Abstract
To overcome deficiency in the global capacity of a single dispatching rule, it is vital to select a dispatching rule in real time for dynamic scheduling. Among the studies addressing the method for selecting dispatching rules, few have no requirements for domain knowledge or accurate training example, which is hard to acquire from the real production system. In this paper, a new learning algorithm, along with the presentation of an adaptive scheduling control policy, is proposed to obtain the dynamic scheduling knowledge effectively, and different dispatching rules are selected to schedule the jobs in the machine buffer according to the current transient state of the system. Case studies are given to illustrate the validity of the scheduling control policy.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Devedzic V, Radovic D (1999) A framework for building intelligent manufacturing system. IEEE Trans Syst Man Cybern C 29(3):422–439
Holweg M (2007) The genealogy of lean production. J Oper Manag 25(2):38–42 doi:10.1016/j.jom.2006.04.001
Domico M, Saidler JM (2002) Clean, green manufacturing. Ceram Ind 152(2):54–56
Gunasekaran A (1999) Agile manufacturing: a framework for research and development. Int J Prod Econ 62(1/2):87–105 doi:10.1016/S0925-5273(98)00222-9
Jiang ZB, Fung RYK (2003) An adaptive agile manufacturing control infrastructure based on TOPNs-CS modelling. Int J Adv Manuf Technol 22(3/4):191–215 doi:10.1007/s00170-002-1459-z
Koufteros X, Vonderembse M, Doll W (2001) Concurrent engineering and its consequences. J Oper Manag 19(1):97–115 doi:10.1016/S0272-6963(00)00048-6
Qiang L, Zhang YF, Nee AYC (2001) A distributive and collaborative concurrent product design system through the WWW/Internet. Int J Adv Manuf Technol 17(5):315–322 doi:10.1007/s001700170165
Yan HS, Liu F (2001) Knowledgeable manufacturing system—a new kind of advanced manufacturing system. Comput Integr Manuf Syst 7(8):7–11 (in Chinese)
Yan HS (2006) A new complicated-knowledge representation approach based on knowledge meshes. IEEE Trans Knowl Data Eng 18(1):47–62 doi:10.1109/TKDE.2006.2
Yan HS, Dong H (2007) A new approach to modeling, control and simulation of knowledgeable manufacturing cell. Int J Prod Res 45(17):3779–3808 doi:10.1080/00207540500497082
Xue CG, Yan HS (2004) A study on self-reconfiguration of a knowledgeable manufacturing system. Proc Instit Mech Eng B J Eng Manuf 218(11):1601–1617
Zhang WJ, Freiheit T, Yang HS (2005) Dynamic scheduling in flexible assembly system based on timed Petri nets model. Robot Comput Integr Manuf 21(6):550–558 doi:10.1016/j.rcim.2004.12.002
Shnits B, Rubinovitz J, Sinreich D (2004) Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system. Int J Prod Res 42(17):3457–3472 doi:10.1080/00207540410001699444
Choi BK, You NK (2006) Dispatching rules for dynamic scheduling of one-of-a-kind production. Int J Comput Integr Manuf 19(4):383–392 doi:10.1080/09511920500407541
Diaz AR, Tchernykh A, Ecker KH (2003) Algorithms for dynamic scheduling of unit execution time tasks. Eur J Oper Res 146(2):403–416 doi:10.1016/S0377-2217(02)00236-9
Cowling PI, Ouelhadj D, Petrovic S (2004) Dynamic scheduling of steel casting and milling using multi-agents. Prod Plan Control 15(2):178–188 doi:10.1080/09537280410001662466
Zhou ZD, Wang HH, Chen YP, Ong SK, Fuh JYH, Nee AYC (2003) A multi-agent-based agile scheduling model for a virtual manufacturing environment. Int J Adv Manuf Technol 21(12):980–984 doi:10.1007/s00170-002-1420-1
Priore P, Fuente D, Pino R, Puente J (2003) Dynamic scheduling of flexible manufacturing systems using neural networks and inductive learning. Integr Manuf Syst 14(2):160–168 doi:10.1108/09576060310459456
Branke J, Mattfeld DC (2005) Anticipation and flexibility in dynamic scheduling. Int J Prod Res 43(15):3103–3129 doi:10.1080/00207540500077140
Chou FD, Chang PC, Wang HM (2006) A hybrid genetic algorithm to minimize makespan for the single batch machine dynamic scheduling problem. Int J Adv Manuf Technol 31(3/4):350–359 doi:10.1007/s00170-005-0194-7
Baker KR (1984) Sequencing rules and due-date assignments in a job shop. Manage Sci 30(9):1093–1104
Arzi Y, Iaroslavitz L (1999) Neural network-based adaptive production control system for a flexible manufacturing cell under a random environment. IIE Trans 31(3):217–230
Wan GH (1999) Fuzzy logic system for dynamic job shop scheduling. Proc IEEE Int Conf Syst Man Cybern 4:546–551
Piramuthu S, Shaw M, Fulkerson B (2000) Information-based dynamic manufacturing system scheduling. Int J Flex Manuf Syst 12(2/3):219–234 doi:10.1023/A:1008151831821
Koulamas C (1994) The total tardiness problem: review and extensions. Oper Res 42(6):1025–1041
Kim CO, Jun J, Baek JK, Smith RL, Kim YD (2005) Adaptive inventory control models for supply chain management. Int J Adv Manuf Technol 26(9/10):1184–1192 doi:10.1007/s00170-004-2069-8
McDonnell P, Joshi S, Qiu RG (2005) A learning approach to enhancing machine reconfiguration decision-making games in a heterarchical manufacturing environment. Int J Prod Res 43(20):4321–4334 doi:10.1080/00207540500142431
Theodoridis S, Koutroumbas K (2003) Pattern recognition, 2nd edn. Academic, San Diego, CA, USA
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, HB., Yan, HS. An adaptive approach to dynamic scheduling in knowledgeable manufacturing cell. Int J Adv Manuf Technol 42, 312–320 (2009). https://doi.org/10.1007/s00170-008-1588-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-008-1588-0