Data-Driven Scheduling Optimization of Electricity Customer Service Based on Demand Analysis and Skill Matching.
Saved in:
| Title: | Data-Driven Scheduling Optimization of Electricity Customer Service Based on Demand Analysis and Skill Matching. |
|---|---|
| Authors: | Qin, Hao1,2 (AUTHOR), Xu, Zhipeng1,2 (AUTHOR) 202321015210@mail.scut.edu.cn, Yi, Yingqi1 (AUTHOR), Wu, Shunda1,2 (AUTHOR), Xue, Ying1 (AUTHOR) |
| Source: | Energies (19961073). Feb2026, Vol. 19 Issue 3, p808. 16p. |
| Subject Terms: | *Scheduling, *Workforce planning, *Particle swarm optimization, *Customer services, *Industrial efficiency, *Economic demand, *Customer satisfaction, *Resource allocation |
| Abstract: | To address surging and uncertain electricity customer demands, this paper proposes a data-driven electricity customer service scheduling (ECSS) optimization model to improve customer service quality and alleviate agent scheduling pressure. The method begins by building a demand analysis model based on customer feature extraction using the maximal information coefficient (MIC). An agent workforce sizing model is then developed by integrating the AHP–fuzzy comprehensive evaluation and Z-score standardization, accounting for call-volume proportion, hourly call-handling capacity, and time-period length. Furthermore, a demand–skill matching method is introduced between customer calls and agent skills. A particle swarm optimization (PSO)-based intelligent scheduling algorithm is established, with queuing time, skill level, and handling time as key objectives and constraints. Case-study validation shows that the model improves operational efficiency by approximately 26.28% and reduces annual labor costs by about 6.13%, thereby enhancing customer satisfaction, service center efficiency, and scheduling system economy. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | To address surging and uncertain electricity customer demands, this paper proposes a data-driven electricity customer service scheduling (ECSS) optimization model to improve customer service quality and alleviate agent scheduling pressure. The method begins by building a demand analysis model based on customer feature extraction using the maximal information coefficient (MIC). An agent workforce sizing model is then developed by integrating the AHP–fuzzy comprehensive evaluation and Z-score standardization, accounting for call-volume proportion, hourly call-handling capacity, and time-period length. Furthermore, a demand–skill matching method is introduced between customer calls and agent skills. A particle swarm optimization (PSO)-based intelligent scheduling algorithm is established, with queuing time, skill level, and handling time as key objectives and constraints. Case-study validation shows that the model improves operational efficiency by approximately 26.28% and reduces annual labor costs by about 6.13%, thereby enhancing customer satisfaction, service center efficiency, and scheduling system economy. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 19961073 |
| DOI: | 10.3390/en19030808 |
