capacity scheduling system." International Journal of Production Economics 36, no. 2 (1994): 221–27. http://dx.doi.org/10.1016/0925-5273(94)90027-2.

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20

Benlogab, A., and B. Descotes-Genon. "Scheduling and Rescheduling in Flow Shops with Finite Capacity Storage." IFAC Proceedings Volumes 28, no. 10 (1995): 371–76. http://dx.doi.org/10.1016/s1474-6670(17)51546-3.

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21

Oyen, Mark P. Van, and Demosthenis Teneketzis. "Optimal Scheduling of a Finite Capacity Shuttle under Delayed Information." Probability in the Engineering and Informational Sciences 6, no. 1 (1992): 29–61. http://dx.doi.org/10.1017/s026996480000231x.

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We consider the optimal scheduling of a finite capacity shuttle in a two-node network with imperfect information. When shuttle trips do not depend on the number of passengers carried, we prove optimality and monotonicity of threshold policies. We derive conditions for dispatching that reduce the computational effort required to compute an optimal threshold policy. We prove a counter-example to the optimality of threshold policies for finite horizon problems where trip lengths increase stochastically in the number of passengers carried.

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22

Porter, Keith, David Little, John Kenworthy, and Peter Jarvis. "Finite capacity scheduling tools: observations of installations offer some lessons." Integrated Manufacturing Systems 7, no. 4 (1996): 34–38. http://dx.doi.org/10.1108/09576069610125085.

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23

Nagendra, P. B., and S. K. Das. "Finite capacity scheduling method for MRP with lot size restrictions." International Journal of Production Research 39, no. 8 (2001): 1603–23. http://dx.doi.org/10.1080/00207540010023600.

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24

Carvalho, Andréa N., Luiz Felipe Scavarda, and Leonardo J. Lustosa. "Implementing finite capacity production scheduling: lessons from a practical case." International Journal of Production Research 52, no. 4 (2013): 1215–30. http://dx.doi.org/10.1080/00207543.2013.848484.

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25

KIM, EUNGAB, and MARK P. VAN OYEN. "Finite-capacity multi-class production scheduling with set-up times." IIE Transactions 32, no. 9 (2000): 807–18. http://dx.doi.org/10.1080/07408170008967440.

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26

McCarthy, S. W., and K. D. Barber. "Medium to short term finite capacity scheduling: A planning methodology for capacity constrained workshops." Engineering Costs and Production Economics 19, no. 1-3 (1990): 189–99. http://dx.doi.org/10.1016/0167-188x(90)90042-g.

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27

Roy, R., and S. E. Meikle. "The Role of Discrete Event Simulation Techniques in Finite Capacity Scheduling." Journal of the Operational Research Society 46, no. 11 (1995): 1310. http://dx.doi.org/10.2307/2584566.

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28

Roy, R., and S. E. Meikle. "The Role of Discrete Event Simulation Techniques in Finite Capacity Scheduling." Journal of the Operational Research Society 46, no. 11 (1995): 1310–21. http://dx.doi.org/10.1057/jors.1995.180.

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29

Vanhoucke, M., and D. Debels. "A finite-capacity production scheduling procedure for a Belgian steel company." International Journal of Production Research 47, no. 3 (2008): 561–84. http://dx.doi.org/10.1080/00207540701441970.

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30

Poon, Chung Keung, and Wenci Yu. "On-Line Scheduling Algorithms for a Batch Machine with Finite Capacity." Journal of Combinatorial Optimization 9, no. 2 (2005): 167–86. http://dx.doi.org/10.1007/s10878-005-6855-5.

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31

Xiao-Feng, Yin, Chua Tay-Jin, Wang Feng-Yu, et al. "A rule-based heuristic finite capacity scheduling system for semiconductor backend assembly." International Journal of Computer Integrated Manufacturing 17, no. 8 (2004): 733–49. http://dx.doi.org/10.1080/0951192042000237537.

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32

Chen, Jen-Ming, and Liang-Tu Chen. "Pricing and production lot-size/scheduling with finite capacity for a deteriorating item over a finite horizon." Computers & Operations Research 32, no. 11 (2005): 2801–19. http://dx.doi.org/10.1016/j.cor.2004.04.005.

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33

Martin *, Rodney L. "Finite capacity scheduling with the placement sequence method, specification for a computer program." International Journal of Production Research 43, no. 3 (2005): 611–51. http://dx.doi.org/10.1080/0020754042000264572.

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34

Chen, J. M., and L. T. Chen. "Market-driven production lot-size and scheduling with finite capacity for a deteriorating item." Production Planning & Control 17, no. 1 (2006): 44–53. http://dx.doi.org/10.1080/09537280500363834.

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35

Ozel, O., Jing Yang, and S. Ulukus. "Optimal Broadcast Scheduling for an Energy Harvesting Rechargeable Transmitter with a Finite Capacity Battery." IEEE Transactions on Wireless Communications 11, no. 6 (2012): 2193–203. http://dx.doi.org/10.1109/twc.2012.032812.110813.

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36

Gavish, Bezalel, and Robert E. Johnson. "A Fully Polynomial Approximation Scheme for Single-Product Scheduling in a Finite Capacity Facility." Operations Research 38, no. 1 (1990): 70–83. http://dx.doi.org/10.1287/opre.38.1.70.

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37

Bondi, AndréB. "An analysis of finite capacity queues with priority scheduling and common or reserved waiting areas." Computers & Operations Research 16, no. 3 (1989): 217–33. http://dx.doi.org/10.1016/0305-0548(89)90023-3.

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38

Liu, Qijia, Long Wan, and Lijun Wei. "Online Scheduling on a Single Machine with Grouped Processing Times." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/805294.

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We consider the online scheduling problem on a single machine with the assumption that all jobs have their processing times in[p,(1+α)p], wherep>0andα=(5-1)/2. All jobs arrive over time, and each job and its processing time become known at its arrival time. The jobs should be first processed on a single machine and then delivered by a vehicle to some customer. When the capacity of the vehicle is infinite, we provide an online algorithm with the best competitive ratio of(5+1)/2. When the capacity of the vehicle is finite, that is, the vehicle can deliver at mostcjobs at a time, we provide an

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39

HINO, Rei. "2102 Job-shop Scheduling Problem with Finite Capacity Buffers A Case of Mixed-Integer Programming Model." Proceedings of Manufacturing Systems Division Conference 2005 (2005): 19–20. http://dx.doi.org/10.1299/jsmemsd.2005.19.

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40

Nagel, Everton, Ricardo Melo Czekster, Thais Webber, and César Augusto Missio Marcon. "A Framework Prototype for Multithreaded Implementation Over Micro-Controllers." Journal of Integrated Circuits and Systems 14, no. 1 (2019): 1–10. http://dx.doi.org/10.29292/jics.v14i1.39.

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Multithreading is pervasive in embedded system applications development. The applications requirements are becoming more rigorous, demanding the execution of concurrent tasks that must also take into account modularity and flexibility. An important part of the operating systems development concerns the implementation of scheduling algorithms. In an embedded system context, it is essential to consider that the scheduling algorithm heavily influences application behavior. Due to restricted and finite hardware resources, it is important to evaluate the use of flexible algorithms to guarantee effi

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41

Pongcharoen, P., C. Hicks, and P. M. Braiden. "The development of genetic algorithms for the finite capacity scheduling of complex products, with multiple levels of product structure." European Journal of Operational Research 152, no. 1 (2004): 215–25. http://dx.doi.org/10.1016/s0377-2217(02)00645-8.

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42

SUH, YOUNG SOO. "ESTIMATION ALGORITHM FOR SEQUENTIAL TRANSMISSION OF SENSOR DATA." Journal of Circuits, Systems and Computers 18, no. 08 (2009): 1493–504. http://dx.doi.org/10.1142/s0218126609005812.

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In this paper, a state estimation problem is considered, where sensor data are transmitted with finite communication capacity constraint. To reduce the sensor data transmission, only one sensor data is transmitted at each period among multiple sensors. We propose a dynamic scheduling algorithm, which is based on the difference between the current sensor data and the last transmitted sensor value. The proposed algorithm chooses sensor data, which have the largest contribution to the estimation performance. With the proposed algorithm, when a sensor data is chosen and transmitted, some informati

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43

Ogunwolu, Ladi, O. A. Alli, Chidi Onyedikam, and A. A. Sosimi. "Multi-Item Multi-Period Dynamic Capacity-Constrained Lot-Sizing Model with Parallel Machines and Fuzzy Demand." Advanced Materials Research 367 (October 2011): 627–38. http://dx.doi.org/10.4028/www.scientific.net/amr.367.627.

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Multi-item, multi-period production systems are prevalent in traditional production and distribution settings. A dynamic lot size production scheduling model (DLSPM) for multi-Production/inventory item multi-period production system with parallel machines is proposed in this paper. A mathematical framework that extends the DLSPM to multi-Production/inventory item-multi-period production planning constrained by storage space was built. The criteria of DLSPM explore optimal production schedule with the constraints of inventory, backlogs, production and demand to minimize the total inventory cost

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44

Zhao, Yuntao, Bo Bo, and Yongxin Feng. "A Scheduling Method of Cross-Layers Optimization of Polling Weight for AOS Multiplexing." International Journal of Digital Multimedia Broadcasting 2019 (March 24, 2019): 1–10. http://dx.doi.org/10.1155/2019/2560623.

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The core mechanism of Advanced Orbit System (AOS) mainly contains the packet channel multiplexing and the virtual channel multiplexing. The multiplexing efficiency and frame time directly affect the performance of the AOS and even the whole system. In this paper, in order to optimize AOS multiplexing performance, a scheduling method of cross-layers optimization of polling weight (CLOPW) is proposed. Different from single sublayer optimization such as the isochronous frame methods, the novel method focuses on factors related to AOS performance of two core sublayers, such as packet distribution,

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45

YOUNAS, MUHAMMAD, IRFAN AWAN, and KUO-MING CHAO. "NETWORK-CENTRIC STRATEGY FOR MOBILE TRANSACTIONS." Journal of Interconnection Networks 05, no. 03 (2004): 329–50. http://dx.doi.org/10.1142/s0219265904001209.

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Mobile transaction technology ensures reliability, data consistency, and concurrency in mobile computing applications. Current solutions develop numerous transaction models and protocols in order to enhance the reliability and improve the performance of mobile transactions. However, they give no attention to the underlying network on which mobile transactions are carried out. This paper presents a network-centric strategy in order to improve the performance of mobile transactions. The proposed strategy is based on employing preemptive resume scheduling mechanism in mobile transactions. A cost

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46

Wiers, Vincent C. S. "Finite capacity scheduling: management, selection, and implementation. Gerhard Plenert and Bill Kirchmier, Oliver Wight Manufacturing Publications (Wiley). ISBN 0-471-35264-0." Journal of Scheduling 4, no. 1 (2001): 68–70. http://dx.doi.org/10.1002/1099-1425(200101/02)4:1<68::aid-jos63>3.0.co;2-6.

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47

Grigoriu, Liliana. "Availability of a Hybrid FSO/RF Link While Using the Link’s Diversity for Packet Scheduling." Wireless Communications and Mobile Computing 2018 (May 31, 2018): 1–12. http://dx.doi.org/10.1155/2018/7320989.

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Hybrid-free space optical and radio frequency wireless links are a way of providing reliable transport of real-time traffic in outdoor wireless environments. We consider a link layer protocol that assigns packets to each physical channel of such a hybrid link, which first attempts to send each packet over one of the links (the main link) and, if unsuccessful, sends the packet over the other link (the backup link). The hybrid link processes high-priority traffic by using the link layer protocol and additional (background) traffic at low priority over the backup link. In this setting, high-prior

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48

Wang, Shenghe, Jinzhong Li, Yuguang Xie, Jiayu Bai, Bo Gao, and Shengwei Mei. "Online Dispatch of Shared Energy Storage with Remote Renewable Generations." E3S Web of Conferences 293 (2021): 02062. http://dx.doi.org/10.1051/e3sconf/202129302062.

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The sustained penetration of wind and solar generation is conducive to alleviating the energy crisis and environmental pollution. However, the finite capacity of transmission corridor limits the delivery of remote renewables and leads to curtailment. Energy storage plays an important role in renewables accommodation and improving equipment utilization, and shared energy storage can magnify the benefits through a temporal and spatial complementary. This paper proposes an online dispatch approach of energy storage shared by multiple renewable plants. The uncertainty and non-anticipativity issue

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49

Abdallah, Maissa, Nadine Abdallah, and Maryline Chetto. "Ensuring the sustainability of real-time embedded system under both QoS and Energy Constraints." MATEC Web of Conferences 281 (2019): 04006. http://dx.doi.org/10.1051/matecconf/201928104006.

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Nowadays, wireless sensor networks (WSNs) are more and more used in applications such as environment monitoring, healthcare monitoring, etc...The challenge in sensor networks is to ensure the sustainability of the system by guaranteeing the required performance level. However, with the limited capacity of finite power sources and the need of guaranteeing a long lifetime of those systems, it is suitable to use energy harvesting which allows to supply low-power electronic systems by converting ambient energy into electric power. Hence, our study is concerned with the problem of soft periodic and

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50

Ullah, Ihsan, Muhammad Babar Rasheed, Thamer Alquthami, and Shahzadi Tayyaba. "A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid." Sustainability 12, no. 1 (2019): 184. http://dx.doi.org/10.3390/su12010184.

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The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an en

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