Abstract
If there is a continuous flow of production jobs for some machines, the problem of flow shop scheduling arises. As mentioned in many researches, the complexities of this problem are of exponential kind; therefore it is necessary to design less complex methods or algorithms for solving it. In this paper, a new solution is presented for this kind of scheduling problem by using the idea of neural networks. In fact, this research is a response to the need for solving large and complex problems of this type by non-classical methods. The purpose of the paper is to create an artificial intelligence for doing this kind of scheduling via the neural network training process. Here, the neural network has been trained by using training data obtained from optimal sequence of solved problems of flow shop scheduling. The trained network can provide a priority which shows the sequence of the job and will be very close to the optimal sequence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ashour, S.: Sequencing Theory. Springer, Berlin (1972)
Brown, A.P.G., Lomnicki, Z.A.: Some applications of the branch and bound algorithm to the machine scheduling problem. Operational Research Quarterly 17, 173–186 (1966)
Brucker, P.: Scheduling Algorithms. Springer, Berlin (1998)
Conway, R.L., Maxwell, W.L., Miller, L.W.: Theory of Scheduling. Addison Wesley, Reading (1967)
Dempster, M.A.H., Lenstra, J.K., Rinnooy Kan, A.H.G. (eds.): Deterministic and Stochastic Scheduling. Reidel, Dordrecht (1982)
Akyol, D.E.: Application of neural networks to heuristic scheduling algorithms. Computers & Industrial Engineering 46, 679–696 (2004)
El-Bouri, A., Balakrishnan, S., Popplewell, N.: Jobs on a single machine: A neural network approach. European Journal of Operational Research 126, 474–490 (2000)
El-Bouri, A., Balakrishnan, S., Popplewell, N.: A neural network to enhance local search in the permutation flowshop. Computers & Industrial Engineering 49, 182–196 (2005)
Framinan, J.M., Gupta, J.N.D., Leisten, R.: A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society 55, 1243–1255 (2004)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-completeness. W.H. Freeman and Company, San Francisco (1979)
Geismar, H.N., Dawande, M., Sriskandarajah, C.: Robotic cells with parallel machines: Throughput maximization in constant travel-time cells. Journal of Scheduling 7, 375–395 (2004)
Gupta, J.N.D.: Heuristic algorithms for multistage flowshop scheduling problem. AIIE Transactions 4, 11–18 (1972)
Gupta, J.N.D.: A review of flowshop scheduling research. In: Ritzman, L.P., Krajewski, L.J., Berry, W.L., Goodman, S.T., Hardy, S.T., Vitt, L.D. (eds.) Disaggregation Problems in Manufacturing and Service Organizations. Martinus Nijhoff, The Hague, pp. 363–388 (1979)
Ignall, E., Schrage, L.: Application of branch-and-bound technique to some flow shop problems. Operations Research 13, 400–412 (1965)
Gupta, J.N.D., Stafford Jr., E.F.: Flowshop scheduling research after five decades. European Journal of Operational Research 169, 699–711 (2006)
Johnson, S.M.: Optimal two- and three-stage production schedules with setup times included. Naval Research logistics Quarterly 1, 61–68 (1954)
Lomnicki, Z.A.: A branch and bound algorithm for the exact solution of the three machine scheduling problem. Operational Research Quarterly 16, 89–100 (1965)
McMahon, G.B., Burton, P.G.: Flowshop scheduling with the branch and bound method. Operations Research 15, 473–481 (1967)
Morton, T.E., Pentico, D.W.: Heuristic Scheduling Systems. Wiley, New York (1993)
Osman, I.H., Kelly, J.P.: Meta-heuristics: Theory and Applications. Kluwer Academic Publishers, Boston (1996)
Potts, C.N., Kovalyov, M.Y.: Scheduling with batching: A review. European Journal of Operational Research 120, 228–249 (2000)
Ruiz, R., Maroto, C., Alcaraz, J.: Solving the flowshop scheduling problem with sequence dependent setup times using advanced metaheuristics. European Journal of Operational Research 165, 34–54 (2005)
Sabuncuoglu, I., Gurgun, B.: A neural network model for scheduling problems. European Journal of Operational Research 93, 288–299 (1996)
Tanaev, V.S., Sotskov, Y.N., Strusevich, V.A.: Scheduling Theory: Multi-stage Systems. Kluwer Academic Publishers, Dordrecht (1994)
Wagner, H.M.: An integer linear-programming model for machine scheduling. Naval Research Logistics Quarterly 6, 131–140 (1959)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rouhani, S., Fathian, M., Jafari, M., Akhavan, P. (2010). Solving the Problem of Flow Shop Scheduling by Neural Network Approach. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-14306-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14305-2
Online ISBN: 978-3-642-14306-9
eBook Packages: Computer ScienceComputer Science (R0)