An Integrated Genetic Algorithm for Flexible Job-Shop Scheduling Problem

Flexible job-shop scheduling problem (FJSP) is a well-known difficult combinatorial optimization problem. Many algorithms have been proposed for solving the FJSP in the last few decades, including algorithms based on evolutionary techniques. However, there is room for improvement. In this paper, we present a genetic algorithm (GA) for FJSP. The algorithm encodes the individual with parallel machine process sequence based code, integrates the Most Work Remaining, the Most Operation Remaining and random selection strategies for generating the initial population, and integrates the binary tournament selection and the linear ranking selection strategies to reproduce new individuals. Computational result shows that the integration of more strategies in a genetic framework leads to better results than the traditional genetic algorithms. The integrated genetic algorithm is effective for solving FJSP.