A Hybrid Attraction–Repulsion Optimization Algorithm for Multi-Robot Task Scheduling in Intelligent Greenhouses

Abstract

In intelligent greenhouse environments, multi-robot cooperation over complex road networks and multi-stage operations involves strong coupling among task allocation, operation sequencing, and path selection. Solving these decisions separately may lead to an elongated critical path and inefficient resource utilization. This study addresses the Greenhouse Multi-Robot Task Scheduling (GMRTS) problem by developing a mathematical model aimed at minimizing the system makespan $C_{max}$. To solve this problem, a Hybrid Attraction-Repulsion Optimization Algorithm (HAROA) is proposed, which balances global exploration and local exploitation through a three-phase cooperative search mechanism consisting of the Vortex Diffusion Strategy (VDS), Gravitational Confluence Strategy (GCS), and Turbulent Transition Strategy (TTS). Experimental results on the CEC2017 benchmark suite and intelligent greenhouse scheduling scenarios show that HAROA achieves superior performance over comparative algorithms in terms of solution accuracy and $C_{max}$, together with faster convergence and higher solution stability. Further analyses confirm the effectiveness of the proposed strategies, the transferability of the method, the suitability of the $C_{max}$-oriented formulation, and the applicability of the framework to dynamic task scenarios.