A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization

Shujun Yang,Hanning Chen,Yichuan Zhang,Lianbo Ma,Yan Song,Gang Shi,Ping Zhou

doi:10.1109/access.2021.3105102

Abstract

With the continuous development of E-commerce, warehouse logistics is also facing emerging challenges, including more batches of orders and shorter order processing cycles. When more orders need to be processed simultaneously, some existing task scheduling methods may not be able to give a suitable plan, which delays order processing and reduces the efficiency of the warehouse. Therefore, the intelligent warehouse system that uses autonomous robots for automated storage and intelligent order scheduling is becoming mainstream. Based on this concept, we propose a multi-robot cooperative scheduling system in the intelligent warehouse. The aim of the multi-robot cooperative scheduling system of the intelligent storage is to drive many robots in an intelligent warehouse to perform the distributed tasks in an optimal (e.g., time-saving and energy-conserved) way. In this paper, we propose a multi-robot cooperative task scheduling model in the intelligent warehouse. For this model, we design a maximin-based multi-objective algorithm, which uses a one-by-one update scheme to select individuals. In this algorithm, two indicators are devised to discriminate the equivalent individuals with the same maximin fitness value in the environmental selection process. The results on benchmark test suite show that our algorithm is indeed a useful optimizer. Then it is applied to settle the multi-robot scheduling problem in the intelligence warehouse. Simulation experiment results demonstrate the efficiency of the proposed algorithm on the real-world scheduling problem.

Highlights

With the widespread application of autonomous robots, these cheap, small and smart robots have been widely employed in the intelligent storage management of the logistic industry [1]
It can be seen from the figures that the compromise surface formed by the solution generated by MO-multi-objective optimization algorithms (MOEAs) is better than the surface generated by other algorithms in both convergence and distribution, while MD-MOEA and RVEA* are competitive, but the effect is not very good from the perspective of distribution
There are 10 or 15 autonomous robots in the intelligent warehouse, all of which run at a speed of 1m/s and are initially located near the origin of coordinates

Summary

Introduction

With the widespread application of autonomous robots, these cheap, small and smart robots have been widely employed in the intelligent storage management of the logistic industry [1]. The basic task of the warehouse system in the intelligent storage management is to transport goods, store goods and distribute goods efficiently [2]. The multi-robot coordination mechanism, if being really efficient and robust, it will make the entire intelligent warehouse management system more. In the multi-robot coordination, a set of individual tasks needs to be scheduled (e.g., allocated and executed) in an optimal way. Such multi-robot scheduling problem can be formulated as a task allocation model where a group of autonomous robots should cater to a set of orders (tasks) in optimal routes satisfied with certain criteria [5]

Methods

Results

Conclusion