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Data-Driven Scheduling Optimization of Electricity Customer Service Based on Demand Analysis and Skill Matching.

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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
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Header DbId: enr
DbLabel: Energy & Power Source
An: 191587287
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: Data-Driven Scheduling Optimization of Electricity Customer Service Based on Demand Analysis and Skill Matching.
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  Data: <searchLink fieldCode="AR" term="%22Qin%2C+Hao%22">Qin, Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhipeng%22">Xu, Zhipeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 202321015210@mail.scut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yi%2C+Yingqi%22">Yi, Yingqi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Shunda%22">Wu, Shunda</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xue%2C+Ying%22">Xue, Ying</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Feb2026, Vol. 19 Issue 3, p808. 16p.
– Name: Subject
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  Data: *<searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br />*<searchLink fieldCode="DE" term="%22Workforce+planning%22">Workforce planning</searchLink><br />*<searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Customer+services%22">Customer services</searchLink><br />*<searchLink fieldCode="DE" term="%22Industrial+efficiency%22">Industrial efficiency</searchLink><br />*<searchLink fieldCode="DE" term="%22Economic+demand%22">Economic demand</searchLink><br />*<searchLink fieldCode="DE" term="%22Customer+satisfaction%22">Customer satisfaction</searchLink><br />*<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=191587287
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3390/en19030808
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      – Code: eng
        Text: English
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        PageCount: 16
        StartPage: 808
    Subjects:
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Workforce planning
        Type: general
      – SubjectFull: Particle swarm optimization
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      – SubjectFull: Customer services
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      – SubjectFull: Industrial efficiency
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      – SubjectFull: Economic demand
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      – SubjectFull: Customer satisfaction
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      – SubjectFull: Resource allocation
        Type: general
    Titles:
      – TitleFull: Data-Driven Scheduling Optimization of Electricity Customer Service Based on Demand Analysis and Skill Matching.
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            NameFull: Qin, Hao
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            NameFull: Xu, Zhipeng
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            NameFull: Yi, Yingqi
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            NameFull: Wu, Shunda
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            NameFull: Xue, Ying
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 19
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            – TitleFull: Energies (19961073)
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