A Review on the Application of Hybrid Artificial Intelligence Systems to Optimization Problems in Operations Management

The use of hybrid artificial intelligence systems in operations management has grown during the last years given their ability to tackle combinatorial and NP hard problems. Furthermore, operations management problems usually involve imprecision, uncertainty, vagueness, and high-dimensionality. This paper examines recent developments in the field of hybrid artificial intelligence systems for those operations management problems where hybrid approaches are more representative: design engineering, process planning, assembly line balancing, and dynamic scheduling.

[1]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[2]  Armin Scholl,et al.  A survey on problems and methods in generalized assembly line balancing , 2006, Eur. J. Oper. Res..

[3]  Yasuhiro Hayashi,et al.  Scenario creation method by genetic algorithm for evaluating future plan , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[4]  A. Márkus,et al.  Process planning with genetic algorithms on results of knowledge-based reasoning , 1996 .

[5]  A. Noorul Haq,et al.  A hybrid genetic algorithm approach to mixed-model assembly line balancing , 2006 .

[6]  D. Puangdownreong,et al.  A new hybrid intelligent method for assembly line balancing , 2007, 2007 IEEE International Conference on Industrial Engineering and Engineering Management.

[7]  Francisco J. Vico,et al.  Automatic design synthesis with artificial intelligence techniques , 1999, Artif. Intell. Eng..

[8]  Stanley F. Bullington,et al.  Development of manufacturing control strategies using unsupervised machine learning , 1996 .

[9]  Nils Boysen,et al.  A classification of assembly line balancing problems , 2007, Eur. J. Oper. Res..

[10]  Ying Xiong,et al.  Fuzzy nonlinear programming for mixed-discrete design optimization through hybrid genetic algorithm , 2004, Fuzzy Sets Syst..

[11]  Chieh-Yuan Tsai,et al.  Fuzzy neural networks for intelligent design retrieval using associative manufacturing features , 2003, J. Intell. Manuf..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Takenao Ohkawa,et al.  A learning method of scheduling knowledge by genetic algorithm , 1995, Proceedings 1995 INRIA/IEEE Symposium on Emerging Technologies and Factory Automation. ETFA'95.

[14]  Daizhong Su,et al.  Evolutionary optimization within an intelligent hybrid system for design integration , 1999, Artif. Intell. Eng. Des. Anal. Manuf..

[15]  Joaquín Bautista,et al.  An Extended Beam-ACO Approach to the Time and Space Constrained Simple Assembly Line Balancing Problem , 2008, EvoCOP.

[16]  Emanuel Falkenauer,et al.  Genetic Algorithms and Grouping Problems , 1998 .

[17]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[18]  Jiachuan Wang,et al.  Interactive evolutionary solution synthesis in fuzzy set-based preliminary engineering design , 2003, J. Intell. Manuf..

[19]  David Taniar,et al.  Computational Science and Its Applications - ICCSA 2006, International Conference, Glasgow, UK, May 8-11, 2006, Proceedings, Part I , 2006, ICCSA.

[20]  Anil K. Jain,et al.  PRODUCTION SCHEDULING/RESCHEDULING IN FLEXIBLE MANUFACTURING , 1997 .

[21]  Brahim Rekiek,et al.  State of art of optimization methods for assembly line design , 2002, Annu. Rev. Control..

[22]  Yuehwern Yih,et al.  A learning-based methodology for dynamic scheduling in distributed manufacturing systems , 1995 .

[23]  Albert Jones,et al.  A hybrid approach for real-time sequencing and scheduling , 1995 .

[24]  John R. Dixon,et al.  A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes , 1989 .

[25]  Yuehwern Yih,et al.  Trace-driven knowledge acquisition (TDKA) for rule-based real time scheduling systems , 1990, J. Intell. Manuf..

[26]  Xuan F. Zha Soft computing framework for intelligent human–machine system design, simulation and optimization , 2003, Soft Comput..

[27]  Mitsuo Gen,et al.  Fuzzy multiple objective optimal system design by hybrid genetic algorithm , 2003, Appl. Soft Comput..

[28]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[29]  Jens Gottlieb,et al.  Evolutionary Computation in Combinatorial Optimization , 2006, Lecture Notes in Computer Science.

[30]  Semra Tunali,et al.  Improving the Genetic Algorithms Performance in Simple Assembly Line Balancing , 2006, ICCSA.

[31]  Chung Yee Lee,et al.  Job shop scheduling with a genetic algorithm and machine learning , 1997 .

[32]  S. Sivaloganathan,et al.  A Survey of Design Philosophies, Models, Methods and Systems , 1996 .

[33]  Pai-Chuan Lu The application of fuzzy neural network techniques in constructing an adaptive car-following indicator , 1998, Artif. Intell. Eng. Des. Anal. Manuf..

[34]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[35]  Hwai-En Tseng,et al.  Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing , 2008 .

[36]  Armin Scholl,et al.  State-of-the-art exact and heuristic solution procedures for simple assembly line balancing , 2006, Eur. J. Oper. Res..

[37]  Gary J. Koehler,et al.  Genetic learning of dynamic scheduling within a simulation environment , 1994, Comput. Oper. Res..

[38]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[39]  Mohsen Hamedi,et al.  Intelligent Fixture Design through a Hybrid System of Artificial Neural Network and Genetic Algorithm , 2005, Artificial Intelligence Review.

[40]  Armin Scholl,et al.  Balancing and Sequencing of Assembly Lines , 1995 .