依據行政院公共工程委員會三級品管制度，施工查核小組肩負監督公共工程履約與確保施工品質之責任。然而，某直轄市轄管工程案件眾多，除施工查核工作外，尚有辦理設計查核、夜間查核等其他工作，且各案件之規模與複雜度不一，在施工查核小組有限人力下，傳統人工指派工作方式常受主觀意識影響，難以兼顧公平性與勞逸平均，且耗時費力。 本研究旨在透過數學模式方法，解決施工查核小組之工作指派問題。以某直轄市政府施工查核小組為例，針對16名人力在1個月規劃期內對38件工作量進行最佳化指派，研究考量職務層級（領隊與組員）、性別限制、夜間查核及工作種類間隔等限制條件，並針對不同工作類型設定複雜勞役權重，利用0-1整數規劃建立數學模式，並運用LINGO軟體進行求解。研究結果顯示，本模式能快速求得最佳化工作指派表，且目標函數值為1.0，顯示工作指派具公平性，且透過敏感度分析證實，即便在增減工作天數、增加工作量或減少人力之情境下，本模式仍能維持指派之最佳化與穩定性。本研究成果可提供決策者作為人力調度之科學依據，有效提升行政效率並且達到公平合理。;Under the three-tier quality management system established by the Public Construction Commission of the Executive Yuan， the Construction Audit Task Force is responsible for supervising the contract performance of public works and ensuring construction quality. However， the special municipality in question manages a vast number of engineering projects with varying scales and complexities. In addition to standard construction audits， the task force must also handle design audits and night inspections. Given the limited manpower， traditional manual assignment methods are often swayed by subjectivity， making it difficult to achieve fairness and a balanced workload while being time-consuming and labor-intensive. This study aims to address the task assignment problem of the Construction Audit Task Force through mathematical modeling. Using the Construction Audit Task Force of a special municipality government as a case study， this research optimized the assignment of 38 tasks among 16 staff members over a one-month planning horizon. The study incorporated constraints such as job hierarchy (team leaders and members)， gender restrictions， night inspections， and intervals between task types. Additionally， workload weights were assigned to different task types. A mathematical model was established using 0-1 Integer Programming and solved using LINGO. The results demonstrate that the proposed model can rapidly generate an optimal task assignment schedule. An objective function value of 1.0 indicates that the assignments achieved fairness. Furthermore， sensitivity analysis confirmed that the model maintains its optimization capabilities and stability even under scenarios involving increased and reduced working days， increased workload， or manpower shortages. The findings of this research offer a scientific basis for workforce scheduling， which can effectively improve administrative efficiency and achieve fair and reasonable outcomes.
