Novel modifications of social engineering optimizer to solve a truck scheduling problem in a cross-docking system

Abstract

The truck scheduling problem is one of the most challenging and important types of scheduling with a large number of real-world applications in the area of logistics and cross-docking systems. This problem is formulated to find an optimal condition for both receiving and shipping trucks sequences. Due to the difficulty of the practicality of the truck scheduling problem for large-scale cases, the literature has shown that there is a chance, even with low possibility, for a new optimizer to outperform existing algorithms for this optimization problem. Already applied successfully to solve similar complicated optimization problems, the Social Engineering Optimizer (SEO) inspired by social engineering phenomena, has been never applied to the truck scheduling problem. This motivates us to develop a set of novel modifications of the recently-developed SEO. To validate these optimizers, they are evaluated by solving a set of standard benchmark functions. All the algorithms have been calibrated by the Taguchi experimental design approach to further enhance their optimization performance. In addition to some benchmarks of truck scheduling, a real case study to prove the proposed problem is utilized to show the high-efficiency of the developed optimizers in a real situation. The results indicate that the proposed modifications of SEO considerably outperform the state of the art algorithms and provide very competitive results.