Optimizing Mobile Robot Fleet Distributions in Solar Farms

The maintenance and cleaning of solar farms make up a significant portion of their operational costs. Recently, the use of robots to perform these tasks has gained popularity, requiring efficient routing for their effective deployment. To further complement their missions, charging stations can be strategically placed throughout the site which enable robots to recharge mid-operation. This work addresses this challenge by modelling the problem as a multiple travelling salesmen problem and developing a mixed integer linear programming formulation. Four different solution methods were implemented: a brute force method, a random method, a genetic algorithm based method, and an exact approach. In addition, a search strategy was developed that adds charging stops to existing tours, extending existing MTSP algorithms for problems with battery or fuel constraints.

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Published
2025-04-02
DOI
https://doi.org/10.1109/icarsc65809.2025.10970171
Primary Topic
Advanced Manufacturing and Logistics Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimizing Mobile Robot Fleet Distributions in Solar Farms

Rui Bettencourt, Alberto Vale, Duarte Cerdeira
Advanced Manufacturing and Logistics Optimization
article

Optimizing Mobile Robot Fleet Distributions in Solar Farms

Rui Bettencourt, Alberto Vale, Duarte Cerdeira
article en

Abstract

The maintenance and cleaning of solar farms make up a significant portion of their operational costs. Recently, the use of robots to perform these tasks has gained popularity, requiring efficient routing for their effective deployment. To further complement their missions, charging stations can be strategically placed throughout the site which enable robots to recharge mid-operation. This work addresses this challenge by modelling the problem as a multiple travelling salesmen problem and developing a mixed integer linear programming formulation. Four different solution methods were implemented: a brute force method, a random method, a genetic algorithm based method, and an exact approach. In addition, a search strategy was developed that adds charging stops to existing tours, extending existing MTSP algorithms for problems with battery or fuel constraints.

University of Lisbon (PT), Universidade Politécnica de Lisboa (PT)
Nuclear Fuel Cycle and Supply Chain
Affordable and clean energy
Openalex Percentile: Top 89%
Advanced Manufacturing and Logistics Optimization
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Optimizing Mobile Robot Fleet Distributions in Solar Farms — Rui Bettencourt, Alberto Vale, et al. · (2025) | TGRS Research Map | TGRS