Applying genetic local search to solve the Flexible Job-shop Scheduling Problem

The flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem (JSP). This paper presents a hybrid genetic algorithm which incorporates the principle of ldquothe survival of the fittestrdquo from genetic algorithm (GA) into tabu search (TS) to solve the FJSP. According to the characteristics of the FJSP, an extended operation-based representation which simultaneously describes the sequence of operations and the assignment of operations to machines are applied to represent solution for the genetic algorithm, and solutions are constructed using a procedure that generates active schedules. Two effective crossover and mutation operators are proposed to adapt to the chromosome structure. After individuals of GA are obtained, TS is applied to improve these solutions. The neighborhood structures of TS are designed by extending proposition proposed by Balas and Vazacopoulos to FJSP. The hybrid algorithm is tested on a set of standard instance taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.