Developing artificial intelligence model to optimize ready-mix concrete (RMC) dispatch schedule

Abstract

In this paper, a hybrid optimization model of Dragonfly algorithm and Grey Wolf Optimization is proposed. In this model, the dragonfly algorithm (DA) is combined with the grey wolf optimizer (GWO) to improve the global exploration ability of the GWO algorithm. In contrast to the hybrid GWO developed in the literature, in the proposed model, the optimization process is guided by the GWO and DA. The search process begins with the parallel and separate processing of two subgroups of a population with the GWO and DA; these subgroups are then combined into one group at the end of an iteration. To verify the solution quality of the proposed model, its performance was compared with that of the Particle swam Optimization (PSO) and Ant Lion Optimization (ALO) by using a ready-mix concrete (RMC) dispatch schedule case study. The result indicates that the proposed hybrid model is superior to the PSO and ALO in terms of solution quality, stability, and capacity to discover the global optimum.