A multi-objective particle swarm optimization for dual-resource constrained shop scheduling with resource flexibility

In this paper, a novel multi-objective hybrid particle swarm algorithm is proposed to solve the dual-resource constrained shop scheduling problem with minimizing production period and production cost being the objectives. First, particles are represented and updated directly in the discrete domain. Then simulated annealing with variable neighborhoods structure is introduced to improve the local search ability. Third, an external archive based on Pareto-dominance is applied to store the non-dominated solutions. The computational results are provided and compared with existing methods. It is shown that the proposed algorithm achieves better performance in both convergence and diversity.