Using evolutionary computation and local search to solve multi-objective flexible job shop problems

Finding realistic schedules for Flexible Job Shop Problems has attracted many researchers recently due to its NP-hardness. In this paper, we present an efficient approach for solving the multi-objective flexible job shop by combining Evolutionary Algorithm and Guided Local Search. Instead of applying random local search to find neighborhood solutions, we introduce a guided local search procedure to accelerate the process of convergence to Pareto-optimal solutions. The main improvement of this combination is to help diversify the population towards the Pareto-front. Empirical studies show that 1) the gaps between the obtained results and known lower bounds are small, and 2) the multi-objective solutions of our algorithms dominate previous designs for solving the same benchmarks while incurring less computational time.