Genetic Algorithm and Ant Colony Optimization for Optimizing Combinatorial Fuzzy Job Shop Scheduling Problems

In this paper, we present a genetic algorithm and ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). The genetic algorithm generates the initial population, selects the individuals for reproduction creating new individuals. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, 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 optimized 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. Computational results of these optimization algorithms are compared by analyzing the JSSP benchmark instances, FT10 and the ABZ10 problems.