A flexible manufacturing system (FMS) plays an important and central role in today's advanced manufacturing. It replaces human tasks (especially those that are highly dangerous ones), efficiently performed tasks, or crucially precise tasks. Considering the NP-hard nature of such computation when the numbers of parameters, robots, or/and tasks are increasing. The objective of this paper is to propose a super parallel computation method optimally to rearrange tasks of an FMS in a production line. A biological computing approach is presented to minimize the waiting time of machines and workstations, and maximize the usage of robots. Biological computing with powerful massive parallelism enables the generation of all feasible solutions at one time, as opposed to the limitation of conventional computing in reaching an optimal solution. The proposed method is illustrated using two different examples of single and multiple robots. Finally, solving an FMS problem is explained from a biological computing point of view.
BrowneJ.HarhenJ.ShivnanJ.Production management systems, 1996 (Addison-Wesley, New York).
2.
PaulliJ.A hierarchical approach for the FMS scheduling problem. Eur. J. Opl Res., 1995, 86(1), 32–42.
3.
LogendranR.SrikandarajahC.Sequencing of robot activities and parts in two-machines robotic cells. Int. J. Prod. Res., 1996, 34(12), 3447–3463.
4.
GragD. P.Coordinate robots in a flexible manufacturing work cell. In Proceedings of the IEEE/ASME International Conference on Advance intelligent mechatronics, Como, Italy, 2001, 8–12 July, pp. 633–648.
5.
Lopez-JuarezI.Corona-CastueraJ.Pena-CabreraM.Ordaz-HernandezK.On the design of intelligent robotic agents for assembly. Int. J. Inf. Sci., 2005, 171(4), 377–402.
6.
ChenH. G.GuerreroH. H.A rule-based robot scheduling system for flexible manufacturing cells. J. Intell. Mfg, 1996, 3(5), 285–296.
7.
HeZ. Y.HanY. Q.WangH. W.WangH. W.A modular neural network based on genetic algorithm for FMS reliability optimization. In Proceedings of International Conference on Machine learning and cybernetics, Xi'an, China, 2003, Vol. 3, pp. 1413–1418 (IEEE).
8.
KumarN.ShankerK.A genetic algorithm for FMS part type selection and machine loading. Int. J. Prod. Res., 2000, 38(16), 3861–3887.
9.
YangH. H.WuZ. M.The application of adaptive genetic algorithms in FMS dynamic rescheduling. Int. J. Computer Integrated Mfg, 2003, 16(6), 382–397.
10.
KamelE.BilesW.Fuzzy logic control of material flow in flexible manufacturing systems. In Proceedings of the 1997 IEEE International Symposium on Intelligent control, 1997, Istanbul, Turkey, pp. 209–214.
11.
KongY.GongX.WangJ.Real time scheduling for FMS based on fuzzy logic. In Proceedings of the First International Conference on Innovative computing, information and control, Beijing, China, 2006, Vol. 2, pp. 195–196 (IEEE).
12.
LowC.YipY.WuT. H.Modelling and heuristics of FMS scheduling with multiple objective. Computer Ops Res., 2006, 33(3), 674–694.
13.
MinH.-S.YihY.KimC.-O.A competitive neural network approach to multi-objective FMS scheduling. Int. J. Prod. Res., 1998, 36(7), 1749–1765.
14.
AdlemanL. M.Computing with DNA. Scientific Am., 1998, 61(August), 54–61.
15.
AdlemanL. M.Molecular computation of solutions to combinatorial problems. Science, 1994, 266(5187), 1021–1024.
16.
AiliH.ZhuD.DNA computing model for minimum spanning tree problem. In Proceedings of Eighth International Symposium on Symbolic and numeric algorithms for scientific computing, SYNAC '06, Timisoara, Romania, September 2006, pp. 372–377 (IEEE).
17.
AkbarimajdA.MehdizadehK.NekoiM. A.SabahiK.A modified DNA-computing algorithm to solve TSP. In Proceedings of 2006 IEEE International Conference on Mechatronic, Budapest, Hungary, July 2006, pp. 65–68 (IEEE).
18.
BakarR. A. B.WatadaJ.A bio-soft computing approach to re-arrange a flexible manufacturing robot. In Knowledge-based intelligent information and engineering systems, Lecture Notes in Computer Science (LNCS), 2007, pp. 308–315 (Springer-Verlag, New York).
19.
BakarR. A. B.WatadaJ.Biological computation of optimal task arrangement to multiple robot flexible machining cell. Int. J. Biomed. Soft Computing Human Sci., 2008, 12 (2).
20.
BakarR. A. B.WatadaJ.PedryczW.DNA approach to solve clustering problem based on a mutual order. Biosystems, 2008, 91(1), 1–12.
21.
InnisM. A.GelfandD. H.Optimization of PCRs. In PCR protocols: a guide to methods and applications (Eds GelfandD. H.InnisM. A.SninskyJ. J.WhiteT. J.), 1990, pp. 3–12 (Academic Press, New York).
22.
ItoY.FukusakiE.DNA as a nanomaterial. J. Molecular Catalysis — B: Enzymatic, 2004, 28(4-6), 155–166.
23.
JengD. J.-F.KimI.WatadaJ.Bio-Soft computing with fixed-lenght DNA to a group control optimization problem. Soft Computing, 2008, 12(3), 223–228.
24.
LeeJ. Y.ShinS.-Y.ParkT. H.ZhangB.-T.Solving travelling salesman problems with DNA molecules encoding numerical values. BioSystems, 2004, 78(1-3), 39–47.
25.
LiptonR. J.DNA solution of hard computational problem. Sciences, 1995, 268, 542–545.
26.
NarayananA.ZorbalasS.DNA algorithms for computing shortest paths. In Proceedings of the Genetic Programming, Wisconsin, USA, 1998, pp. 718–723 (Morgan-Kaufman).
27.
RozenbergG.SalomaaA.Watson-Crick complementary, universal computation and genetic engineering. Technical Report, Department of Computer Science, Leiden University, 1998, pp. 96–128.
28.
TsaftarisS. A.KatsaggelosA. K.PappasT. N.PopoutsakisE. T.How can DNA computing be applied to digital signal processing? Signal Processing Mag., IEEE, 2004, 21(6), 57–61.
29.
UdoF.SamS.WolfgangB.HilmarR.DNA sequence generator: A program for the construction of DNA sequences. In Proceedings of the Seventh International Workshop on DNA based computer, Tampa, USA, 2001, pp. 23–32 (Springer-Verlag).
30.
YinZ.CuiJ.YanY.MaY.Job shop scheduling problem based on DNA computing. J. Systems Engng Electronics, 2006, 17(3), 654–659.
31.
YamamotoM.MatsuuraN.ShibaT.OhuchiA.DNA solution of the shortest path problem by concentration control. In Proceedings of the 11th International Conference on Genome informatics, Tokyo, Japan, 18–19 December 2000, pp. 466–467.
32.
YangX.LuQ.LiC.LiaoX.Biological computation of the solution to the quadratic assignment problem. Appl. Math. Comput., 2008, 200(1), 369–377.
33.
DawandeM.GeismarH. N.SethiS. P.SrikandarajahC.Sequencing and scheduling in robotic cells: Recent development. J. Scheduling, 2005, 8(5), 387–426.
34.
Abdel-RahmanM. T.RoushdyE. H.Design and utilization of a low cost flexible manufacturing cell. In Proceedings of the 14th Annual Conference of the Industrial Electronics Society (IECON'88), 24–28 October 1988, Vol. 4, pp. 960–964.
35.
SrinoiP.ShayanE.GhotbF.A fuzzy logic modeling of dynamic scheduling in FMS. Int. J. Prod. Res., 2006, 44(11), 2183–2203.
36.
PrioreP.FuenteD.PuenteJ.ParrenoJ.A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engng Applic. Artific. Intelligence, 2006, 19(3), 247–255.
37.
RabeloL.YihY.JonesA.WitzgaliG.Intelligent FMS scheduling using modular neural networks. In Proceedings of the IEEE International Conference on Neural networks, San Francisco, USA, 1993, Vol. 3, pp. 1224–1229.
