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A Novel Maximin-Based Multi-Objective Evolutionary Algorithm Using One-by-One Update Scheme for Multi-Robot Scheduling Optimization
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DOI：10.1109/ACCESS.2021.3105102.png)
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Abstract
En 中文
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.
Keywords:
Task analysis
Robots
Robot kinematics
Optimization
Job shop scheduling
Warehousing
Evolutionary computation
Many-objective optimization
multi-objective optimization
maximin fitness function
one-by-one update scheme
multi-robot scheduling optimization
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