Skip to main content
SpringerLink
Log in

A Review on Intelligent Systems in Research and Development

  • Chapter
  • First Online:
Intelligent Techniques in Engineering Management

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 87))

Abstract

Research and development (R&D) is one of the topics drawing great concern from researchers and practitioners. R&D is important for many of the areas such as pharmaceutical and medical industry, technology, and manufacturing. On the other hand, the sophisticated nature of the real life problems and its long computational time led researchers to develop intelligent systems. Intelligent systems have been used in many of the R&D areas since they give more reliable and consistent solutions when compared to the traditional solution techniques. In this pursuit, the chapter focuses on analyzing intelligent systems from a broader perspective considering a various research and development areas. For this aim, a comprehensive literature review from the years mainly between 2000 and 2014 is conducted. From the literature, it is observed that there are a large number of studies from 2010 to 2014. The motivation to conduct this chapter is to contribute to the literature by presenting an extensive literature review and making a synthesis with regard to intelligent systems in research and development .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahmad, F., Mat Isa, N.A., Hussain, Z., Osman, M.K.: Intelligent medical disease diagnosis using improved hybrid genetic algorithm—multilayer perceptron network. J. Med. Syst. 37, 9934 (2013)

    Article  Google Scholar 

  • Albritton, M.D., McMullen, P.R.: Optimal product design using a colony of virtual ants. Eur. J. Oper. Res. 176(1), 498–520 (2007)

    Article  MATH  Google Scholar 

  • Alcaraz, J., Maroto, C., Ruiz, R.: Solving the multi-mode resource-constrained project scheduling problem with genetic algorithms. J. Oper. Res. Soc. 54(6), 614–626 (2003)

    Article  MATH  Google Scholar 

  • Amini, A., Vaghefi, R.M., De La Garza, J.M., Buehrer, R.M.: Improving GPS-based vehicle positioning for intelligent transportation systems. In: Proceedings of IEEE Intelligent Vehicles Symposium, (2014), pp. 1023–1029

    Google Scholar 

  • Aparicio, F., De Buenaga, M., Rubio, M., Hernando, A.: An intelligent information access system assisting a case based learning methodology evaluated in higher education with medical students. Comput. Educ. 58, 1282–1295 (2012)

    Article  Google Scholar 

  • Australia Bureau of Statistics: Research and Experimental Development: Business enterprises, ABS 8104.0 (Canberra) (1996)

    Google Scholar 

  • Ballagi, Á., Kóczy, L.T., Gedeon, T.D.: Fuzzy communication and motion control by fuzzy signatures in intelligent mobile robots. Stud. Comp. Intell. 241, 147–1647 (2009)

    Article  Google Scholar 

  • Barros, H., Silva, A., Costa, E., Bittencourt, I.I., et al.: Steps, techniques, and technologies for the development of intelligent applications based on Semantic Web Services: A case study in e-learning systems. Eng. Appl. Artif. Intel. 24, 1355–1367 (2011)

    Article  Google Scholar 

  • Bielskis, A.A., Denisovas, V., Drungilas, D., et al.: Modelling of intelligent multi-agent based E-health care system for people with movement disabilities. Elektronika ir Elektrotechnika 6, 37–42 (2008)

    Google Scholar 

  • Bielskis, A.A., Denisovas, V., Drungilas, D., et al.: Pareto ant colony optimization based algorithm to solve maintenance and production scheduling problem in parallel machine case. In: Proceedings of International Conference on Computers and Industrial Engineering, CIE 2009, (2009) pp. 1210–1215

    Google Scholar 

  • Chaudhri, V.K., Gunning, D., Lane, H.C., Roschelle, J.: Intelligent learning technologies Part 2: applications of artificial intelligence to contemporary and emerging educational challenges. AI. Mag. 34(4), 10–12 (2013)

    Google Scholar 

  • Chen, L., Fang, Q.-S., Zhang, Z.-Y.: Research on the identification of temperature in intelligent building based on feed forward neural network and particle swarm optimization algorithm. In: Proceedings—2010 6th International Conference on Natural Computation ICNC 2010, vol. 4, pp. 1816–1820 (2010)

    Google Scholar 

  • Chen, S.-M., Griffis, F.H., Chen, P.-H., Chang, L.-M.: Simulation and analytical techniques for construction resource planning and scheduling. Automat. Constr. 21(1), 99–113 (2012)

    Article  Google Scholar 

  • Chu, W., Li, Y., Liu, C., Mou, W., Tang, L.: A manufacturing resource allocation method with knowledge-based fuzzy comprehensive evaluation for aircraft structural parts. Int. J. Prod. Res. 52(11), 3239–3258 (2014)

    Article  Google Scholar 

  • Cobo, M.J., Chiclana, F., Collop, A., et al.: A bibliometric analysis of the intelligent transportation systems research based on science mapping. IEEE Trans. Intell. Transp. 15(2), 901–908 (2014)

    Article  Google Scholar 

  • Crockett, K., Latham, A., Mclean, D., O’Shea, J.: A fuzzy model for predicting learning styles using behavioral cues in an conversational intelligent tutoring system. In: 2013 IEEE International Conference on Fuzzy Systems FUZZ-IEEE 2013, Hyderabad, India (2013)

    Google Scholar 

  • Deepak, B.B.V.L., Parhi, D.R.: Target seeking behaviour of an intelligent mobile robot using advanced particle swarm optimization. In: Presented on IEEE International Conference on Control, Automation, Robotics and Embedded Systems CARE 2013, Jabalpur, India, 16–18 Dec 2013

    Google Scholar 

  • Deo, D.S.: Applications of expert system/neural networks/genetic algorithms in an intelligent building complex. J. Inst. Eng. (India): Ar Eng. Div. 87, 4–9 (2006)

    Google Scholar 

  • Deskovski, S., Gacovski, Z.: Intelligent missile guidance system developed for training at Macedonian military academy. In: Proceedings on 16th Triennial World Congress of International Federation of Automatic Control IFAC 2005, vol. 16, pp. 1–6. Prague Czech Republic (2005)

    Google Scholar 

  • Dezfoulian, S.H., Wu, D., Ahmad, I.S.: A generalized neural network approach to mobile robot navigation and obstacle avoidance. Adv. Intel. Syst. Comput. 193, 25–42 (2012)

    Article  Google Scholar 

  • Djellal, F., Francoz, D., Gallouj, C., Galluuj, F., Jacquin, Y.: Revising the definition of research and development in the light of the specificities of services. Sci. Public Pol. 30(6), 415–429 (2003)

    Article  Google Scholar 

  • Ferreira, A.: Strategies of human-robot interaction for automatic microassembly. Proc. IEEE Int. Conf. Rob. Autom. 3, 3076–3081 (2003)

    Google Scholar 

  • Firoozi, Z., Ismail, N., Ariafar, S.H., Tang, S.H., Ariffin, M.K.M.A.: A genetic algorithm for solving supply chain network design model. AIP Conf. Proc. 1557, 211–214 (2013)

    Article  Google Scholar 

  • Frazzon, E.M., Israel, E., Albrecht, A., Pereira, C.E., Hellingrath, B.: Spare parts supply chains’ operational planning using technical condition information from intelligent maintenance systems. Annu. Rev. Control 38, 147–154 (2014)

    Article  Google Scholar 

  • Guo, Q., Xu, Q.-W.: Analysis and modeling of the central air-conditioning system in intelligent buildings. J. B Inst. Technol. 11(3), 295–297 (2002)

    MathSciNet  Google Scholar 

  • Haghighi, P.D., Burstein, F., Zaslavsky, A., Arbon, P.: Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings. Decis. Support. Syst. 54, 1192–1204 (2013)

    Article  Google Scholar 

  • Haider, K., Tweedale, J., Urlings, P., Jain, L.: Intelligent decision support system in defense maintenance methodologies. In: Proceedings of 2nd annual international conference on emerging techonologies ICET 2006, pp. 560–567. Peshawar, Pakistan (2006)

    Google Scholar 

  • He, J.: Study on the efficient design scheme of intelligent building. Appl. Mech. Mater. 484–485, 695–699 (2014)

    Article  Google Scholar 

  • He, W., Lu, T., Yu, C.Q.: A novel traffic flow forecasting method based on the artificial neural networks and intelligent transportation systems data mining. Adv. Mat. Res. 842, 708–711 (2014)

    Article  Google Scholar 

  • Hsu, C.-L., Tseng, K.C., Chuang, Y.-H.: A secure IRB system for assisting the development of intelligent medical devices. Expert Syst. Appl. 39, 12512–12521 (2012)

    Article  Google Scholar 

  • Hsu. C.-Y., Yang, C.-S., Yu, L.-C., et al.: Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system. Intern. J. Prod. Econ. http://dx.doi.org/10.1016/j.ijpe.2014.08.014. (2014)

  • Hu, L., Bi, X.: Education software design of electronic technique that based on virtual reality technology. Adv. Intel. Syst. Comput. 180, 173–178 (2013)

    Article  Google Scholar 

  • Huang, S., Yu, H.: Intelligent fault monitoring and diagnosis in electrical machines. Measurement 46(9), 3640–3646 (2013)

    Article  Google Scholar 

  • Hussein, H.M.A., Kumar, S.: A framework for development of an intelligent system for design and manufacturing of stamping dies. In: 27th International Conference on CADCAM, Robotics and Factories of the Future 2014; London (2014)

    Google Scholar 

  • Ichoua, S., Pechmann, A.: Production scheduling for sustainable manufacturing systems. Key Eng. Mat. 572(1), 235–238 (2014)

    Google Scholar 

  • Jespersen, S.T., Olsen, P.: Factors influencing the effectiveness of R&D efforts in the Nordic countries. TemaNord 553, Denmark (2007)

    Google Scholar 

  • Jia, Q., Seo, Y.: An improved particle swarm optimization for the resource-constrained project scheduling problem. Int. J. Adv. Manuf. Tech. 67(9–12), 2627–2638 (2013)

    Article  Google Scholar 

  • Jin, M., Ma, R., Yao, L., Ren, P.: An effective heuristic algorithm for robust supply chain network design under uncertainty. Appl. Math. Inf. Sci. 8(2), 819–826 (2014)

    Article  MathSciNet  Google Scholar 

  • Jong, W.-R., Lai, P.-J., Lo, C.-W.: Applying ant colony system algorithm in the navigation process for plastic injection mould manufacturing scheduling optimization. Int. J. Prod. Res. 52(9), 2530–2549 (2014)

    Article  Google Scholar 

  • Jung, S.W., Park, M.Y., Park, I.M., Jung, Y.K., Shin, H.B.: A robust control of intelligent mobile robot based on voice command. In: Presented on the 6th International Conference on Intelligent Robotics and Applications ICIRA 2013, pp. 25–28. Busan, South Korea, Sept 2013

    Google Scholar 

  • Kahraman, C., Kaya, I.: A fuzzy multiple attribute utility model for intelligent building assessment. J. Civ. Eng. Manag. 18(6), 811–820 (2012)

    Article  Google Scholar 

  • Kaplanoǧlu, V.: Multi-agent based approach for single machine scheduling with sequence-dependent setup times and machine maintenance. Appl. Soft Comput. 23, 165–179 (2014)

    Article  Google Scholar 

  • Krishnakumar, K.: Intelligent systems for aerospace engineering—an overview, NASA Technical Report, Document ID: 20030105746 (2003)

    Google Scholar 

  • Kuchta, D.: The use of fuzzy numbers in practical project planning and control. Stud. Fuzz. Soft. Comp. 254, 323–339 (2010)

    Article  MathSciNet  Google Scholar 

  • Kumar, M.: Medical diagnosis using generic intelligent agents. Commun. Comput. Inf. Sci. 125, 41–48 (2011)

    Article  Google Scholar 

  • Kwon, P.-J., Kim, H., Kim, U.: A study on the web-based intelligent self-diagnosis medical system. Adv. Eng. Softw. 40(6), 402–406 (2009)

    Article  MATH  Google Scholar 

  • Lee, H.-Y.: An integrated model for planning development projects using ACO and construction simulation. Civ. Eng. Environ. Syst. 28(4), 285–300 (2011)

    Article  Google Scholar 

  • Lee, H.C., Lee, J.M., Seo, J.H.: Design and improvement of product using intelligent function model based cost estimating. Expert Syst. Appl. 38, 3131–3141 (2011a)

    Article  MathSciNet  Google Scholar 

  • Lee, J., Ghaffari, M., Elmeligy, S.: Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems. Annu. Rev. Control 35(1), 111–122 (2011b)

    Article  Google Scholar 

  • Legardeur, J., Merlo, C., Fischer, X.: An integrated information system for product design assistance based on artificial intelligence and collaborative tools. Int. J P L Manage. 1(3), 211–229 (2006)

    Google Scholar 

  • Li, J., Zhang, Y.: Intelligent building automation and control based on IndasIBMS. In: Proceedings of International Conference on Service Science ICSS 2013, pp. 266–270. Shenzhen, China, 11–13 April 2013

    Google Scholar 

  • Li, W., Zhu, K.: Research on DCW-PSO algorithm and its application in intelligent transportation systems. In: Proceeding of 5th International Conference on Natural Computation, ICNC 2009, vol. 4, pp. 393–397 (2009)

    Google Scholar 

  • Li, X.P., Liu, F., Wang, X.J., Zhao, F.N.: Design and implementation of intelligent distance education platform. Adv. Mater. Res. 760–762, 1728–1732 (2013)

    Article  Google Scholar 

  • Liang, Z., Wakahara, Y.: Speed-volume relationship model for speed estimation on urban roads in intelligent transportation systems. Adv. Intel. Syst. Comput. 1089, 685–690 (2015)

    Article  Google Scholar 

  • Lin, C.-H., Yu, J.-L., Liu, J.-C., Lee, C.-J.: Genetic algorithm for shortest driving time in intelligent transportation systems. In: Proceedings—2008 International Conference on Multimedia and Ubiquitous Engineering, MUE 2008, pp. 402–406. (2008)

    Google Scholar 

  • Liu, L., Chen, L., Shi, C., Chen, H.: The study of collaborative learning grouping strategy. In: Intelligent Tutoring System, Proceedings of the 14th International Conference on Computer Supported Cooperative Work in Design CSCWD 2010, pp. 642–646 (2010)

    Google Scholar 

  • Lu, T.-F.: Intelligent systems techniques and their application in manufacturing systems. Expert Syst. 2, 381–410 (2002)

    Article  Google Scholar 

  • Lu, T.-F., Lin, G.C.: CAD, vision and sensor based intelligent robot server. Comput. Integr. Manuf. 9(2), 91–100 (1996)

    Article  Google Scholar 

  • Lu, X., Clements-Croome, D., Viljanen, M.: Past, present and future mathematical models for buildings: Focus on intelligent buildings (Part 1). Intell. Build. Intern. 1(1), 23–38 (2009)

    Article  MATH  Google Scholar 

  • Luo, J., Zhu, C., Cui, J., Liu, Z., Zhang, W.: Planning and scheduling for the air defense of warship (Guofang Keji Daxue Xuebao). /J. Natl. Univ. Defen. Technol. 35(2), 40–45 (2013)

    Google Scholar 

  • Luxton, D.D.: Recommendations for the ethical use and design of artificial intelligent care providers. Artif. Intell. Med. 62, 1–10 (2014)

    Article  Google Scholar 

  • Ma, J., Tian, F.: Intelligent learning ant colony algorithm. Appl. Mech. Mater. 48–49, 625–631 (2011)

    Article  Google Scholar 

  • Michaud, F.: Engineering education and the design of intelligent mobile robots for real use. Intell. Autom. Soft. Comput. 13(1), 19–28 (2007)

    Article  Google Scholar 

  • Micheletti, N., Kanevski, M., Bai, S., Wang, J., Hong, T.: Intelligent analysis of landslide data using machine learning algorithms. Landslide Sci. Pract. Spat. Anal. Model. 3, 161–167 (2013)

    Article  Google Scholar 

  • Moshtagh-Khorasani, M., Akbarzadeh-T, M.-R., Jahangiri, N., Khoobdel, M.: An intelligent system based on fuzzy probabilities for medical diagnosis—A study in aphasia diagnosis. J. Res. Med. Sci. 14(2), 89–103 (2009)

    Google Scholar 

  • Mousavi, S.M., Bahreininejad, A., Musa, S.N., Yusof, F.: A modified particle swarm optimization for solving the integrated location and inventory control problems in a two-echelon supply chain network. J. Intell. Manuf. (Article in press) (2014)

    Google Scholar 

  • Myneni, L.S., Narayanan, N.H., Rebello, S., Rouinfar, A., Pumtambekar, S.: An interactive and intelligent learning system for physics education. IEEE Trans. Learn. Technol. 6(3), 228–239 (2013)

    Article  Google Scholar 

  • Nazari-Shirkouhi, S., Keramati, A., Rezaie, K.: Improvement of customers’ satisfaction with new product design using an adaptive neuro-fuzzy inference systems approach. Neural Comput. Appl. 23, 333–343 (2013)

    Article  Google Scholar 

  • Noori, K., Jenab, K.: Fuzzy reliability-based traction control model for intelligent transportation systems. IEEE Trans. Syst. Man Cybern. A 43(1), 229–234 (2013)

    Article  Google Scholar 

  • OECD: Organisation for Economic Co-Operation and Development. Frascati manual: Proposed Standard Practice for Surveys on Research and Experimental Development, OECD Publishing, Paris, (2002)

    Google Scholar 

  • Oztemel, E., Kurt Tekez, E.: A general framework of a reference model for intelligent integrated manufacturing systems (REMIMS). Eng. Appl. Artif. Intell. 22, 855–864 (2009)

    Article  Google Scholar 

  • Petrisor, G.-M., Stanciu, L.: Application for robot motion planning. In: 9th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2014, pp. 97–100. Timisoara, Romania, (2014)

    Google Scholar 

  • Prasanth, R.S.S., Hans Raj, K.: Artificial bee colony algorithm integrated with differential evolution operators for product design and manufacturing optimization. Adv. Intel. Syst. C 202, 323–333 (2013a)

    Article  Google Scholar 

  • Prasanth, R.S.S., Hans Raj, K.: Artificial bee colony algorithm integrated with differential evolution operators for product design and manufacturing optimization. Adv. Intel. Syst. Comput. 202, 323–333 (2013b)

    Article  Google Scholar 

  • Puzis, R., Shirtz, D., Elovici, Y.: A particle swarm model for estimating reliability and scheduling system maintenance. Enterp. Inf. Syst. (Article in press) (2014)

    Google Scholar 

  • Ramík, D.M., Sabourin, C., Moreno, R., Madani, K.: A machine learning based intelligent vision system for autonomous object detection and recognition. Appl. Intell. 40(2), 358–375 (2014)

    Article  Google Scholar 

  • Relich, M., Muszynski, W.: The use of intelligent systems for planning and scheduling of product development projects. Procedia Comput. Sci. 35, 1586–1595 (2014)

    Article  Google Scholar 

  • Rudas, I.J., Fodor, J.: Intelligent Systems. Int. J. Comp. Commun. Control 3, 132–138 (2008)

    Google Scholar 

  • Sawatani Y, Arimoto T (2013) Value co-creation in R&D. Advances in the Human Side of Service Engineering. In: Spohrer JC, Freund L (ed) Taylor & Francis Group CRC Press, USA

    Google Scholar 

  • Schalkoff, R.J.: Intelligent Systems: Principles, Paradigms and Pragmatics. Jones and Bartlett Publishers, LLC, USA (2011)

    Google Scholar 

  • Shah, N., Kumar, S., Bastani, F., Yen, I.-L.: Optimization models for assessing the peak capacity utilization of intelligent transportation systems. Eur. J. Oper. Res. 16(1), 239–251 (2012)

    Article  MathSciNet  Google Scholar 

  • Shen, Z., Burnham, K.J., Smalov, L.: Optimised job-shop scheduling via genetic algorithm for a manufacturing production system. Adv. Intel. Syst. Comput. 1089, 89–92 (2015)

    Article  Google Scholar 

  • Shi, X.-M., Chen, C.-L., Shi, W.-H., Zhang, H.-Q.: Function planning of maintenance equipment based on genetic algorithm (Xi Tong Gong Cheng Yu Dian Zi Ji Shu). Syst. Eng. Electron. 36(7), 1346–1350 (2014)

    Google Scholar 

  • Singh, S., Galar, D., Baglee, D., Björling, S.-E.: Self-maintenance techniques: a smart approach towards self-maintenance system. Int. J. Syst. Assur. Eng. Manage. 5(1), 75–83 (2014)

    Article  Google Scholar 

  • Stankov, S., Rosic´, M., Zitko, B., Grubisic, A.: TEx-Sys model for building intelligent tutoring systems. Comput. Educ. 51, 1017–1036 (2008)

    Google Scholar 

  • Straub, J.: Intelligent water drops for aerospace and defense applications. In: Proceedings of SPIE—The International Society for Optical Engineering, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, vol. 9119. Baltimore USA, (2014)

    Google Scholar 

  • Subramanian, S., Anandhakumar, R., Ganesan, S.: Artificial bee colony based solution technique for generator maintenance scheduling. Aust. J. EE Eng. 9(2), 109–126 (2012)

    Google Scholar 

  • Tambouratzis, T., Karalekas, D., Moustakas, N.: A methodological study for optimizing material selection in sustainable product design. J. Ind. Ecol. 18(4), 508–516 (2014)

    Article  Google Scholar 

  • Taner, A.H., Brignell, J.E.: Virtual instrumentation and intelligent sensors. Sens. Actuators Phys. 61, 427–430 (1997)

    Article  Google Scholar 

  • Tang, L., Jing, K., He, J.: Supply chain scheduling optimization under networked manufacturing based on improved ant colony optimization algorithm (Xitong Gongcheng Lilun yu Shijian). Syst. Eng. Theor. Pract. 34(5), 1267–1275 (2014)

    Google Scholar 

  • Tangjitsitcharoen, S., Moriwaki, T.: Intelligent monitoring and identification of cutting states of chips and chatter on CNC turning machine. J. Manuf. Process. 10, 40–46 (2008)

    Article  Google Scholar 

  • Tilbury, J.B., Van Eetvelt, P.W.J., Garibaldi, J.M., et al.: Receiver operating characteristic analysis for intelligent medical systems—A new approach for finding confidence intervals. IEEE Trans. Bio-Med. Eng. 47(7), 952–963 (2000)

    Article  Google Scholar 

  • Vázquez, E., Ciurana, J., Rodríguez, C.A., et al.: Swarm intelligent selection and optimization of machining system parameters for microchannel fabrication in medical devices. Mater. Manuf. Process. 26(3), 403–414 (2011)

    Article  Google Scholar 

  • Viet, H.-H., Choi, S.-Y., Chung, T.-C.: Dyna-QUF: Dyna-Q based univector field navigation for autonomous mobile robots in unknown environments. J. Cent. S. Univ. 20(5), 1178–1188 (2013)

    Article  Google Scholar 

  • Volkov, A., Latyshev, K.: Intelligent and complex monitoring in intelligent buildings. Appl. Mech. Mater. 580–583, 3204–3207 (2014)

    Article  Google Scholar 

  • Wang, N.: Research on design of free combat attack and defense action based on wireless sensor motion recognition. Appl. Mech. Mater. 602–605, 2075–2078 (2014)

    Article  Google Scholar 

  • Wang, S., Liu, M.: Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method. Int. J. Prod. Res 52(5), 1495–1508 (2014)

    Article  Google Scholar 

  • Wang, M.Y., Ding, H.W., Liu, Q.L., Yang, Z.J.: The application of polling in intelligent transportation system. Appl. Mech. Mater. 602–605, 853–856 (2014a)

    Google Scholar 

  • Wang, X.N., Lin, Q., Chen, J.: Application of artificial intelligence in intelligent buildings. Appl. Mech. Mater. 347–350, 466–470 (2013)

    Google Scholar 

  • Wang, Y.-Y., Deng, G.-S., Ma, T., Li, L.: Robot path planning based on wave collision algorithm (Zhongbei Daxue Xuebao). J. N. Univ. China 35(3), 286–292 (2014b)

    Google Scholar 

  • Wang, Y.-Z., Zhou, L., Wang, Y., Xu, Y.: Research on patterns of unit behavior based on air defense equipment command task (Binggong Xuebao). Act. Armamentarii 35(4), 540–546 (2014c)

    MathSciNet  Google Scholar 

  • Wen, W., Wang, W.K., Wang, C.H.: A knowledge-based intelligent decision support system for national defense budget planning. Expert Syst. Appl. 28(1), 55–65 (2005)

    Article  Google Scholar 

  • Xu, J., Wei, P.: Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects. J. Ind. Manage. Optim. 9(1), 31–56 (2013)

    Google Scholar 

  • Yang, H.S., Chung, J., Ryu, B.S., Lee, J.: CAIR-2: Intelligent mobile robot for guidance and delivery. AI Mag. 17(1), 47–53 (1996)

    Google Scholar 

  • Yangm, S., Fu, W., Li, D., Wang, W.: Research on application of genetic algorithm for intelligent mobile robot navigation based on dynamic approach. In: Proceedings of 2007 IEEE International Conference on Automation and Logistics ICAL 2007, pp. 898–902. Jinan, China, (2007)

    Google Scholar 

  • Yavuz, H.: An integrated approach to the conceptual design and development of an intelligent autonomous mobile robot. Robot. Auton. Syst. 55, 498–512 (2007)

    Article  Google Scholar 

  • Yumin, L., Zhongyuan, Y.: Optimal designing of multi-channel WDM filter using intelligent particle swarm optimization algorithm. In: 6th International Conference Simulated Evolution and Learning, SEAL 2006, Hefei, vol. 4247, pp. 205–212 China, 15–18 Oct 2006

    Google Scholar 

  • Yuniarto, M.N., Labib, A.W.: Fuzzy adaptive preventive maintenance in a manufacturing control system: A step towards self-maintenance. Int. J. Prod. Res. 44(1), 159–180 (2006)

    Article  Google Scholar 

  • Zhang, R., Liu, X., Liu, L., Zhang, X.: Research on an intelligent manufacturing system for tokamak machine. J. Fusion Energy (Article in press) (2014a)

    Google Scholar 

  • Zhang, W., Pan, X.-H., Zhu, Y., Zhang, Y., Wu, Y.-B.: Manufacturing service open strategy based on intelligent single particle optimization (Jisuanji Jicheng Zhizao Xitong). Comput. Integr. Manuf. 17(9), 2079–2084 (2011a)

    Google Scholar 

  • Zhang, Y., Qin, J., Wang, Q., Chen, W., Zhang, H.-B.: Research on maintenance decision-making method of relay protection based on interval numbers grey fuzzy comprehensive evaluation (Dianli Xitong Baohu yu Kongzhi). Power Syst. Prot. C 42(4), 16–21 (2014b)

    Google Scholar 

  • Zhang, Z.Y., Cheng, H.M., Zhang, S.G.: An optimization model for the identification of temperature in intelligent building. J. Inform. Technol. Res. 4(2), 61–69 (2011b)

    Article  Google Scholar 

  • Zhou, Y., Jiang, J.: A parallel evolution based intelligent optimization algorithm to solve large-scale production planning problem in defense industry. J. Soft. 8(10), 2474–2481 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Engineering, Istanbul Technical University, Faculty of Management, 34367, Macka, Istanbul, Turkey

    İrem Otay

Authors
  1. İrem Otay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to İrem Otay .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Cengiz Kahraman

  2. Department of Industrial Engineering, Istanbul Technical University Management Faculty, Istanbul, Turkey

    Sezi Çevik Onar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Otay, İ. (2015). A Review on Intelligent Systems in Research and Development. In: Kahraman, C., Çevik Onar, S. (eds) Intelligent Techniques in Engineering Management. Intelligent Systems Reference Library, vol 87. Springer, Cham. https://doi.org/10.1007/978-3-319-17906-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-17906-3_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17905-6

  • Online ISBN: 978-3-319-17906-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation