Hybrid Rule-Based, Fuzzy, and Optimization Matching Algorithm for Reserve Force Task Recommendation
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报告开始:2026年10月12日 14:00(Asia/Ho_Chi_Minh)

报告时间:15min

所在会场:[S5] Track 5: Emerging Trends of AI/ML [S5-3] Track 5: Emerging Trends of AI/ML

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摘要
When a reserve unit is mobilized, choosing reservists for a task takes more than finding someone with the right skill. The person also has to be active and available, have no conflicting schedule, be within a workable distance, hold a suitable rank, and not already be carrying too many assignments. In this paper we describe and test the matching algorithm used in a web-based skill-management and task-recommender system for reserve force mobilization. The algorithm works in three stages. First, a deterministic set of rules drops any candidate who cannot be assigned. Next, a fuzzy inference step turns skill match, distance, and rank difference into a single suitability score. Finally, a weighted optimization step combines suitability with rank and current workload and sorts the remaining candidates into a shortlist. We tested the algorithm with 13 mock reservist profiles across two deployment scenarios, along with 28 boundary-value unit tests, five weight settings, and three versions of the algorithm. Every unit test passed. With the full hybrid version, Precision@3 and top-three accuracy were both 100%, Recall@3 was 80%, and ranking took 0.0179 ms on average over 2,000 runs. These results suggest the method is fast and easy to interpret as a decision aid. Still, the scenarios were simulated, and the reference labels reflected criteria similar to those the algorithm uses. What the results show is internal consistency, not proof that the method works in the field.
关键词
personnel-task matching, rule-based system, fuzzy inference, optimization, recommender system, reserve force mobilization.
报告人
Maribel Campo
Faculty FEU Institute of Technology

稿件作者
Maribel Campo FEU Institute of Technology
Dennis Nava FEU Institute of Technology
Roman Villones FEU Institute of Technology
Mar Eli Sagsagat FEU Institute of Technology
Ace Lagman FEU INSTITUTE OF TECHNOLOGY MANILA
Delsa Bandila Cotabato State University
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重要日期
  • 会议日期

    10月11日

    2026

    至

    10月14日

    2026

  • 12月30日 2025

    报告提交截止日期

  • 09月28日 2026

    提前注册日期

  • 10月10日 2026

    初稿截稿日期

  • 10月14日 2026

    注册截止日期

主办单位
United Societies of Science
承办单位
Posts and Telecommunications Institute of Technology
协办单位
IEEE Section
IEEE Vietnam Section
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