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Journal articles on the topic 'Just-in-time scheduling'

Author: Grafiati

Published: 25 May 2024

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1

Mahavishnu, V. C., A. N. Senthilvel, and S. Umamaheswari. "Bee Colony Optimization Solution to Single Machine Just in Time Scheduling Problem." International Journal of Future Computer and Communication 3, no. 6 (December 2014): 400–404. http://dx.doi.org/10.7763/ijfcc.2014.v3.336.

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2

Egbelu, Pius J., and Hsu Pin Wang. "Scheduling for just-in-time manufacturing." Engineering Costs and Production Economics 16, no. 2 (April 1989): 117–24. http://dx.doi.org/10.1016/0167-188x(89)90006-2.

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3

Monette, Jean-Noël, Yves Deville, and Pascal Van Hentenryck. "Just-In-Time Scheduling with Constraint Programming." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 241–48. http://dx.doi.org/10.1609/icaps.v19i1.13356.

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This paper considers Just-In-Time Job-Shop Scheduling, in which each activity has an earliness and a tardiness cost with respect to a due date. It proposes a constraint programming approach, which includes a novel filtering algorithm and dedicated heuristics. The filtering algorithm uses a machine relaxation to produce a lower bound that can be obtained by solving a Just-In-Time Pert problem. It also includes pruning rules which update the variable bounds and detect precedence constraints. The paper presents experimental results which demonstrate the effectiveness of the approach over a wide range of benchmarks.

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4

Vincent, T'kindt. "Multicriteria models for just-in-time scheduling." International Journal of Production Research 49, no. 11 (June 2011): 3191–209. http://dx.doi.org/10.1080/00207541003733783.

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5

McGarry, M. P., M. Reisslein, C. J. Colbourn, M. Maier, F. Aurzada, and M. Scheutzow. "Just-in-Time Scheduling for Multichannel EPONs." Journal of Lightwave Technology 26, no. 10 (May 2008): 1204–16. http://dx.doi.org/10.1109/jlt.2008.919366.

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6

Müller-Hannemann, Matthias, and Alexander Sonnikow. "Non-approximability of just-in-time scheduling." Journal of Scheduling 12, no. 5 (July 18, 2009): 555–62. http://dx.doi.org/10.1007/s10951-009-0120-1.

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7

Manoj, U. V., Jatinder N. D. Gupta, Sushil K. Gupta, and Chelliah Sriskandarajah. "Supply chain scheduling: Just-in-time environment." Annals of Operations Research 161, no. 1 (December 1, 2007): 53–86. http://dx.doi.org/10.1007/s10479-007-0290-1.

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8

Sourd, Francis. "Punctuality and idleness in just-in-time scheduling." European Journal of Operational Research 167, no. 3 (December 2005): 739–51. http://dx.doi.org/10.1016/j.ejor.2004.07.018.

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9

Shabtay, Dvir. "The just-in-time scheduling problem in a flow-shop scheduling system." European Journal of Operational Research 216, no. 3 (February 2012): 521–32. http://dx.doi.org/10.1016/j.ejor.2011.07.053.

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10

NAKAMURA, Yutaka, Yoshiaki KOHARA, Keiichi YAMAGATA, and Hiroyuki TAMURA. "Rule Based FMS Scheduling for Just-in-Time." Transactions of the Society of Instrument and Control Engineers 25, no. 2 (1989): 236–42. http://dx.doi.org/10.9746/sicetr1965.25.236.

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11

Zhang, Hongbin, Yu Yang, and Feng Wu. "Just-in-time single-batch-processing machine scheduling." Computers & Operations Research 140 (April 2022): 105675. http://dx.doi.org/10.1016/j.cor.2021.105675.

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12

Chung, Dae-Young, and Byung-Cheon Choi. "Just-in-time Scheduling with Multiple Competing Agents." Journal of the Korean Operations Research and Management Science Society 37, no. 1 (March 31, 2012): 19–28. http://dx.doi.org/10.7737/jkorms.2012.37.1.019.

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13

Mukhopadhyay, Samar K. "Optimal scheduling of just‐in‐time purchase deliveries." International Journal of Operations & Production Management 15, no. 9 (September 1995): 59–69. http://dx.doi.org/10.1108/01443579510099652.

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14

CHUNG, DAE-YOUNG, and BYUNG-CHEON CHOI. "JUST-IN-TIME SCHEDULING UNDER SCENARIO-BASED UNCERTAINTY." Asia-Pacific Journal of Operational Research 30, no. 02 (April 2013): 1250055. http://dx.doi.org/10.1142/s0217595912500558.

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This paper considers the single-machine scheduling problem, where job parameters are uncertain and the performance measure is to maximize the weighted number of just-in-time jobs, defined as jobs completed exactly on their due dates. Uncertainty is described through a finite set of well-defined scenarios. The criteria for this environment is to minimize the maximum deviation from optimality for all scenarios. We present the computational complexity results for several cases.

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15

Boysen, Nils, Dirk Briskorn, and Simon Emde. "Just-in-time vehicle scheduling with capacity constraints." IIE Transactions 48, no. 2 (June 29, 2015): 134–45. http://dx.doi.org/10.1080/0740817x.2015.1056390.

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16

Brauner, N., and Y. Crama. "The maximum deviation just-in-time scheduling problem." Discrete Applied Mathematics 134, no. 1-3 (January 2004): 25–50. http://dx.doi.org/10.1016/s0166-218x(03)00222-1.

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17

Agnetis, A., F. Nicolò, and M. Lucertini. "Just-in-time Scheduling in a Pipeline Manufacturing System." IFAC Proceedings Volumes 22, no. 14 (September 1989): 81–87. http://dx.doi.org/10.1016/s1474-6670(17)54330-x.

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18

Agosta, Giovanni, Stefano Crespi Reghizzi, Gerlando Falauto, and Martino Sykora. "JIST: Just-In-Time Scheduling Translation for Parallel Processors." Scientific Programming 13, no. 3 (2005): 239–53. http://dx.doi.org/10.1155/2005/127158.

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The application fields of bytecode virtual machines and VLIW processors overlap in the area of embedded and mobile systems, where the two technologies offer different benefits, namely high code portability, low power consumption and reduced hardware cost. Dynamic compilation makes it possible to bridge the gap between the two technologies, but special attention must be paid to software instruction scheduling, a must for the VLIW architectures. We have implemented JIST, a Virtual Machine and JIT compiler for Java Bytecode targeted to a VLIW processor. We show the impact of various optimizations on the performance of code compiled with JIST through the experimental study on a set of benchmark programs. We report significant speedups, and increments in the number of instructions issued per cycle up to 50% with respect to the non-scheduling version of the JITcompiler. Further optimizations are discussed.

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19

Nishi, Tatsushi, Akihiro Sakata, Shinji Hasebe, and Iori Hashimoto. "Autonomous decentralized scheduling system for just-in-time production." Computers & Chemical Engineering 24, no. 2-7 (July 2000): 345–51. http://dx.doi.org/10.1016/s0098-1354(00)00471-3.

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20

Álvarez-Pérez, G. A., J. L. González-Velarde, and J. W. Fowler. "Crossdocking— Just in Time scheduling: an alternative solution approach." Journal of the Operational Research Society 60, no. 4 (April 2009): 554–64. http://dx.doi.org/10.1057/palgrave.jors.2602590.

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21

Baptiste, Philippe, Marta Flamini, and Francis Sourd. "Lagrangian bounds for just-in-time job-shop scheduling." Computers & Operations Research 35, no. 3 (March 2008): 906–15. http://dx.doi.org/10.1016/j.cor.2006.05.009.

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22

Liu, Ke, Nael Abu-Ghazaleh, and Kyoung-Don Kang. "Exploiting slack time for just-in-time scheduling in wireless sensor networks." Real-Time Systems 45, no. 1-2 (April 28, 2010): 1–25. http://dx.doi.org/10.1007/s11241-010-9093-6.

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23

Chen, Junru, Wei Weng, Gang Rong, and Shigeru Fujimura. "Integrating Genetic Algorithm with Time Control for Just-In-Time Scheduling Problems." IFAC-PapersOnLine 48, no. 3 (2015): 893–97. http://dx.doi.org/10.1016/j.ifacol.2015.06.196.

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24

Choi, Byung-Cheon, Myoung-Ju Park, and Jibok Chung. "Two-Agent Scheduling with Preemptions and Just-in-Time Jobs." Journal of the Korean Operations Research and Management Science Society 44, no. 1 (February 28, 2019): 1–11. http://dx.doi.org/10.7737/jkorms.2019.44.1.001.

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25

MILTENBURG, JOHN, and GORDON SINNAMON. "Scheduling mixed-model multi-level just-in-time production systems." International Journal of Production Research 27, no. 9 (September 1989): 1487–509. http://dx.doi.org/10.1080/00207548908942636.

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26

AL-ASSERI, I., and M. HARlGA. "A simple lot-scheduling model for just-in-time manufacturing." International Journal of Production Research 33, no. 11 (November 1995): 3143–61. http://dx.doi.org/10.1080/00207549508904865.

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27

MILTENBURG, JOHN, and GORDON SINNAMON. "Algorithms for Scheduling Multi-Level Just-In-Time Production Systems." IIE Transactions 24, no. 2 (May 1992): 121–30. http://dx.doi.org/10.1080/07408179208964209.

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28

Hendel, Yann, Nina Runge, and Francis Sourd. "The one-machine just-in-time scheduling problem with preemption." Discrete Optimization 6, no. 1 (February 2009): 10–22. http://dx.doi.org/10.1016/j.disopt.2008.08.001.

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29

Choi, Byung-Cheon, Jibok Chung, and Myoung-Ju Park. "A just-in-time scheduling problem with two competing agents." Optimization Letters 14, no. 7 (October 15, 2019): 1869–86. http://dx.doi.org/10.1007/s11590-019-01494-x.

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30

Levner, E. V., and A. S. Nemirovsky. "A network flow algorithm for just-in-time project scheduling." European Journal of Operational Research 79, no. 2 (December 1994): 167–75. http://dx.doi.org/10.1016/0377-2217(94)90348-4.

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31

Qingxiang, Li, Zhao Xiaofei, He Yude, and Yin Shaojun. "INEH-VNS Algorithm Solved Automatic Production System Scheduling Problem under Just-in-Time Environment." Journal of Applied Mathematics 2023 (November 17, 2023): 1–17. http://dx.doi.org/10.1155/2023/6680897.

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Automatic production system scheduling problem under a just-in-time environment is researched in this paper. The automatic production system is composed of many tanks and one robotic, the tank of the researched problem is responsible for processing the job, and the robotic moves the job from one tank to the other tank. The difference between the researched problem and the classic shop scheduling problem is that the former must consider job scheduling and the robotic move sequence, but the latter considers only job scheduling. For optimizing simultaneously job scheduling and robotic move sequence in the proposed problem and minimizing total earliness/tardiness, an improved NEH (Nawaz-Enscore-Ham) and variable search (INEH-VNS) algorithm are developed. In the proposed method, firstly, to obtain initial solution, an improved NEH is shown. Secondly, for computing value of the objective function, the double procedure method is constructed. Thirdly, according to the properties of the proposed problem, three neighborhood structures, adjacent exchange, random insertion, and job exchange, are investigated. To test the performance of the INEH-VNS, 100 instances are randomly generated. When the run time is the same, compared with CPLEX 12.5, the INEH-VNS algorithm can find high-quality approximate optimal solution, a special big scale. Compared with the G-VNS algorithm, the average improvement rate of the approximate optimal solution is 45.9%, and the average stability rate of the INEH-VNS algorithm enhances 75.04%. That is to say, the INEH-VNS algorithm is outstanding and more effective.

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32

Zhou, Binghai, and Tao Peng. "Scheduling just-in-time part replenishment of the automobile assembly line with unrelated parallel machines." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 14 (March 26, 2019): 5113–30. http://dx.doi.org/10.1177/0954406219839083.

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With increasing product customization, just-in-time part replenishment has become a significant scheduling problem in the automobile assembly system. This paper investigates a new unrelated parallel machine scheduling problem of an assembly line, where machines are employed to deliver material boxes from an in-house warehouse to workstations. The schedule is to appropriately specify the assignment and sequence of material boxes on each machine for minimizing line-side inventories under no stock-out constraints. By taking advantages of domain properties, an exact algorithm is developed to cope up with small-scale instances. In terms of real-world scale instances, a hybrid teaching–learning-based optimization metaheuristic is established by integrating teaching–learning-based optimization with a beam search technique. Experimental results indicate that the scheduling algorithms are effective and efficient in solving the proposed unrelated parallel machine scheduling.

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33

Bozdotejko, Wojciech, Zdzisław Hejducki, Mariusz Uchroński, and Mieczysław Wodecki. "Scheduling of composite bridge components transportation in Just In Time system." IFAC Proceedings Volumes 46, no. 9 (2013): 2087–91. http://dx.doi.org/10.3182/20130619-3-ru-3018.00218.

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34

Emde, Simon. "Scheduling the replenishment of just-in-time supermarkets in assembly plants." OR Spectrum 39, no. 1 (July 15, 2016): 321–45. http://dx.doi.org/10.1007/s00291-016-0455-x.

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35

Laguna, Manuel, and Jos� Luis Gonz�lez Velarde. "A search heuristic for just-in-time scheduling in parallel machines." Journal of Intelligent Manufacturing 2, no. 4 (August 1991): 253–60. http://dx.doi.org/10.1007/bf01471113.

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36

Boncelet, Charles G., and David L. Mills. "A labeling algorithm for just-in-time scheduling in TDMA networks." ACM SIGCOMM Computer Communication Review 22, no. 4 (October 1992): 170–75. http://dx.doi.org/10.1145/144191.144269.

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37

Eren, Tamer. "Human and machine effects in a just-in-time scheduling problem." Human Factors and Ergonomics in Manufacturing 19, no. 4 (July 2009): 294–99. http://dx.doi.org/10.1002/hfm.20160.

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38

Chetty, Sivashan, and Aderemi O. Adewumi. "A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem." Discrete Dynamics in Nature and Society 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/350308.

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The Just-In-Time (JIT) scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA), is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS) and Simulated Annealing (SA). SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm.

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39

Kurniawan, Bobby, Ade Irman, Evi Febianti, K. Kulsum, Lely Herlina, Muhammad Adha Ilhami, Yusraini Muharni, and Fellek Getu Tadesse. "Solving Just-in-Time Single Machine Scheduling with Variable Discrete Speed Machine using Hybrid NSGA-II." Jurnal Teknik Industri 22, no. 2 (August 31, 2021): 211–23. http://dx.doi.org/10.22219/jtiumm.vol22.no2.211-223.

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Due to industrialization and population growth, increasing energy demand can lead to energy scarcity because non-renewable resources are primarily used as energy sources. In addition, carbon dioxide gas, the waste of industrialization, can harm the environment. Therefore, environmentally friendly methods are encouraged in the industrial environment as energy preservation and climate change mitigation. This research discusses just-in-time single machine scheduling that takes into account energy consumption. In this research, energy consumption depends on the machine’s speed. The objectives are minimizing the just-in-time (JIT) penalty (the sum of weighted earliness/tardiness) and energy consumption. This research proposed a hybrid NSGA-II with a local search to solve the multi-objective scheduling problem. Thus, solving the JIT single-machine scheduling problem considers energy consumption to conserve energy and increase production efficiency. Numerical experiments demonstrated that the hybrid NSGA-II with local search is more effective than the standard NSGA-II in solving the problem. Therefore, decision-makers can use the scheduling model to select alternative solutions that consider energy and the environment without sacrificing efficiency.

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40

Shabtay, Dvir, Omri Dover, and Moshe Kaspi. "Single-machine two-agent scheduling involving a just-in-time criterion." International Journal of Production Research 53, no. 9 (November 13, 2014): 2590–604. http://dx.doi.org/10.1080/00207543.2014.970706.

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41

Tedford, J. D., and C. Lowe. "Scheduling for just-in-time flexible manufacturing using adaptive fuzzy logic." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 213, no. 7 (July 1999): 741–45. http://dx.doi.org/10.1243/0954405991517164.

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42

MILTENBURG, JOHN, and GORDON SINNAMON. "Revisiting the mixed-model multi-level just-in-time scheduling problem." International Journal of Production Research 33, no. 7 (July 1995): 2049–52. http://dx.doi.org/10.1080/00207549508904798.

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43

LEE, M. K., and S. Y. KIM. "Scheduling of storage/retrieval orders under a just-in-time environment." International Journal of Production Research 33, no. 12 (December 1995): 3331–48. http://dx.doi.org/10.1080/00207549508904877.

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44

Emde, Simon, Hamid Abedinnia, and Christoph H. Glock. "Scheduling electric vehicles making milk-runs for just-in-time delivery." IISE Transactions 50, no. 11 (October 26, 2018): 1013–25. http://dx.doi.org/10.1080/24725854.2018.1479899.

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45

Choi, Byung-Cheon, and Jibok Chung. "Two-agent single-machine scheduling problem with just-in-time jobs." Theoretical Computer Science 543 (July 2014): 37–45. http://dx.doi.org/10.1016/j.tcs.2014.05.022.

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46

Boysen, Nils, and Stefan Bock. "Scheduling just-in-time part supply for mixed-model assembly lines." European Journal of Operational Research 211, no. 1 (May 2011): 15–25. http://dx.doi.org/10.1016/j.ejor.2010.10.029.

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47

Fliedner, Malte, Nils Boysen, and Armin Scholl. "Solving symmetric mixed-model multi-level just-in-time scheduling problems." Discrete Applied Mathematics 158, no. 3 (February 2010): 222–31. http://dx.doi.org/10.1016/j.dam.2009.09.013.

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48

Leyvand, Yaron, Dvir Shabtay, George Steiner, and Liron Yedidsion. "Just-in-time scheduling with controllable processing times on parallel machines." Journal of Combinatorial Optimization 19, no. 3 (November 14, 2009): 347–68. http://dx.doi.org/10.1007/s10878-009-9270-5.

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49

Gerstl, Enrique, and Gur Mosheiov. "Single machine just-in-time scheduling problems with two competing agents." Naval Research Logistics (NRL) 61, no. 1 (December 3, 2013): 1–16. http://dx.doi.org/10.1002/nav.21562.

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50

Hazır, Öncü, and Safia Kedad-Sidhoum. "Batch sizing and just-in-time scheduling with common due date." Annals of Operations Research 213, no. 1 (December 29, 2012): 187–202. http://dx.doi.org/10.1007/s10479-012-1289-9.

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