Department of Data Science and Engineering, IISER Bhopal, Bhopal, India.
Department of Physics, IISER Bhopal, Bhopal, India.
Sci Rep. 2023 Feb 9;13(1):2365. doi: 10.1038/s41598-023-29593-x.
Waste collection in developing nations faces multi-fold challenges, such as resource constraints and real-time changes in waste values, while finding the optimal routes. This paper attempts to address these challenges by modeling real-time waste values in smart bins and Collection Vehicles (CV). Further, waste value prioritized routes for coordinated CV, during various time intervals are modeled in a multi-agent environment for finding good routes. The CV, as agents, implement the formulated linear program to maximize the collected waste while minimizing the distance to the central depot. The city of Chandigarh, India, was divided into regions and the model was implemented to achieve significantly better performance in terms of waste collected in less distance and total bins covered when compared to the existing scenario. The stakeholders can use the outcomes to effectively plan the resources for better collection practices, which will have a positive impact on the environment.
发展中国家的废物收集面临着多方面的挑战，例如资源限制和实时变化的废物价值，同时还需要找到最佳路线。本文试图通过在智能垃圾桶和收集车辆 (CV) 中实时建模废物价值来应对这些挑战。此外，还在多代理环境中为协调 CV 建模了不同时间间隔的废物价值优先路线，以找到好的路线。作为代理的 CV 会执行制定的线性规划，以在收集废物的同时最大限度地减少距离中央仓库的距离。印度昌迪加尔市被划分为多个区域，并实施了该模型，与现有场景相比，在较短距离内收集更多废物和总垃圾桶覆盖范围方面取得了显著更好的性能。利益相关者可以利用这些结果有效地规划资源，以实现更好的收集实践，这将对环境产生积极影响。
