Genetic Algorithm for the Optimization of Collaborative Systems
Tad Gonsalves*, Shinichiro Baba**, and Kiyoshi Itoh*
*Laboratory of Information & Systems Engineering, Faculty of Science & Technology, Sophia University, 7-1 Kioicho, Chiyoda-ku, Tokyo 102-8554, Japan
**Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 1776-15-402 Maginu, Miyamae, Kawasaki, Kanagawa 216-0035, Japan
The “survival of the fittest” strategy of the Genetic Algorithm has been found to be robust and is widely used in solving combinatorial optimization problems like job scheduling, circuit design, antenna array design, etc. In this paper, we discuss the application of the Genetic Algorithm to the operational optimization of collaborative systems, illustrating our strategy with a practical example of a clinic system. Collaborative systems (also known as co-operative systems) are modeled as server-client systems in which a group of collaborators come together to provide service to end-users. The cost function to be optimized is the sum of the service cost and the waiting cost. Service cost is due to hiring professionals and/or renting equipment that provide service to customers in the collaborative system. Waiting cost is incurred when customers who are made to wait in long queues balk, renege or do not come to the system for service a second time. The number of servers operating at each of the collaborative places, and the average service time of each of the servers are the decision variables, while server utilization is a constraint. The Genetic Algorithm tailored to collaborative systems finds the minimum value of the cost function under these operational constraints.
-  T. Gonsalves and K. Itoh, “Generic Core Life Cycle and Conceptual Architecture for the Development of Collaborative Systems,” P. Bernus and M. Fox (Eds.), Knowledge Sharing in the Integrated Enterprise: Inter-operability Strategies for the Enterprise Architect, Springer, New York, 2005.
-  A. Hasegawa, S. Kumagai, and K. Itoh, “Collaboration Task Analysis by Identifying Multi-Context and Collaborative Linkage,” CERA, Vol.8, No.1, pp. 61-71, 2000.
-  J. Banks and J. S. Carson II, “Discrete-Event System Simulation,” Prentice-Hall, New Jersey, 1984.
-  J. Banks, J. S. Carson II, B. L. Nelson, and D. M. Nicol, “DES Simulation (3rd ed.),” Prentice-Hall, New Jersey, 2001.
-  T. Gonsalves, K. Itoh, and R. Kawabata, “Performance Design and Improvement of Collaborative Engineering Systems by the Application of Knowledge-Based Qualitative Reasoning,” Knowledge Based Design Series, The ATLAS, Vol.1, pp. 1-31, 2005
-  J. Kuipers, “Qualitative Reasoning Modeling and Simulation with Incomplete Knowledge,” The MIT Press, Cambridge MA, 1994.
-  J. H. Holland, “Adaptations in Natural and Artificial systems,” The University of Michigan Press, Ann Arbor MI, 1975.
-  D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” Addison-Wesley, Reading MA, 1989.
-  M. Mitchell, “An Introduction to Genetic Algorithm,” MIT Press, Cambridge MA, 1996.
-  Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs,” Springer-Verlag, 1996.
-  L. Davis (Ed.), “The Genetic Algorithms Handbook,” Van Nostrand Reinhold, New York, 1991.
-  J. R. Koza, “Genetic Programming,” MIT Press, MA, 1992.
-  S. Forrest, “Genetic Algorithms,” Computing Surveys, Vol.28:1, pp. 77-80, 1996.
-  C. Reeves, “Genetic Algorithms,” F. Glover and G. A. Kochenberger (Eds.), Handbook of Metaheuristics, Kluwer Academic Publications, Boston, 2003.
-  A. M. Law and W. D. Kelton, “Simulation Modeling and Analysis,” (2nd ed.), McGraw-Hill, New York, 1991.
-  F. S. Hillier, “Economic Models for Industrial Waiting Line Problems,” Management Science, Vol.10, No.1, pp. 119-130, 1963.
-  D. R. Anderson, D. J. Sweeney, and T. A. Williams, “An Introduction to Management Science: Quantitative Approaches to Decision Making (10th ed.),” Thomson South-Western, Ohio, 2003.
-  K. Itoh, S. Honiden, J. Sawamura, and K. Shida, “A Method for Diagnosis and Improvement on Bottleneck of Queuing Network by Qualitative and Quantitative Reasoning,” Journal of Artificial Intelligence, Vol.5, No.1, pp. 92-105, 1990 (in Japanese).
-  R. Jain, “The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling,” John Wiley & Sons, New York, 1991.
-  K. Deb, “Genetic Algorithms for Function Optimization,” F. Herra and J. L. Verdegay (Eds.), Genetic Algorithms and Soft Computing, Physica-Verlag, Heidelberg, 1996.
-  E. K. Chong and S. H. Zak, “An Introduction to Optimization (2nd ed.),” John Wiley & Sons, New York, 2001.
-  K. Deb, “Optimization for engineering design: Algorithms and examples,” Prentice Hall, Delhi, 1995.
-  A. E. Smith and D. W. Coit, “Penalty Functions,” Handbook of Evolutionary Computation, T. Baeck, D. Fogel, and Z.Michalewicz (Eds.), A Joint Publication of Oxford University Press and Institute of Physics Publishing, Oxford, 1995.
-  R. L. Haupt and S. E. Haupt, “Practical Genetic Algorithms,” John Wiley & Sons, New York, 1991.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.