Modeling of biological intelligence for SCM system optimization. (English) Zbl 1235.90022

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

Comput. Math. Methods Med. 2012, Article ID 769702, 10 p. (2012).

Summary: This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

MSC:

90B06	Transportation, logistics and supply chain management
90C59	Approximation methods and heuristics in mathematical programming

Software:

ABC

References:

[1]	H. Sarimveis, P. Patrinos, C. D. Tarantilis, and C. T. Kiranoudis, “Dynamic modeling and control of supply chain systems: a review,” Computers and Operations Research, vol. 35, no. 11, pp. 3530-3561, 2008. · Zbl 1146.90353
[2]	Y. Zheng, J. Wang, and J. Xue, “A-team based supply chain management agent architecture,” International Journal on Artificial Intelligence Tools, vol. 18, no. 6, pp. 801-823, 2009. · Zbl 05742375
[3]	M. Dong, Process modeling, performance analysis and configuration simulation in integrated supply chain network design, Ph.D. thesis, Virginia Polytechnic Institute and State University, Blacksburg, Va, USA, 2001.
[4]	E. Sanchez, A. Perez-Uribe, A. Upegui et al., “PERPLEXUS: pervasive computing framework for modeling complex virtually-unbounded systems,” in Proceedings of the 2nd NASA/ESA Conference on Adaptive Hardware and Systems (AHS ’07), pp. 587-591, August 2007.
[5]	J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, Mich, USA, 1975.
[6]	D. B. Fogel, “Introduction to simulated evolutionary optimization,” IEEE Transactions on Neural Networks, vol. 5, no. 1, pp. 3-14, 1994.
[7]	I. Rechenberg, Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien der Biologischen Evolution, Frommberg-Holzboog, Stuttgart, Germany, 1973.
[8]	H. P. Schwefel, Kybernetischer Evolution als Strategie der experimentellen Forschung in der Strömungstechnik, Diploma thesis, Technical University of Berlin, Berlin, Germany, 1975.
[9]	R. Storn and K. Price, “Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces,” Tech. Rep. TR-95-012, International Computer Science Institute, Berkeley, Calif, USA, 1995. · Zbl 0888.90135
[10]	J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942-1948, Perth WA, Australia, December 1995.
[11]	M. Clerc, Particle Swarm Optimization, ISTE, London, UK, 2006. · Zbl 1130.90059
[12]	J. D. Farmer, N. H. Packard, and A. S. Perelson, “The immune system, adaptation, and machine learning,” Physica D: Nonlinear Phenomena, vol. 22, no. 1-3, pp. 187-204, 1986.
[13]	M. Gen and R. Cheng, Genetic algorithms and engineering design, Wiley-Interscience, New York, NY, USA, 1997.
[14]	G. Zhou, H. Min, and M. Gen, “The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach,” Computers and Industrial Engineering, vol. 43, no. 1-2, pp. 251-261, 2002.
[15]	M. Gen, A. Kumar, and J. R. Kim, “Recent network design techniques using evolutionary algorithms,” International Journal of Production Economics, vol. 98, no. 2, pp. 251-261, 2005.
[16]	A. Cakravastia, I. S. Toha, and N. Nakamura, “A two-stage model for the design of supply chain networks,” International Journal of Production Economics, vol. 80, no. 3, pp. 231-248, 2002.
[17]	G. Wang, Y. Liu, and M. Zheng, “Fuzzy two-stage supply chain problem and its intelligent algorithm,” in Proceedings of the 6th International Symposium on Neural Networks, vol. 5552, part 2 of Lecture Notes in Computer Science, pp. 15-24, Wuhan, China, 2009. · Zbl 05557441
[18]	F. T. S. Chan, S. H. Chung, and S. Wadhwa, “A heuristic methodology for order distribution in a demand driven collaborative supply chain,” International Journal of Production Research, vol. 42, no. 1, pp. 1-19, 2004. · Zbl 1052.90502
[19]	N. Jawahar and A. N. Balaji, “A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge,” European Journal of Operational Research, vol. 194, no. 2, pp. 496-537, 2009. · Zbl 1154.90629
[20]	C. Feng and Y. Zhang, “A genetic algorithm of two-stage supply chain distribution problem associated with fixed charge and multiple transportation modes,” in Proceedings of the 5th International Conference on Natural Computation (ICNC ’09), pp. 76-80, Tianjin, China, August 2009.
[21]	F. Altiparmak, M. Gen, L. Lin, and T. Paksoy, “A genetic algorithm approach for multi-objective optimization of supply chain networks,” Computers and Industrial Engineering, vol. 51, no. 1, pp. 196-215, 2006.
[22]	F. Altiparmak, M. Gen, L. Lin, and I. Karaoglan, “A steady-state genetic algorithm for multi-product supply chain network design,” Computers and Industrial Engineering, vol. 56, no. 2, pp. 521-537, 2009.
[23]	A. Costa, G. Celano, S. Fichera, and E. Trovato, “A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms,” Computers and Industrial Engineering, vol. 59, no. 4, pp. 986-999, 2010.
[24]	W. C. Yeh, “An efficient memetic algorithm for the multi-stage supply chain network problem,” International Journal of Advanced Manufacturing Technology, vol. 29, no. 7-8, pp. 803-813, 2006.
[25]	Y. Yun, C. Moon, and D. Kim, “Hybrid genetic algorithm with adaptive local search scheme for solving multistage-based supply chain problems,” Computers and Industrial Engineering, vol. 56, no. 3, pp. 821-838, 2009.
[26]	F. Sivrikaya-\cSrifo\vglu and G. Ulusoy, “Parallel machine scheduling with earliness and tardiness penalties,” Computers and Operations Research, vol. 26, no. 8, pp. 773-787, 1999. · Zbl 0932.90016
[27]	L. Min and W. Cheng, “A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines,” Artificial Intelligence in Engineering, vol. 13, no. 4, pp. 399-403, 1999.
[28]	J. M. Garcia, S. Lozano, K. Smith, T. Kwok, and G. Villa, “Coordinated scheduling of production and delivery from multiple plants and with time windows using genetic algorithms,” in Proceedings of the 9th International Conference on Neural Information Processing, vol. 3, pp. 1153-1158, Singapore, 2002.
[29]	C. W. Feng, T. M. Cheng, and H. T. Wu, “Optimizing the schedule of dispatching RMC trucks through genetic algorithms,” Automation in Construction, vol. 13, no. 3, pp. 327-340, 2004.
[30]	Y. H. Lee, C. S. Jeong, and C. Moon, “Advanced planning and scheduling with outsourcing in manufacturing supply chain,” Computers and Industrial Engineering, vol. 43, no. 1-2, pp. 351-374, 2002.
[31]	S. Karabuk, “Modeling and optimizing transportation decisions in a manufacturing supply chain,” Transportation Research Part E: Logistics and Transportation Review, vol. 43, no. 4, pp. 321-337, 2007.
[32]	S. H. Zegordi, I. N. K. Abadi, and M. A. B. Nia, “A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain,” Computers and Industrial Engineering, vol. 58, no. 3, pp. 373-381, 2010.
[33]	R. Chidambaram and D. Armbruster, “Application of genetic algorithms to semiconductor supply chain planning,” in Proceedings of the International Conference on Algorithmic Mathematics and Computer Science (AMCS ’05), pp. 77-83, Las Vegas, Nev, USA, June 2005.
[34]	D. Naso, M. Surico, B. Turchiano, and U. Kaymak, “Genetic algorithms for supply-chain scheduling: a case study in the distribution of ready-mixed concrete,” European Journal of Operational Research, vol. 177, no. 3, pp. 2069-2099, 2007. · Zbl 1110.90039
[35]	L. Dong and H. Ding, “A genetic algorithm based dynamic berth allocation strategy for container terminals,” in Proceedings of the 8th International Conference of Chinese Logistics and Transportation Professionals, Chengdu, China, August 2008.
[36]	M. R. Delavar, M. Hajiaghaei-Keshteli, and S. Molla-Alizadeh-Zavardehi, “Genetic algorithms for coordinated scheduling of production and air transportation,” Expert Systems with Applications, vol. 37, no. 12, pp. 8255-8266, 2010.
[37]	C. L. Yang and Y. K. Chen, “Using genetic algorithm for better route arrangement a case of Taiwan pelican express company,” in Proceedings of the IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI ’06), pp. 669-673, Shanghai, China, June 2006.
[38]	S. J. Sadjadi, M. Jafari, and T. Amini, “A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study),” International Journal of Advanced Manufacturing Technology, vol. 44, no. 1-2, pp. 194-200, 2009.
[39]	M. Azarmi, A. Jahanmiri, and S. Vakili, “An optimization model for refinery production scheduling (design a software using genetic algorithms & MILP),” in Proceedings of the 8th World Congress of Chemical Engineering, August 2009.
[40]	H. C. W. Lau, T. M. Chan, W. T. Tsui, and W. K. Pang, “Application of genetic algorithms to solve the multidepot vehicle routing problem,” IEEE Transactions on Automation Science and Engineering, vol. 7, no. 2, Article ID 4840417, pp. 383-392, 2010.
[41]	V. Chankong and Y. Y. Haimes, Multiobjective Decision Making Theory and Methodology, Elsevier, New York, NY, USA, 1983. · Zbl 0622.90002
[42]	F. T. S. Chan and S. H. Chung, “A multi-criterion genetic algorithm for order distribution in a demand driven supply chain,” International Journal of Computer Integrated Manufacturing, vol. 17, no. 4, pp. 339-351, 2004.
[43]	F. T. S. Chan, S. H. Chung, and S. Wadhwa, “A hybrid genetic algorithm for production and distribution,” Omega, vol. 33, no. 4, pp. 345-355, 2005.
[44]	K. Al-Mutawah, V. Lee, and Y. Cheung, “Modeling supply chain complexity using a distributed multi-objective genetic algorithm,” in Proceedings of International Conference on Computational Science and Its Applications, vol. 3980 of Lecture Notes in Computer Science, pp. 586-595, Melbourne, Australia, 2006. · Zbl 1162.90326
[45]	F. Altiparmak, M. Gen, L. Lin, and T. Paksoy, “A genetic algorithm approach for multi-objective optimization of supply chain networks,” Computers and Industrial Engineering, vol. 51, no. 1, pp. 196-215, 2006.
[46]	R. Z. Farahani and M. Elahipanah, “A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain,” International Journal of Production Economics, vol. 111, no. 2, pp. 229-243, 2008.
[47]	Z. H. Che and C. J. Chiang, “A modified Pareto genetic algorithm for multi-objective build-to-order supply chain planning with product assembly,” Advances in Engineering Software, vol. 41, no. 7-8, pp. 1011-1022, 2010. · Zbl 1231.90397
[48]	A. Gunasekaran and E. W. T. Ngai, “Modeling and analysis of build-to-order supply chains,” European Journal of Operational Research, vol. 195, no. 2, pp. 319-334, 2009. · Zbl 1156.90409
[49]	D. Fogel and K. Chellapilla, “Revisiting evolutionary programming,” in Applications and Science of Computational Intelligence, vol. 3390, pp. 2-11, Orlando, Fla, USA, March 1998.
[50]	X. Y. Huang and Z. Lu, “Interactive evolutionary programming and its application in supply chain,” Dongbei Daxue Xuebao/Journal of Northeastern University, vol. 21, no. 5, pp. 569-572, 2000.
[51]	J. Li, J. R. Wang, Z. W. Hu, and J. H. Zhang, “EP-based optimization of strategic safety stocks in reverse logistics systems,” Journal of Dong Hua University, vol. 21, no. 5, pp. 65-68, 2004.
[52]	J. Homberger, “A generic coordination mechanism for lot-sizing in supply chains,” Electronic Commerce Research, vol. 11, no. 2, pp. 1123-149, 2011.
[53]	V. Dalkilic, E. J. Lodree Jr., A. Ramesh, and G. Sarpkaya, “Order splitting for multiple-supplier multiple-item healthcare supply chains,” in Proceedings of the IIE Annual Conference and Exhibition, Orlando, Fla, USA, 2006.
[54]	M. A. Falcone, H. S. Lopes, and L. Dos Santos Coelho, “Supply chain optimisation using evolutionary algorithms,” International Journal of Computer Applications in Technology, vol. 31, no. 3-4, pp. 158-167, 2008.
[55]	S. Routroy and R. Kodali, “Differential evolution algorithm for supply chain inventory planning,” Journal of Manufacturing Technology Management, vol. 16, no. 1, pp. 7-17, 2005.
[56]	S. Routroy and P. Sanisetty, “Inventory planning for a multi-echelon supply chain,” International Journal of Operational Research, vol. 2, no. 3, pp. 269-283, 2007. · Zbl 1171.90319
[57]	S. Routroy and K. C. Maddala, “Multi-echelon supply chain inventory planning with demand and leadtime uncertainty,” International Journal of Operational Research, vol. 5, no. 3, pp. 251-264, 2009. · Zbl 1169.90310
[58]	K. Prasertwattana and Y. Shimizu, “Optimization of material ordering and inventory control of supply chain through an incentive scheme using differential evolution,” Journal of Japan Industrial Management Association, vol. 59, no. 4, pp. 283-289, 2008.
[59]	B. V. Babu and A. M. Gujarathi, “Multi-objective differential evolution (MODE) for optimization of supply chain planning and management,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC ’07), pp. 2732-2739, Singapore, September 2007.
[60]	L. Dos Santos Coelho and H. S. Lopes, “Supply chain optimization using chaotic differential evolution method,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3114-3119, Montréal, Canada, October 2006.
[61]	J.-M. Xu, J.-Z. Xiong, Y. Chen, and G.-W. Hu, “Supply chain optimization using migration differential evolution ensemble,” in Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS ’10), vol. 3, pp. 755-759, Xiamen, China, 2010.
[62]	A. Colorni, M. Dorigo, and V. Maniezzo, “Distributed optimization by ant colonies,” in Proceedings of the European Conference on Artificial Life, pp. 134-142, Paris, France, 1991.
[63]	C. A. Silva, J. M. C. Sousa, T. A. Runkler, and J. M. G. S. Da Costa, “Distributed optimisation of a logistic system and its suppliers using ant colonies,” International Journal of Systems Science, vol. 37, no. 8, pp. 503-512, 2006. · Zbl 1103.90310
[64]	C. A. Silva, J. M. C. Sousa, T. A. Runkler, and J. M. G. Sá da Costa, “Distributed supply chain management using ant colony optimization,” European Journal of Operational Research, vol. 199, no. 2, pp. 349-358, 2009. · Zbl 1176.90668
[65]	H. S. Wang, “A two-phase ant colony algorithm for multi-echelon defective supply chain network design,” European Journal of Operational Research, vol. 192, no. 1, pp. 243-252, 2009. · Zbl 1180.90105
[66]	L. A. Moncayo-Martínez and D. Z. Zhang, “Multi-objective ant colony optimisation: a meta-heuristic approach to supply chain design,” International Journal of Production Economics, vol. 131, no. 1, pp. 407-420, 2011.
[67]	M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, 2002. · Zbl 05451976
[68]	J. Izquierdo, R. Minciardi, I. Montalvo, M. Robba, and M. Tavera, “Particle Swarm Optimization for the biomass supply chain strategic planning,” in Proceedings of the International Congress on Environmental Modelling and Software, pp. 1272-1280, 2008.
[69]	G. W. Hu, “Generalized genetic particle swarm optimization for supply chain optimization,” Journal of Computer Applications, vol. 28, no. 11, pp. 2840-2843, 2008. · Zbl 1171.90559
[70]	R. Kadadevaramath, K. Mohanasundaram, K. Rameshkumar, and B. Chandrashekhar, “Production and distribution scheduling of Supply Chain structure using intelligent Particle Swarm Optimisation algorithm,” International Journal of Intelligent Systems Technologies and Applications, vol. 6, no. 3-4, pp. 249-268, 2009.
[71]	M. Bachlaus, M. K. Pandey, C. M. Shankar, and M. K. Tiwari, “Designing an integrated multi-echelon agile supply chain network: a Hybrid Taguchi-Particle swarm optimization approach,” International Journal of Flexible Manufacturing System, vol. 19, no. 6, pp. 486-515, 2007.
[72]	A. A. Taleizadeh, S. T. A. Niaki, N. Shafii, R. G. Meibodi, and A. Jabbarzadeh, “A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain,” International Journal of Advanced Manufacturing Technology, vol. 51, no. 9-12, pp. 1209-1223, 2010.
[73]	A. K. Sinha, H. K. Aditya, M. K. Tiwari, and F. T. S. Chan, “Agent oriented petroleum supply chain coordination: co-evolutionary particle swarm optimization based approach,” Expert Systems with Applications, vol. 38, no. 4, pp. 6132-6145, 2011.
[74]	X. J. Wu, H. Zhou, and C. H. Liang, “Collaborative optimization of multi-echelon supply chain based on co-evolutionary particle swarm optimization,” Computer Integrated Manufacturing Systems, vol. 16, no. 1, pp. 127-132, 2010.
[75]	G. Taguchi, S. Chowdhary, and S. Taguchi, Robust engineering, McGraw-Hill, New York, NY, USA, 2000.
[76]	C. Soares, G. Dozier, E. Lodree, J. Phillips, K. Nobles, and W. P. Yong, “Optimization of the multiple retailer supply chain management problem,” in Proceedings of the 46th Annual Southeast Regional Conference on XX ACM-SE, pp. 490-495, Auburn, Ala, USA, March 2008.
[77]	X. S. Yang, “Engineering optimizations via nature-inspired virtual bee algorithms,” in Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach, vol. 3562 of Lecture Notes in Computer Science, pp. 317-323, Springer, Berlin, Germany, 2005.
[78]	D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, and M. Zaidi, “The bees algorithm,” Tech. Rep., Manufacturing Engineering Centre, Cardiff University, 2005.
[79]	D. Karaboga, “An idea based on honeybee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes University, 2005.
[80]	D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459-471, 2007. · Zbl 1149.90186
[81]	V. V. Kumar, F. T. S. Chan, N. Mishra, and V. Kumar, “Environmental integrated closed loop logistics model: an artificial bee colony approach,” in Proceedings of the 8th International Conference on Supply Chain Management and Information Systems: Logistics Systems and Engineering (SCMIS ’10), Hong Kong, China, 2010.
[82]	A. Pal, F. T. S. Chan, B. Mahanty, and M. K. Tiwari, “Aggregate procurement, production, and shipment planning decision problem for a three-echelon supply chain using swarm-based heuristics,” International Journal of Production Research, vol. 49, no. 10, pp. 2873-2905, 2011.
[83]	S. Banerjee, G. S. Dangayach, S. K. Mukherjee, and P. K. Mohanti, “Modelling process and supply chain scheduling using hybrid meta-heuristics,” Studies in Computational Intelligence, vol. 128, pp. 277-300, 2008. · Zbl 1151.90393
[84]	J.-Q. Li, Q.-K. Pan, and K.-Z. Gao, “Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems,” International Journal of Advanced Manufacturing Technology, vol. 55, no. 9-12, pp. 1159-1169, 2011.
[85]	A. Prakash and S. G. Deshmukh, “A multi-criteria customer allocation problem in supply chain environment: an artificial immune system with fuzzy logic controller based approach,” Expert Systems with Applications, vol. 38, no. 4, pp. 3199-3208, 2011.
[86]	M. Shukla, N. Shukla, M. K. Tiwari, and F. T. S. Chan, “Integrated model for the batch sequencing problem in a multi-stage supply chain: an artificial immune system based approach,” International Journal of Production Research, vol. 47, no. 4, pp. 1015-1037, 2009. · Zbl 1216.90011
[87]	M. Hajiaghaei-Keshteli, “The allocation of customers to potential distribution centers in supply chain networks: GA and AIA approaches,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2069-2078, 2011. · Zbl 05889524
[88]	D. Srinivasan and T. H. Seow, “Particle swarm inspired evolutionary algorithm (PS-EA) for multi-objective optimization problem,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2292-2297, Canbella, Australia, 2003.
[89]	R. Poli, C. Di Chio, and W. B. Langdon, “Exploring extended particle swarms: a genetic programming approach,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’05), pp. 169-176, Washington, DC, USA, June 2005.
[90]	W. Fu, M. Johnston, and M. Zhang, “Hybrid particle swarm optimisation algorithms based on differential evolution and local search,” in Proceedings of the Australasian Conference on Artificial Intelligence, vol. 6464 of Lecture Notes in Artificial Intelligence, pp. 313-322, 2010.
[91]	Y. J. Wang, “Improving particle swarm optimization performance with local search for high-dimensional function optimization,” Optimization Methods and Software, vol. 25, no. 5, pp. 781-795, 2010. · Zbl 1205.90273
[92]	S. Meeran and M. S. Morshed, “A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study,” Journal of Intelligent Manufacturing. In press.
[93]	X. Wang, X. Z. Gao, and S. J. Ovaska, “A hybrid artificial immune optimization method,” International Journal of Computational Intelligence Systems, vol. 2, no. 3, pp. 249-256, 2009.
[94]	D. Guo, J. Wang, J. Huang, R. Han, and M. Song, “Chaotic-NSGA-II: an effective algorithm to solve multi-objective optimization problems,” Proceedings of the International Conference on Intelligent Computing and Integrated Systems (ICISS ’10), pp. 20-23, 2010.
[95]	R. Caponetto, L. Fortuna, S. Fazzino, and M. G. Xibilia, “Chaotic sequences to improve the performance of evolutionary algorithms,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 3, pp. 289-304, 2003. · Zbl 05451942
[96]	G. Zhang, “Quantum-inspired evolutionary algorithms: a survey and empirical study,” Journal of Heuristics, vol. 17, no. 3, pp. 303-351, 2010. · Zbl 1214.68378
[97]	E. G. Bakhoum and C. Toma, “Specific mathematical aspects of dynamics generated by coherence functions,” Mathematical Problems in Engineering, vol. 2011, Article ID 436198, 10 pages, 2011. · Zbl 1248.37075
[98]	E. G. Bakhoum and C. Toma, “Dynamical aspects of macroscopic and quantum transitions due to coherence function and time series events,” Mathematical Problems in Engineering, vol. 2010, Article ID 428903, 13 pages, 2010. · Zbl 1191.35219
[99]	F. Tao, L. Zhang, Z. H. Zhang, and A. Y. C. Nee, “A quantum multi-agent evolutionary algorithm for selection of partners in a virtual enterprise,” Manufacturing Technology, vol. 59, no. 1, pp. 485-488, 2010.