Treffer: 以整數規劃法探討學生實習分配問題.
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Providing internship programs as part of university education is seen as an effective strategy to enhance students' competitiveness in their careers. Collaboration between academia and industry in offering internship positions helps cultivate the talent needed for industry development. However, universities often do not consider the overall situation of students when allocating internships, leading to mismatches between students and internship positions, which affects the effectiveness of internships. This study utilizes integer programming to investigate the student internship allocation problem, considering students' overall preferences to optimize their allocation to internship positions that align with their individual preferences. Drawing on the mathematical model proposed by Magnanti and Natarajan in 2018, this study derives an optimal student internship allocation that considers both efficiency and fairness through integer programming. Since work performance can increase if students obtain their preferred internship jobs, this study utilizes overall utility maximization to derive an efficient internship allocation. To avoid the significant variance in the preference value of each student-internship pair, this study minimizes the number of students to internship positions they dislike. Finally, a random allocation mechanism is used to ensure a fairer overall allocation result. [ABSTRACT FROM AUTHOR]
大學提供學生實習計畫作為學習的一部分, 被視為提昇學生職涯競爭力的有效策略。 產業界與學校合作提供實習職缺, 共同養成產業發展所需人才。然而, 目前學校所提供的諸 多實習機會並未考量整體學生情況來有效進行實習分配, 導致學生與實習單位並不適配, 影 響實習成效。本研究運用整數規劃法探討學生實習分配問題, 考慮所有學生對各實習單位的 偏好值, 最佳化整體學生被分配到符合個人偏好的實習單位。本研究參考2018年Magnanti與 Natarajan 所提出的數學模型, 透過整數規劃法來求解兼顧效率與公平性的最佳的實習分配方 案。由於學生獲得較喜愛的實習工作可增加工作績效, 因此利用總體效用總和最大化來獲得 有效率的實習分配。而為了避免實習分配結果中有各實習配對偏好值差異很大的現象, 本研 究也利用最小化分配到偏好值較低的實習配對人數以及隨機分配機制, 以確保整體分配結果 更具公平性。. [ABSTRACT FROM AUTHOR]
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