Sustainable and Efficient Academic Administration through Eulerian Routing and Algorithmic Decision-Making
Keywords:
Administrative analytics, Digital academic ecosystems, Dynamic scheduling, Eulerian optimization, Graph-theoretic governance, NEP-driven transformationAbstract
The transformative vision of the National Education Policy 2020 (NEP 2020) has accelerated the digital restructuring of higher education institutions, demanding intelligent, flexible, and optimization-driven governance mechanisms. As academic institutions increasingly adopt digital platforms for curriculum delivery, credit transfer, internship monitoring, continuous evaluation, and multidisciplinary coordination, administrative workflows have become highly interconnected and dynamic. Particularly in government degree colleges facing faculty shortages and infrastructural constraints, efficient task scheduling and resource allocation have emerged as critical challenges.
This study proposes a graph-theoretic optimization framework grounded in the Chinese Postman Problem (CPP) to design adaptive Eulerian scheduling models for digital academic ecosystems. Academic and administrative tasks are modeled as vertices and weighted edges within a connected network, where edge weights represent processing time, digital load, or priority level. By transforming non-Eulerian institutional networks into Eulerian graphs using minimum-weight matching techniques, the proposed model ensures complete task coverage with minimal redundancy and optimal traversal cost.
Unlike traditional static scheduling approaches, the framework introduces dynamic recalibration of edge weights to reflect real-time academic variability, policy-driven priorities, and workload fluctuations. The algorithm integrates combinatorial optimization and shortest-path strategies to enhance transparency, accountability, and operational efficiency within digital governance structures such as Academic Bank of Credits (ABC) management and interdisciplinary programme coordination. The findings demonstrate that Eulerian optimization significantly reduces administrative latency, duplication of effort, and resource wastage while improving workflow coherence. The study contributes to the emerging domain of algorithmic governance in higher education by bridging discrete mathematics, digital transformation, and institutional sustainability. It establishes a mathematically rigorous yet practically implementable model for smart governance in contemporary digital higher education systems.
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