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Efficient immune algorithm for optimal allocations in series-parallel continuous manufacturing systems

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Abstract

This paper uses an immune algorithm (IA) meta-heuristic optimization method to solve the problem of structure optimization of series-parallel production systems. In the considered problem, redundant machines and buffers in process are included in order to attain a desirable level of availability. A procedure which determines the minimal cost system configuration is proposed. In this procedure, multiple choices of producing machines and buffers are allowed from a list of product available in the market. The elements of the system are characterized by their cost, estimated average up and down times, productivity rates and buffers capacities. The availability is defined as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. The proposed meta-heuristic is used as an optimization technique to seek for the optimal design configuration. The advantage of the proposed IA approach is that it allows machines and buffers with different parameters to be allocated.

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References

  • Beji, N., Jarboui, B., Eddaly, M., & Chabchoub, H. (2010). A hybrid particle swarm optimization algorithm for the redundancy allocation problem. Journal of Computational Science. doi:10.1016/j.jocs.2010.06.001.

  • Billiton R., Allan R. (1984) Reliability evaluation of power systems. Pitman, London

    Google Scholar 

  • Brownlee, J. (2007). Clonal selection algorithms, technical report. Victoria, Australia: Complex Intelligent Systems Laboratory (CIS), Centre for Information Technology Research (CITR), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology; 2007 Feb; Technical Report ID: 070209A.

  • Campelo F., Guimares F. G., Igarashi H., Ramirez J. A., Noguchi S. (2006) A modified immune network algorithm for multi-modal electromagnetic problems. IEEE Transactions on Magnetics 42(4): 1111–1114

    Article  Google Scholar 

  • Chen T. C. (2006) IAs based approach for reliability redundancy allocation problems. Applied Mathematics and Computation 182: 1556–1567

    Article  Google Scholar 

  • Chen T. C., You P. S. (2005) Immune algorithms-based approach for redundant reliability problems with multiple component choices. Computers in Industry 56(2): 195–205

    Article  Google Scholar 

  • Chern M. S. (1992) On the computational complexity of reliability redundancy allocation in a series system. Operations Research Letters 11: 309–315

    Article  Google Scholar 

  • Chu C. W., Lin M. D., Liu G. F., Sung Y. H. (2008) Application of immune algorithms on solving minimum-cost problem of water distribution network. Mathematical and Computer Modelling 48: 1888–1900

    Article  Google Scholar 

  • Coelho L. S. (2009) An efficient particle swarm approach for mixed-integer programming in reliability–Redundancy optimization applications. Reliability Engineering and System Safety 94: 830–837

    Article  Google Scholar 

  • Coello C. A. C., Cortes N. C. (2004) Hybridizing a genetic algorithm with an artificial immune system for global optimization. Engineering Optimization 36: 607–634

    Article  Google Scholar 

  • Danzhen, G., Qian, A., & Chen, C. (2008). The application of artificial immune network in load classification. In Third international conference on electric utility deregulation and restructuring and power technologies, 6–9 April, Nanjing, China (pp. 1394 – 1398).

  • Dasgupta D. (1999) Artificial immune systems and their applications. Springer, Berlin, Heidelberg

    Book  Google Scholar 

  • Dasgupta, D., KrishnaKumar, K., Wong, D., & Berry, M. (2004). Negative selection algorithm for aircraft fault detection. Artificial immune systems. In Proceedings of ICARIS 2004, Springer (pp. 1–14).

  • De Castro, L. N., & Timmis, J. (2002a). An artificial immune network for multimodal function optimization. In WCCI proceedings of the 2002 world on congress on computational intelligence (Vol. 1, pp. 699–704).

  • De Castro L. N., Timmis J. (2002b) Artificial immune systems: A new computational intelligence approach. Springer, London, Berlin, Heidelberg

    Google Scholar 

  • De Castro L. N., Timmis J. (2003) Artificial immune systems as a novel soft computing paradigm. Soft Computing 7: 526–544

    Article  Google Scholar 

  • De Castro, L. N., & Von Zuben, F. J. (2000). An evolutionary immune network for data clustering. In Proceedings of the IEEE SBRN (Brazilian Symposium on Artificial Neural Networks), Rio de Janeiro, 22–25 November (pp. 84–89).

  • De Castro L. N., Von Zuben F. J. (2002) Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3): 239–250

    Article  Google Scholar 

  • Dolgui A., Eremeev A. V., Sigaev V. S. (2007) HBBA: Hybrid algorithm for buffer allocation in tandem production lines. Journal of Intelligent Manufacturing 18: 411–420

    Article  Google Scholar 

  • Farmer J., Packard N. H., Perelson A. (1986) The immune system, adaptation and machine learning. Physica D 22: 187–204

    Article  Google Scholar 

  • Forrest, S., Perelson, A. S., Allen, L. & Cherukuri, R. (1994). Self–nonself discrimination in a computer. In Proceedings of IEEE symposium on research in security and privacy, Oakland, CA, Springer (pp. 202–212).

  • Gen M., Yun Y. S. (2006) Soft computing approach for reliability optimization: State-of-the-art survey. Reliability Engineering and System Safety 91(9): 1008–1026

    Article  Google Scholar 

  • Harmer P. K., Williams P. D., Gunsch G. H., Lamont G. B. (2002) An artificial immune system architecture for computer security applications. IEEE Transactions on Evolutionary Computation 6(3): 252–280

    Article  Google Scholar 

  • Harris R. S., Kong Q., Maizels N. (1996) Somatic hypermutation and the three R’s: Repair, replication and recombination. Mutation Research 436: 157–178

    Article  Google Scholar 

  • Hart E., Timmis J. (2008) Application areas of AIS: The past, the present and the future. Applied Soft Computing 8: 191–201

    Article  Google Scholar 

  • Helber S. (2001) Cash-flow-oriented buffer allocation in stochastic flow lines. International Journal of Production Research 39: 3061–3083

    Article  Google Scholar 

  • Hofmeyr S., Forrest S. (2000) Architecture of an artificial immune system. Evolutionary Computing 8(4): 443–473

    Article  Google Scholar 

  • Hou T.-H., Su C.-H., Chang H.-Z. (2008) An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits. Journal of Intelligent Manufacturing 19: 361–374

    Article  Google Scholar 

  • Huang S. J. (2002) Application of immune based optimization method for fault-section estimation in a distribution system. IEEE Transactions on Power Delivery 17: 779–784

    Article  Google Scholar 

  • Huang S. T. (2000) An immune-based optimization method to capacitor placement in a radial distribution system. IEEE Transactions on Power Delivery 15: 744–749

    Article  Google Scholar 

  • Hunter J. S. (1985) Statistical design applied to product design. Journal of Quality Technology 17: 210–221

    Google Scholar 

  • Jerne N. (1974) Towards a network theory of the immune system. Annuals of Immunology 125: 373–389

    Google Scholar 

  • Kimura T. (1996) Optimal buffer design of an M/G/s queue with finite capacity. Stochastic Models 12(1): 165–180

    Article  Google Scholar 

  • Kumar R., Izui K., Masataka Y., Nishiwaki S. (2008) Multilevel redundancy allocation optimization using hierarchical genetic algorithm. IEEE Transactions on Reliability 57(4): 650– 661

    Article  Google Scholar 

  • Kuo W., Prasad V. R. (2000) An annotated overview of system-reliability optimization. IEEE Transactions on Reliability 49(2): 176–187

    Article  Google Scholar 

  • Kuo W., Wan R. (2007) Recent advances in optimal reliability allocation. IEEE Transactions on Systems, Man and Cybernetics. Part A: Systems and Humans 37(2): 143–156

    Article  Google Scholar 

  • Laurentys C. A., Ronacher G., Palhares R. M., Caminhas W. M. (2010) Design of an artificial immune system for fault detection: A negative selection approach. Expert Systems with Applications 37(7): 5507–5513

    Article  Google Scholar 

  • Levitin G., Lisnianski A., Ben-Haim H., Elmakis D. (1998) Redundancy optimization for series–parallel multi-state systems. IEEE Transactions reliability 47(2): 165– 172

    Article  Google Scholar 

  • Levitin G., Lisniaski A., Elmakis D. (1997) Structure optimization of power system with different redundant elements. Electric Power System Research 43: 19–27

    Article  Google Scholar 

  • Levitin G., Meizin L. (2001) Structure optimization for continuous production systems with buffers under reliability constraints. International Journal of Production Economics 70: 77–87

    Article  Google Scholar 

  • Liang Y.C., Chen Y.C. (2007) Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm. Realiability Engineering and System Safety 92: 323–331

    Article  Google Scholar 

  • Lisnianski A., Ding Y. (2009) analysis for repairable multi-state system by using combined stochastic processes methods and universal generating function technique. Reliability Engineering and System Safety 94: 1788–1795

    Article  Google Scholar 

  • Lutz C. M., Davis K. R., Sun M. (1998) Determining buffer location and size in production line using tabu search. European Journal of Operational Research 106: 301–316

    Article  Google Scholar 

  • Maslow A. H. (1954) Motivation and personality. Harper & Bros, New York

    Google Scholar 

  • Massim Y., Yalaoui F., Amodeo L., Chatelet E., Zeblah A. (2010) Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization. Computers & Operations Research 37: 611–620

    Article  Google Scholar 

  • Massim Y., Zeblah A., Meziane R., Benguediab M., Ghouraf A. (2005) Optimal design and reliability evaluation of multi-state series–parallel power systems. Nonlinear Dynamics 40(4): 309–321

    Article  Google Scholar 

  • Matta, A., Pezzoni, M., & Semeraro, Q. (2010). A Kriging-based algorithm to optimize production systems approximated by analytical models. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0397-0.

  • Meizin L. K. (1984) Choosing the optimal buffer stock between two section of a continuous process. Automation and Remote control 45(8): 1086–1089

    Google Scholar 

  • Meizin L. K. (1987) Optimizing contingency reserve. Automation and Remote control 48(2): 268–271

    Google Scholar 

  • Miyamoto A., Nakamura H., Kruszka L. (2004) Application of the improved immune algorithm to structural design support system. Journal of Structural Engineering ASCE 130(1): 108–119

    Article  Google Scholar 

  • Moghaddam R. T., Safari J., Sassani F. (2008) Reliability optimization of series–parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliability Engineering and System Safety 93(4): 550–556

    Article  Google Scholar 

  • Nasaroui, O., Gonzalez, F., & Dasgupta, D., (2002). The fuzzy artificial immune system: Motivations, basic concepts, and application to clustering and web profiling. In Proceedings of the 2002 IEEE international conference on fuzzy systems (FUZZ-IEEE’02), Honolulu, HI, USA, 12–17 May, 1, (pp. 711–716).

  • Neal, M. (2003). Meta-stable memory in an artificial immune network. Artificial immune systems. In Procedings of ICARIS 2003, Springer (pp. 168–181).

  • Nourelfath M., Dutuit Y. (2004) A combined approach to solve the redundancy optimization problem for multi-state systems under repair policies. Reliability Engineering and System Safety 86: 205–213

    Article  Google Scholar 

  • Nourelfath, M., Nahas. N., & Zeblah. A. (2003). An ant colony approach to redundancy optimization for multi-state system. In International conference on industrial engineering and product management (IEPM’2003) Porto-Portugal.

  • Ouzineb M., Nourelfath M., Gendreau M. (2010) An efficient heuristic for reliability design optimization problems. Computers & Operations Research 37: 223–235

    Article  Google Scholar 

  • Plett, E., & Das, S. (2009). A new algorithm based on negative selection and idiotypic networks for generating parsimonious detector sets for industrial fault detection applications. Lecture notes in computer science, 5666/2009, pp. 288–300.

  • Ramirez-Marquez J. E., Coit D. (2007) Optimization of system reliability in the presence of common cause failures. Reliability Engineering & System Safety 92(10): 1421–1434

    Article  Google Scholar 

  • Saniee Abadeh, M., Habibi, J., Daneshi, M., Jalali, M., & Khezrzadeh, M. (2007). Intrusion detection using a hybridization of evolutionary fuzzy systems and artificial immune systems. In Proceedings of congress on evolutionary computation (CEC), 25–28 September, Singapore.

  • Taguchi G., Chowdhury S., Taguchi S. (2000) Robust engineering—Learn how to boost quality while reducing costs and time to market. McGraw-Hill, New York

    Google Scholar 

  • Tian Z., Levitin G., Zuo M. J. (2009) A joint reliability–redundancy optimization approach for multi-state series-parallel systems. Reliability Engineering and System Safety 94: 1568–1576

    Article  Google Scholar 

  • Tiwari M. K., Prakash Kumar A., Mileham A. R. (2005) Determination of optimal assembly sequence using psycho-clonal algorithm. (IMechE), Part-B, Journal of Engineering Manufacture 219: 137–149

    Article  Google Scholar 

  • Tiwari M. K., Raghavendra N., Agrawal S., Goyal S. K. (2010) A hybrid Taguchi-Immune approach to optimize an integrated supply chain design problem with multiple shipping. European Journal of Operational Research 203(1): 95–106

    Article  Google Scholar 

  • Ülker E., Turanalp M. E., Halkaci H. S. (2009) An artificial immune system approach to CNC tool path generation. Journal of Intelligent Manufacturing 20: 67–77

    Article  Google Scholar 

  • Ushakov I. (1986) Universal generating function. Soviet Journal of Computer Systems and Science 24(5): 118–129

    Google Scholar 

  • Ushakov I. (1987) Optimal standby problems and a universal generating function. Soviet Journal of Computer Systems and Science 25(4): 79–82

    Google Scholar 

  • Wang X., Gao X. Z., Ovaska S. J. (2007) A hybrid optimization algorithm based on ant colony and immune principles. International Journal of Computer Science & Applications 4(3): 30–44

    Google Scholar 

  • Wen X., Song A. (2004) An immune evolutionary algorithm for sphericity error evaluation. International Journal of Machine Tools and Manufactures 44(10): 1077–1084

    Article  Google Scholar 

  • Yalaoui A., Châtelet E., Chu C. (2005) A new dynamic programming method for reliability & redundancy allocation in a parallel-series system. IEEE Transactions on Reliability 54(2): 254–261

    Article  Google Scholar 

  • Yan, H. S., An, Y. W., & Shi, W. W. (2010). A new bottleneck detecting approach to productivity improvement of knowledgeable manufacturing system. Journal of Intelligent Manufacturing. doi:10.1007/s10845-009-0244-3.

  • Yildiz A. R. (2009) A novel particle swarm optimization approach for product design and manufacturing. International Journal of Advanced Manufacturing Technology 40: 617–628

    Article  Google Scholar 

  • Zandieh M., Fatemi Ghomi S. M. T., Moattar Husseini S. M. (2006) An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times. Applied Mathematics and Computation 180(6): 111–127

    Article  Google Scholar 

  • Zhang R., Wu C. (2010) Hybrid immune simulated annealing algorithm for the job shop scheduling problem. Applied Soft Computing 10(1): 79–89

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Sidi Bel Abbes, BP 89, Gumbeta, 22000, Sidi Bel Abbes, Algeria

    Y. Massim & A. Zeblah

  2. Institut Charles Delaunay (CNRS FRE 2848), {LOSI}, University of Technology of Troyes, Troyes, 12 rue Marie Curie, Troyes Cedex, France

    F. Yalaoui & A. Yalaoui

  3. Institut Charles Delaunay (CNRS FRE 2848), {LM2S}, University of Technology of Troyes, Troyes, 12 rue Marie Curie, Troyes Cedex, France

    E. Chatelet

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  1. Y. Massim
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  2. F. Yalaoui
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  4. A. Yalaoui
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Correspondence to Y. Massim.

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Massim, Y., Yalaoui, F., Chatelet, E. et al. Efficient immune algorithm for optimal allocations in series-parallel continuous manufacturing systems. J Intell Manuf 23, 1603–1619 (2012). https://doi.org/10.1007/s10845-010-0463-7

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  • DOI: https://doi.org/10.1007/s10845-010-0463-7

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