Implementation of a multivariable modular structure for Fuzzy Taxi Scheduling System (FTSS)

Taxi scheduling problem represent the heart of management problems for most Taxi companies. This is due to the fact that information in such environment is dynamic in nature with a high degree of uncertainty. Fuzzy logic can be utilized to deal with such uncertainty in the information. Genetic algorithm can be utilized for optimization of solution. This paper describes the implementation of a hybridized intelligent algorithm to reach a decision under uncertainty and to tune the possible solution using genetic algorithm. This hybridization is one type of Genetic Fuzzy system termed the Genetic Fuzzimetric Technique (GFT). GFT was used with AHP technique to develop a prototype system to illustrate the proposed solution to the taxi scheduling problem termed as “Fuzzy Taxi Scheduling System” (FTSS). The developed prototype can also act as an educational tool towards illustrating the mechanism of building modular multivariable fuzzy system. Using FTSS, five criteria were chosen as inputs and one needed output which is the score of specific driver/taxi. An example of 4 Taxis/Drivers with different characteristic values was executed on FTSS to achieve the final optimized Taxi choice.