Multi-Objective Combinatorial Optimization for Dynamic Inspection Scheduling and Skill-Based Team Formation in Distributed Solar Energy Infrastructure
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| Title: | Multi-Objective Combinatorial Optimization for Dynamic Inspection Scheduling and Skill-Based Team Formation in Distributed Solar Energy Infrastructure |
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| Authors: | Mazin Alahmadi |
| Source: | Systems. 13:822 |
| Publisher Information: | MDPI AG, 2025. |
| Publication Year: | 2025 |
| Description: | Maintaining operational efficiency in distributed solar energy systems requires intelligent coordination of inspection tasks and workforce resources to handle diverse fault conditions. This study presents a bi-level multi-objective optimization framework that addresses two tightly coupled problems: dynamic job scheduling and skill-based team formation. The job scheduling component assigns geographically dispersed inspection tasks to mobile teams while minimizing multiple conflicting objectives, including travel distance, tardiness, and workload imbalance. Concurrently, the team formation component ensures that each team satisfies fault-specific skill requirements by optimizing team cohesion and compactness. To solve the bi-objective team formation problem, we propose HMOO-AOS, a hybrid algorithm integrating six metaheuristic operators under an NSGA-II framework with an Upper Confidence Bound-based Adaptive Operator Selection. Experiments on datasets of up to seven instances demonstrate statistically significant improvements (p |
| Document Type: | Article |
| Language: | English |
| ISSN: | 2079-8954 |
| DOI: | 10.3390/systems13090822 |
| Rights: | CC BY |
| Accession Number: | edsair.doi...........3f23f85bc85e94b89ea7afba9131e01b |
| Database: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | Maintaining operational efficiency in distributed solar energy systems requires intelligent coordination of inspection tasks and workforce resources to handle diverse fault conditions. This study presents a bi-level multi-objective optimization framework that addresses two tightly coupled problems: dynamic job scheduling and skill-based team formation. The job scheduling component assigns geographically dispersed inspection tasks to mobile teams while minimizing multiple conflicting objectives, including travel distance, tardiness, and workload imbalance. Concurrently, the team formation component ensures that each team satisfies fault-specific skill requirements by optimizing team cohesion and compactness. To solve the bi-objective team formation problem, we propose HMOO-AOS, a hybrid algorithm integrating six metaheuristic operators under an NSGA-II framework with an Upper Confidence Bound-based Adaptive Operator Selection. Experiments on datasets of up to seven instances demonstrate statistically significant improvements (p |
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| ISSN: | 20798954 |
| DOI: | 10.3390/systems13090822 |