Applying Genetic Local Search Algorithm to Solve the Job-Shop Scheduling Problem

This paper presents a genetic local search algorithm for the Job-Shop Scheduling problem, and the chromosome representation of the problem is based on the operation-based representation. In order to reduce the search space, schedules are constructed using a procedure that generates active schedules. After a schedule is obtained, a local search heuristic based on N6 neighborhood structure is applied to improve the solution. In order to avoid premature convergence of the conventional genetic algorithms (GA), the improved precedence operation crossover (IPOX) and approach of the generation alteration schema are proposed. The approach is tested on a set of standard instances taken from the literature. The computation results validate the effectiveness of the proposed algorithm.