A Zestful Combination of Abc with Ga for Quenching of Job Shop Scheduling Problems

The job shop scheduling problem (JSSP) has attracted much attention in the field of both information sciences and operations research. This paper considers the permutation Job shop scheduling problem with the objective of minimizing make span. Artificial Bee Colony algorithm (ABC) is one of the search heuristics used to solve global optimization problems in complex search spaces. It is observed that, the efficiency of ABC in solving a Job shop problem can be improved significantly by tailoring another technique Genetic Algorithm (GA) to suit the structure of the problem. In this paper, an effective GA for rectifying Job shop scheduling problems is also proposed. The multi objective job shop scheduling problem can rectify and the performance will improve to an extreme by execution of proposed hybrid ABC-GA approach. The proposed technique will be implemented in the working platform of Matlab and the performance will be analyzed by comparing with the conventional methods for JSSP. Computational results based on some permutation Job shop scheduling show that the GA gives a better solution when compared with the earlier reported results.