In this paper, we present a genetic algorithm and ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). The genetic algorithm comprises of different stages like generating the initial population, selecting the individuals for reproduction and reproducing new individuals. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimizes using GA and ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the genetic algorithm and the ant colony algorithm are evaluated and compared. The improvement in the performance of the algorithms based on the computed parameters is also discussed in this paper. Computational results of these optimization algorithms are compared by analyzing the JSSP benchmark instances, FT10 and the ABZ10 problems.
P. Surekha and Sumathi, S., “Solving Fuzzy Based Job Shop Scheduling Problems using GA and ACO”, International Journal of Emerging Trends in Computing and Information Sciences, vol. 1, no. 2, pp. 95-102, 2010.