Research on Optimum Design of Waste Recycling Network for Agricultural Production

• Published in: Systems (Basel) Vol. 13; no. 7; p. 570
• Main Authors: Wu, Huabin, Zhang, Jing, Ji, Yanshu, Su, Yuelong, Shu, Shumiao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2025
• Subjects: Agricultural production; agricultural production waste; Agricultural wastes; Agriculture; Algorithms; Analytic hierarchy process; ant colony algorithm; Ant colony optimization; immune algorithm; Industrial locations; Infrastructure; Integrated approach; Medical wastes; Operating costs; Optimization models; path optimization; Public health; Recycling; recycling network; Refuse and refuse disposal; Route optimization; Route planning; Rural areas; Scheduling; Site selection; Traffic; Vehicles; Waste management; Iran; China
• DOI: 10.3390/systems13070570
• ISSN: 2079-8954

Agricultural production waste (APW) is characterized by pollution, increasing volume, spatial dispersion, and temporal and spatial variability in its generation. The improper handling of APW poses a growing risk to the environment and public health. This paper focuses on the planning of APW recycling networks, primarily analyzing the selection of temporary storage sites and treatment facilities, as well as vehicle scheduling and route optimization. First, to minimize the required number of temporary storage sites, a set coverage model was established, and an immune algorithm was used to derive preliminary site selection results. Subsequently, the analytic hierarchy process and fuzzy comprehensive evaluation method were employed to refine and determine the optimal site selection results for recycling treatment facilities. Second, based on the characteristics of APW, with the minimization of recycling transportation costs as the optimization objective, an ant colony algorithm was used to establish a corresponding vehicle scheduling route optimization model, yielding the optimal solution for recycling vehicle scheduling and transportation route optimization. This study not only improved the recycling efficiency of APW but also effectively reduced the recycling costs of APW.