Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

Abstract

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.