Paper:
Language: English:
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
Received: January 16, 2007
Accepted: May 22, 2007
Keywords: genetic algorithm, simulation optimization, meta-heuristic, collaborative systems
Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.11, No.7 pp. 793-802, 2007
Abstract
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.
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