|Table of Contents|

[1] Wang Ruihua, Fei Shumin, Liu Qingqing, et al. New dispatching rule in furniture production schedulingfor reducing weather impacts [J]. Journal of Southeast University (English Edition), 2016, 32 (3): 379-384. [doi:10.3969/j.issn.1003-7985.2016.03.020]
Copy

New dispatching rule in furniture production schedulingfor reducing weather impacts()
Share:

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
32
Issue:
2016 3
Page:
379-384
Research Field:
Mathematics, Physics, Mechanics
Publishing date:
2016-09-20

Info

Title:
New dispatching rule in furniture production schedulingfor reducing weather impacts
Author(s):
Wang Ruihua1 2 Fei Shumin1 Liu Qingqing3
1 School of Automation, Southeast University, Nanjing 210096, China
2 School of Automation Engineering, Qingdao University of Technology, Qingdao 266520, China
3 School of Information and Control, Nanjing University of Information and Technology, Nanjing 210044, China
Keywords:
dispatching rule fuzzy decision suitability relative humidity
PACS:
TB114.1
DOI:
10.3969/j.issn.1003-7985.2016.03.020
Abstract:
In order to reduce the possibility that quality problems occur resulting from “bad” weather, a new dispatching rule is designed for the job sequencing problem in the machine shop of a wood furniture factory. First, two indices including risky duration and risk magnitude are established to characterize the weather conditions. Based on these two indices, the job suitability under the future air state is derived by the fuzzy decision method, and integrated with a traditional heuristic to compute the dispatching priority of each job. Then, a new measure matching degree is constructed to evaluate the effectiveness of the dispatching rule. The greater the matching degree, the smaller the possibility that the quality problems of wood products occur. Finally, simulation experiments show that the dispatching rule can greatly increase the matching degree while maintaining low weighted tardiness.

References:

[1] Vollmann T E, Berry W L, Whybark D C. Manufacturing planning and control systems[M]. Homewood, IL, USA: Irwin, 1992: 1-20.
[2] Stoop P P M, Wiers V C S. The complexity of scheduling in practice[J]. International Journal of Operations & Production Management, 1996, 16(10): 37-53. DOI:10.1108/01443579610130682.
[3] Ouelhadj D, Petrovic S. A survey of dynamic scheduling in manufacturing systems[J]. Journal of Scheduling, 2009, 12(4): 417-431. DOI:10.1007/s10951-008-0090-8.
[4] Yin Y, Cheng S R, Cheng T C E, et al. Just-in-time scheduling with two competing agents on unrelated parallel machines[J]. Omega, 2016, 63: 41-47. DOI:10.1016/j.omega.2015.09.010.
[5] Yin Y, Wang Y, Cheng T C E, et al. Two-agent single-machine scheduling to minimize the batch delivery cost[J]. Computers & Industrial Engineering, 2016, 92: 16-30. DOI:10.1016/j.cie.2015.12.003.
[6] Morton T E, Rachamadugu R M. Myopic heuristics for the single machine weighted tardiness problem, Technical Report CMURI-TR-83-9[R]. Pittsburgh, USA:Graduate School of Industrial Administration, Carnegie-Mellon University, 1983.
[7] Kanet J J, Li X. A weighted modified due date rule for sequencing to minimize weighted tardiness[J]. Journal of Scheduling, 2004, 7(4): 261-276. DOI:10.1023/b:josh.0000031421.64487.95.
[8] He W, Sun D H. Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies[J]. The International Journal of Advanced Manufacturing Technology, 2013, 66(1): 501-514. DOI:10.1007/s00170-012-4344-4.
[9] Petrovic D, Duenas A. A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions[J]. Fuzzy Sets and Systems, 2006, 157(16): 2273-2285. DOI:10.1016/j.fss.2006.04.009.
[10] Duenas A, Petrovic D. An approach to predictive-reactive scheduling of parallel machines subject to disruptions[J]. Annals of Operations Research, 2008, 159(1): 65-82. DOI:10.1007/s10479-007-0280-3.
[11] Varas M, Maturana S, Pascual R, et al. Scheduling production for a sawmill: A robust optimization approach[J]. International Journal of Production Economics, 2014, 150: 37-51. DOI:10.1016/j.ijpe.2013.11.028.
[12] Fang K T, Lin B M T. Parallel-machine scheduling to minimize tardiness penalty and power cost[J]. Computers & Industrial Engineering, 2013, 64(1): 224-234. DOI:10.1016/j.cie.2012.10.002.
[13] Le C V, Pang C K. Fast reactive scheduling to minimize tardiness penalty and energy cost under power consumption uncertainties[J]. Computers & Industrial Engineering, 2013, 66(2): 406-417. DOI:10.1016/j.cie.2013.07.006.
[14] Li X, Ishii H, Chen M. Single machine parallel-batching scheduling problem with fuzzy due-date and fuzzy precedence relation[J]. International Journal of Production Research, 2014, 53(9): 2707-2717. DOI:10.1080/00207543.2014.975866.
[15] Yin Y, Cheng T C E, Yang X, et al. Two-agent single-machine scheduling with unrestricted due date assignment[J]. Computers & Industrial Engineering, 2015, 79: 148-155. DOI:10.1016/j.cie.2014.10.025.
[16] Aydilek A, Aydilek H, Allahverdi A. Production in a two-machine flowshop scheduling environment with uncertain processing and setup times to minimize makespan[J]. International Journal of Production Research, 2015, 53(9): 2803-2819. DOI:10.1080/00207543.2014.997403.
[17] Yin Y, Liu M, Hao J, et al. Single-machine scheduling with job-position-dependent learning and time-dependent deterioration[J]. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2012, 42(1): 192-200. DOI:10.1109/tsmca.2011.2147305.
[18] Wang R H, Fei S M. Rescheduling: External environment-related real-time events[J]. IFAC Proceedings Volumes, 2014, 47(3): 10743-10747. DOI:10.3182/20140824-6-za-1003.01792.

Memo

Memo:
Biography: Wang Ruihua(1985—), female, doctor, lecturer, 123.wrh@163.com.
Foundation item: The National Natural Science Foundation of China(No. 61273119).
Citation: Wang Ruihua, Fei Shumin, Liu Qingqing.New dispatching rule in furniture production scheduling for reducing weather impacts[J].Journal of Southeast University(English Edition), 2016, 32(3):379-384.DOI:10.3969/j.issn.1003-7985.2016.03.020.
Last Update: 2016-09-20