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Journal articles on the topic 'Flowshop Scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Choi, Seong Woo. "Heuristics for an m-Machine Re-Entrant Permutation Flowshop with the Objective of Total Tardiness." Applied Mechanics and Materials 752-753 (April 2015): 890–95. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.890.

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We focus on an m-machine re-entrant flowshop scheduling problem with the objective of minimizing total tardiness. In the re-entrant flowshop considered here, routes of all jobs are identical as in ordinary flowshops, but the jobs must be processed multiple times on the machines. We present heuristic algorithms, which are modified from well-known existing algorithms for the general m-machine flowshop problem or newly developed in this paper. For evaluation of the performance of the algorithms, computational experiments are performed on randomly generated test problems and results are reported.

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2

Rossi, Andrea, and Michele Lanzetta. "Nonpermutation flow line scheduling by ant colony optimization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 27, no. 4 (2013): 349–57. http://dx.doi.org/10.1017/s0890060413000176.

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AbstractA flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Comput

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3

Allahverdi, Ali, Tariq Aldowaisan, and Yuri N. Sotskov. "Two-machine flowshop scheduling problem to minimize makespan or total completion time with random and bounded setup times." International Journal of Mathematics and Mathematical Sciences 2003, no. 39 (2003): 2475–86. http://dx.doi.org/10.1155/s016117120321019x.

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This paper addresses the two-machine flowshop scheduling problem with separate setup times to minimize makespan or total completion time (TCT). Setup times are relaxed to be random variables rather than deterministic as commonly used in the OR literature. Moreover, distribution-free setup times are used where only the lower and upper bounds are given. Global and local dominance relations are developed for the considered flowshops and an illustrative numerical example is given.

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4

Frostig, E., and I. Adiri. "Stochastic flowshop no-wait scheduling." Journal of Applied Probability 22, no. 1 (1985): 240–46. http://dx.doi.org/10.2307/3213765.

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This paper deals with special cases of stochastic flowshop, no-wait, scheduling. n jobs have to be processed by m machines . The processing time of job Ji on machine Mj is an independent random variable Ti. It is possible to sequence the jobs so that , . At time 0 the realizations of the random variables Ti, (i are known. For m (m ≧ 2) machines it is proved that a special SEPT–LEPT sequence minimizes the expected schedule length; for two (m = 2) machines it is proved that the SEPT sequence minimizes the expected sum of completion times.

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5

Frostig, E., and I. Adiri. "Stochastic flowshop no-wait scheduling." Journal of Applied Probability 22, no. 01 (1985): 240–46. http://dx.doi.org/10.1017/s0021900200029181.

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This paper deals with special cases of stochastic flowshop, no-wait, scheduling. n jobs have to be processed by m machines . The processing time of job Ji on machine Mj is an independent random variable Ti . It is possible to sequence the jobs so that , . At time 0 the realizations of the random variables Ti , ( i are known. For m (m ≧ 2) machines it is proved that a special SEPT–LEPT sequence minimizes the expected schedule length; for two (m = 2) machines it is proved that the SEPT sequence minimizes the expected sum of completion times.

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6

Ho, Johnny C., and Jatinder N. D. Gupta. "Flowshop scheduling with dominant machines." Computers & Operations Research 22, no. 2 (1995): 237–46. http://dx.doi.org/10.1016/0305-0548(94)e0007-t.

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7

Randhawa, Sabah U., Cynthia Juwono, and Sheikh Burhanuddin. "Scheduling in Multistage Flowshop Systems." Industrial Management & Data Systems 94, no. 5 (1994): 16–24. http://dx.doi.org/10.1108/02635579410063270.

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8

Bernstein, M. "Finding heuristics for flowshop scheduling." ACM SIGART Bulletin, no. 99 (January 1987): 32–33. http://dx.doi.org/10.1145/24667.24670.

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9

Eren, Tamer, and Ertan Güner. "The tricriteria flowshop scheduling problem." International Journal of Advanced Manufacturing Technology 36, no. 11-12 (2007): 1210–20. http://dx.doi.org/10.1007/s00170-007-0931-1.

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10

Cheng, Chen-Yang, Shih-Wei Lin, Pourya Pourhejazy, Kuo-Ching Ying, and Yu-Zhe Lin. "No-Idle Flowshop Scheduling for Energy-Efficient Production: An Improved Optimization Framework." Mathematics 9, no. 12 (2021): 1335. http://dx.doi.org/10.3390/math9121335.

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Production environment in modern industries, like integrated circuits manufacturing, fiberglass processing, steelmaking, and ceramic frit, is characterized by zero idle-time between inbound and outbound jobs on every machine; this technical requirement improves energy efficiency, hence, has implications for cleaner production in other production situations. An exhaustive review of literature is first conducted to shed light on the development of no-idle flowshops. Considering the intractable nature of the problem, this research also develops an extended solution method for optimizing the Bi-ob

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11

Lebbar, Ghita, Abdellah El Barkany, and Abdelouahhab Jabri. "Scheduling Problems of Flexible Manufacturing Systems: Review, Classification and Opportunities." International Journal of Engineering Research in Africa 26 (October 2016): 142–60. http://dx.doi.org/10.4028/www.scientific.net/jera.26.142.

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This paper will relate initially to the scheduling characteristics of flexible manufacturing systems, and more specifically, the scheduling problems in flowshop and hybrid flowshop type systems representing interesting structures for the modeling of several problems resulting from the industrial world. Subsequently, we will focus our attention on the principal methods for solving scheduling problems, while presenting in the following the main published works for the aforementioned systems. Lastly, a comparative analysis will be carried out to highlight the fundamental ideas leading to the adop

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12

HU, XIAOFENG, JINGSONG BAO, and YE JIN. "A TABU SEARCH ALGORITHM FOR A PIPE-PROCESSING FLOWSHOP SCHEDULING PROBLEM MINIMIZING TOTAL TARDINESS IN A SHIPYARD." Asia-Pacific Journal of Operational Research 26, no. 06 (2009): 817–29. http://dx.doi.org/10.1142/s0217595909002493.

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This paper focuses on scheduling problem of a pipe-processing flowshop in a shipyard. The flowshop composes of five stages, including cutting, bending, welding preprocessing, argon-welding and CO 2-welding, and each stage consists of identical parallel machines. Since thousands of pipes are mounted on the hull block before erection, the pipe-processing scheduling is a critical task for shipbuilding to meet the due date of the block erection. A tabu search algorithm is developed for the scheduling problem with the objective of minimizing total tardiness. Computational experiments are performed

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13

CELANO, G., A. COSTA, and S. FICHERA. "AN EVOLUTIONARY ALGORITHM FOR PURE FUZZY FLOWSHOP SCHEDULING PROBLEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 06 (2003): 655–69. http://dx.doi.org/10.1142/s0218488503002466.

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The pure flowshop scheduling problem is here investigated from a perspective considering me uncertainty associated with the execution of shop floor activities. Being the flowshop problem is NP complete, a large number of heuristic algorithms have been proposed in literature to determine an optimal solution. Unfortunately, these algorithms usually assume a simplifying hypothesis: the problem data are assumed as deterministic, i.e. job processing times and the due dates are expressed through a unique value, which does not reflect the real process variability. For this reason, some authors have r

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14

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 dynami

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15

Bahari, M. Faudzi, Sudrajat Sudrajat, and E. Lesmana. "Minimize Rental Costs on Flowshop Scheduling." JURNAL ILMIAH SAINS 20, no. 2 (2020): 58. http://dx.doi.org/10.35799/jis.20.2.2020.27765.

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ABSTRACTThis paper discusses solving the problem of rental costs in a Flowshop scheduling with n-jobs and m-Machines in small and medium-sized companies to minimize rental costs to be paid. The proposed method is the development of an existing method using several proven propositions. The method used is applied in a sandals company that rents 11 machines used to produce 6 types of products. The proposed method yields a rental fee of IDR 1,071,121.5.Keywords: Flow shop; minimize rental cost; scheduling Minimisasi Biaya Sewa pada Penjadwalan FlowshopABSTRAKPaper ini membahas mengenai minimisasi

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16

Gupta, Jatinder N. D. "Two-Stage, Hybrid Flowshop Scheduling Problem." Journal of the Operational Research Society 39, no. 4 (1988): 359. http://dx.doi.org/10.2307/2582115.

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17

ISHIBUCHI, Hisao, Tadahiko MURATA, and Shigemitsu TOMIOKA. "Flowshop Scheduling by Genetic Local Search." Transactions of the Institute of Systems, Control and Information Engineers 10, no. 10 (1997): 563–65. http://dx.doi.org/10.5687/iscie.10.563.

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18

Čepek, Ondřej, Masanori Okada, and Milan Vlach. "Nonpreemptive flowshop scheduling with machine dominance." European Journal of Operational Research 139, no. 2 (2002): 245–61. http://dx.doi.org/10.1016/s0377-2217(01)00356-3.

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19

Koulamas, Christos, and George J. Kyparisis. "Concurrent flowshop scheduling to minimize makespan." European Journal of Operational Research 156, no. 2 (2004): 524–29. http://dx.doi.org/10.1016/s0377-2217(03)00142-5.

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20

Haouari, Mohamed, and Lotfi Hidri. "On the hybrid flowshop scheduling problem." International Journal of Production Economics 113, no. 1 (2008): 495–97. http://dx.doi.org/10.1016/j.ijpe.2007.10.007.

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21

Allahverdi, A., and M. Savsar. "Stochastic proportionate flowshop scheduling with setups." Computers & Industrial Engineering 39, no. 3-4 (2001): 357–69. http://dx.doi.org/10.1016/s0360-8352(01)00011-0.

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22

Yang, Dar-Li, and Maw-Sheng Chern. "Two-machine flowshop group scheduling problem." Computers & Operations Research 27, no. 10 (2000): 975–85. http://dx.doi.org/10.1016/s0305-0548(99)00070-2.

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23

Almasarwah, Najat, and Gürsel Süer. "Product Scheduling in a Flowshop Cell." Procedia Manufacturing 17 (2018): 206–13. http://dx.doi.org/10.1016/j.promfg.2018.10.038.

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24

Gupta, Jatinder N. D. "Two-Stage, Hybrid Flowshop Scheduling Problem." Journal of the Operational Research Society 39, no. 4 (1988): 359–64. http://dx.doi.org/10.1057/jors.1988.63.

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25

Cao, Dong, and Mingyuan Chen. "Parallel flowshop scheduling using Tabu search." International Journal of Production Research 41, no. 13 (2003): 3059–73. http://dx.doi.org/10.1080/0020754031000106443.

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26

Oladeinde, MH, AI Momodu, and CA Oladeinde. "Flowshop Scheduling Using a Network Approach." Nigerian Journal of Technology 34, no. 2 (2015): 286. http://dx.doi.org/10.4314/njt.v34i2.11.

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27

Dudek, R. A., S. S. Panwalkar, and M. L. Smith. "The Lessons of Flowshop Scheduling Research." Operations Research 40, no. 1 (1992): 7–13. http://dx.doi.org/10.1287/opre.40.1.7.

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28

Gupta, Jatinder N. D., Arindam Majumder, and Dipak Laha. "Flowshop scheduling with artificial neural networks." Journal of the Operational Research Society 71, no. 10 (2019): 1619–37. http://dx.doi.org/10.1080/01605682.2019.1621220.

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29

Lin, B. M. T., and T. C. E. Cheng. "Two-Machine Flowshop Batching and Scheduling." Annals of Operations Research 133, no. 1-4 (2005): 149–61. http://dx.doi.org/10.1007/s10479-004-5029-7.

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30

Murata, Tadahiko, Hisao Ishibuchi, and Hideo Tanaka. "Genetic algorithms for flowshop scheduling problems." Computers & Industrial Engineering 30, no. 4 (1996): 1061–71. http://dx.doi.org/10.1016/0360-8352(96)00053-8.

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31

Fan, B. Q., and T. C. E. Cheng. "Two-agent scheduling in a flowshop." European Journal of Operational Research 252, no. 2 (2016): 376–84. http://dx.doi.org/10.1016/j.ejor.2016.01.009.

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32

Naderi, B., and Rubén Ruiz. "The distributed permutation flowshop scheduling problem." Computers & Operations Research 37, no. 4 (2010): 754–68. http://dx.doi.org/10.1016/j.cor.2009.06.019.

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33

Gruzlikov, A. M., N. V. Kolesov, Iu M. Skorodumov, and M. V. Tolmacheva. "Using solvable classes in flowshop scheduling." International Journal of Advanced Manufacturing Technology 88, no. 5-8 (2016): 1535–46. http://dx.doi.org/10.1007/s00170-016-8828-5.

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34

Gupta, Jatinder N. D., and Edward F. Stafford. "Flowshop scheduling research after five decades." European Journal of Operational Research 169, no. 3 (2006): 699–711. http://dx.doi.org/10.1016/j.ejor.2005.02.001.

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35

Zhong, Weiya, and Zhi-Long Chen. "Flowshop scheduling with interstage job transportation." Journal of Scheduling 18, no. 4 (2014): 411–22. http://dx.doi.org/10.1007/s10951-014-0409-6.

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36

Behnamian, J., and S. M. T. Fatemi Ghomi. "Multi-objective fuzzy multiprocessor flowshop scheduling." Applied Soft Computing 21 (August 2014): 139–48. http://dx.doi.org/10.1016/j.asoc.2014.03.031.

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37

Gharbi, Anis, Mohamed Labidi, and Mohamed Aly Louly. "The Nonpermutation Flowshop Scheduling Problem: Adjustment and Bounding Procedures." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/273567.

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We consider the makespan minimization in a flowshop environment where the job sequence does not have to be the same for all the machines. Contrarily to the classical permutation flowshop scheduling problem, this strongly NP-hard problem received very scant attention in the literature. In this paper, some improved single-machine-based adjustment procedures are proposed, and a new two-machine-based one is introduced. Based on these adjustments, new lower and upper bounding schemes are derived. Our experimental analysis shows that the proposed procedures provide promising results.

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38

Tang, Dan, and Hong Ping Shu. "A Heuristic Algorithm for Flowshop Scheduling Problem." Advanced Materials Research 591-593 (November 2012): 626–30. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.626.

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Flow Shop Scheduling Problem is a class of scheduling problems with a work shop in which the flow control shall enable an appropriate sequencing for each job and for processing on a set of machines in compliance with given processing orders. In this paper, we propose a new heuristic algorithm based on the analysis and research of which problem, the new method introducing a evaluate mechanism of the relative position of any two jobs to the completion time, and the efficiency and performance has been improved .The result of simulation experiments shows that, our new heuristic algorithm has good

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39

Cao, Ying, Hua Xuan, and Jing Liu. "Dynamic Hybrid Flowshop Scheduling with Batching Production." Applied Mechanics and Materials 65 (June 2011): 562–67. http://dx.doi.org/10.4028/www.scientific.net/amm.65.562.

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Based on the background of steel-tube production, this paper presents the dynamic hybrid flowshop scheduling with batching scheduling at the first stage, and establishes an integer programming model for this problem. The objective is to minimize the total weighted completion time. The Lagrangian relaxation algorithm is constructed to slove the above model where dynamic programming is presented to solve batch-level subproblems, and a two-stage heuristic is presented to construct a feasible schedule. Testing results on small to medium problem sizes show that this method can obtain satisfactory q

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40

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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41

Narain, Laxmi, and P. C. Bagga. "Flowshop/no-idle scheduling to minimise the mean flowtime." ANZIAM Journal 47, no. 2 (2005): 265–75. http://dx.doi.org/10.1017/s1446181100010026.

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AbstractThis paper deals with n-job, 2-machine flowshop/mean flowtime scheduling problems working under a “no-idle” constraint, that is, when machines work continuously without idle intervals. A branch and bound technique has been developed to solve the problem.

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42

Liao, C. J., W. C. Yu, and C. B. Joe. "Bicriterion Scheduling in the Two-Machine Flowshop." Journal of the Operational Research Society 48, no. 9 (1997): 929. http://dx.doi.org/10.2307/3010492.

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43

Rajendran, Chandrasekharan. "Two-Stage Flowshop Scheduling Problem with Bicriteria." Journal of the Operational Research Society 43, no. 9 (1992): 871. http://dx.doi.org/10.2307/2583288.

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44

Sadjadi, S. J., M. B. Aryanezhad, and Mohsen Ziaee. "The General Flowshop Scheduling Problem: Mathematical Models." Journal of Applied Sciences 8, no. 17 (2008): 3032–37. http://dx.doi.org/10.3923/jas.2008.3032.3037.

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45

Lee, Chung-Yee. "Two-machine flowshop scheduling with availability constraints." European Journal of Operational Research 114, no. 2 (1999): 420–29. http://dx.doi.org/10.1016/s0377-2217(97)00452-9.

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46

ZHANG, Chang-Sheng, Ji-Gui SUN, Qing-Yun YANG, and Li-Hui ZHENG. "A Hybrid Algorithm for Flowshop Scheduling Problem." Acta Automatica Sinica 35, no. 3 (2009): 332–36. http://dx.doi.org/10.3724/sp.j.1004.2009.00332.

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47

Chou, Fuh-Der, and Ching-En Lee. "Two-machine flowshop scheduling with bicriteria problem." Computers & Industrial Engineering 36, no. 3 (1999): 549–64. http://dx.doi.org/10.1016/s0360-8352(99)00149-7.

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48

Koulamas, Christos, and George J. Kyparisis. "The three-stage assembly flowshop scheduling problem." Computers & Operations Research 28, no. 7 (2001): 689–704. http://dx.doi.org/10.1016/s0305-0548(00)00004-6.

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49

Rajendran, Chandrasekharan. "Two-Stage Flowshop Scheduling Problem with Bicriteria." Journal of the Operational Research Society 43, no. 9 (1993): 871–84. http://dx.doi.org/10.1057/jors.1992.126.

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50

Yang, W. H. "Group scheduling in a two-stage flowshop." Journal of the Operational Research Society 53, no. 12 (2002): 1367–73. http://dx.doi.org/10.1057/palgrave.jors.2601454.

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