Multi-objective scheduling of a single mobile robot based on the grey wolf optimization algorithm

Abstract

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used for fulfilling material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually conflict. Therefore, obtaining the optimally scheduled robotic- based material transport system that is simultaneously facing several conflicting objectives is a highly challenging task. To address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation of 13 novel fitness functions combined to form a Pareto front of the multi-objective optimization problem and a novel strategy for optimal exploration of multi- objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with three state-of-the- art metaheuristic methods (MOGA, MOAOA, and MOPSO) on 25 benchmark problems. The results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outper- forms other algorithms in terms of convergence, coverage, and the robust optimal Pareto solution. Finally, transportation paths based on obtained scheduling plans are experimentally corroborated by the mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of material transport tasks based on a single mobile robotic system.