Particle swarm optimization based memetic algorithms framework for scheduling of central planned and distributed flowshops

Abstract

In this chapter, we provide a panorama of the PSO-based memetic algorithm (MA) for traditional permutation flowshop scheduling problem (PFSP) and its several variants. In the proposed algorithm, the global exploration ability of PSO and the local refinement ability of simulated annealing (SA) are delicately integrated and balanced. Some specific techniques related to the nature of PFSP are introduced to further improve the effectiveness of PSO-based MA. The key features in the proposed algorithm are detailed as follows. First, to apply PSO in solving combinatorial optimization problems such as PFSP, we rely on the ranked-order value (ROV) rule that uses random key representation to transform the continuous position information to scheduling permutations. Second, NEH and NEH-based constructive heuristics are introduced to guarantee a proportion of initial particles to be of good qualities. Third, to avoid the premature convergence problem of PSO, an adaptive SA-based local search is proposed to strengthen the exploitation in an efficient way. Forth, for the variation of PFSP that considers distributed processing factories, single assembly factory, and no-wait constraint (DAPFSP-NW), we include an extra encoding layer to represent the factory dispatch; thus, the proposed SA-based MA can still be applied. Moreover, the corresponding heuristic-based initialization and the neighborhoods adopted for local search are redefined. Last but not the least, for the variation with stochastic processing time and assembling time, the technique of hypothesis test (HT) is integrated into PSO-based MA; therefore, the solutions generated in each iteration can be effectively compared in a statistical way. Our experimental results strongly indicate the superiority of the PSO-based MA for solving PFSP, DAPFSP-NW, and stochastic DAPFSPNW at the aspects of optimization quality and robustness.