An adaptive experience-based discrete genetic algorithm for multi-trip picking robot task scheduling in smart orchards

TL;DR

This paper presents an adaptive experience-based discrete genetic algorithm designed to optimize multi-trip picking robot scheduling in smart orchards, effectively handling complex constraints and interdependencies for improved efficiency.

Contribution

The study introduces a novel AEDGA with load-distance balancing, clustering-based local search, and adaptive selection, advancing optimization in multi-robot orchard harvesting tasks.

Findings

AEDGA outperforms existing algorithms on multiple test instances.

The proposed method effectively manages makespan constraints and robot interdependencies.

Experimental results show significant efficiency improvements over state-of-the-art algorithms.

Abstract

The continuous innovation of smart robotic technologies is driving the development of smart orchards, significantly enhancing the potential for automated harvesting systems. While multi-robot systems offer promising solutions to address labor shortages and rising costs, the efficient scheduling of these systems presents complex optimization challenges. This research investigates the multi-trip picking robot task scheduling (MTPRTS) problem. The problem is characterized by its provision for robot redeployment while maintaining strict adherence to makespan constraints, and encompasses the interdependencies among robot weight, robot load, and energy consumption, thus introducing substantial computational challenges that demand sophisticated optimization algorithms.To effectively tackle this complexity, metaheuristic approaches, which often utilize local search mechanisms, are widely employed. Despite the critical role of local search in vehicle routing problems, most existing algorithms are hampered by redundant local operations, leading to slower search processes and higher risks of local optima, particularly in large-scale scenarios. To overcome these limitations, we propose an adaptive experience-based discrete genetic algorithm (AEDGA) that introduces three key innovations: (1) integrated load-distance balancing initialization method, (2) a clustering-based local search mechanism, and (3) an experience-based adaptive selection strategy. To ensure solution feasibility under makespan constraints, we develop a solution repair strategy implemented through three distinct frameworks. Comprehensive experiments on 18 proposed test instances and 24 existing test problems demonstrate that AEDGA significantly outperforms eight state-of-the-art algorithms.