The Arithmetic Optimization Algorithm for Multi-Objective Mobile Robot Scheduling

Abstract

In recent years, metaheuristic algorithms have become increasingly advantageous for solving many real-world optimization-based engineering tasks. Integrated process planning and scheduling of machine tools and mobile robots utilized for transportation tasks in a manufacturing environment represents one such task. Since the number of solutions increases exponentially with the addition of either parts, machines, or robots, this task belongs to a group of NP-hard problems. Therefore, for its successful resolution, it is essential to use efficient algorithms that are able to explore vast solution space and provide optimal solutions. In this paper, we propose an algorithm for solving integrated scheduling of machine tools and mobile robots based on a novel arithmetic metaheuristic optimization. The arithmetic optimization algorithm belongs to a group of stochastic population-based algorithms inspired by arithmetic mathematical operations. The main advantage of the proposed algorithm is in a well-suited balance between exploration and exploitation phases that are appropriate for extremely hard multi-objective optimization. A multi-objective metric is utilized to evaluate obtained Pareto front solutions in terms of the exploration capabilities in the solution space. The proposed algorithm is compared with two other state-of-the-art metaheuristic algorithms. The experimental evaluation is carried out on 20 benchmark problems, and the results show the advantages of the proposed algorithm.