Title: Sustainable scheduling algorithm for social green assets based on big data

Authors: Jia Jiao; Junmei Li

Addresses: School of Accounting, Shaanxi Technical College of Finance and Economics, Xianyang, Shaanxi, 712000, China ' School of Accounting, Shaanxi Technical College of Finance and Economics, Xianyang, Shaanxi, 712000, China

Abstract: The current methods used cannot effectively improve the economic benefits of green assets. Given this, this study innovatively combines big data with sustainable scheduling algorithms, proposes a new scheduling algorithm for green assets, and verifies its effectiveness through simulation experiments. In the comparison of the system penetration rate between the improved scheduling algorithm and the two commonly used scheduling algorithms, the improved algorithm always maintained a range of 70%-86%, and its variation amplitude was relatively gentle. This indicated that the reliability of this algorithm was significantly higher than other algorithms. In the same scenario, the cost of the improved algorithm remained within the range of 1,600-2,800, and its usage time remained within the range of 150 s-230 s for all three algorithms. This indicated that the algorithm consumed significantly less time and cost in job scheduling compared to other scheduling algorithms. The experiment shows that the improved sustainable scheduling algorithm can effectively improve the economic benefits of green asset enterprises.

Keywords: social green assets; big data; sustainable scheduling algorithm; improved sustainable scheduling algorithm; ISSA; simulation model.

DOI: 10.1504/IJEE.2025.151280

International Journal of Environmental Engineering, 2025 Vol.13 No.4, pp.301 - 317

Received: 13 Mar 2025
Accepted: 04 Jul 2025
Published online: 20 Jan 2026 *

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