Inference search algorithm for optimizing scheduling and minimizing mean tardiness in parallel joint robots

Abstract

The issue of scheduling identical parallel joint robots to reduce the mean tardiness is one of the most important challenges in industrial parallel robots. Precedence and job inconsistency are the constraint of this challenging issue. Indeed, due to the precedence constraint and inconsistencies among jobs, achieving best solution to schedule a number of jobs in a number of robots is highly difficult. This problem is regarded as one of the NP-hard class issues. In this study, despite the constraints of precedence and inconsistency among jobs, a new method based on inference search (IS) algorithm was proposed for scheduling and minimizing mean tardiness in identical parallel joint robots. Results of the simulations conducted in this study indicate that the proposed method, unlike genetic algorithms (GA), tabu search (TS) algorithms and hybrid intelligent solution system (HISS) is scalable and it optimized the parameters of mean tardiness and system solution time.