工业工程 ›› 2023, Vol. 26 ›› Issue (1): 162-169.doi: 10.3969/j.issn.1007-7375.2023.01.019

• 系统建模与优化算法 • 上一篇    下一篇

环境友好的农村污水治理网络优化设计

许珂1, 蒋鹏2, 郑美妹1, 刘晓1   

  1. 1. 上海交通大学 机械与动力工程学院,上海 200240;
    2. 四川大学 商学院,四川 成都 610064
  • 收稿日期:2021-07-06 发布日期:2023-03-09
  • 作者简介:许珂(1996—),女,陕西省人,硕士研究生,主要研究方向为选址优化等
  • 基金资助:
    国家自然科学基金面上资助项目 (71673188) ;国家自然科学基金国际合作资助项目 (72061127004)

Environment-Friendly Optimization Design of Rural Wastewater Treatment Networks

XU Ke1, JIANG Peng2, ZHENG Meimei1, LIU Xiao1   

  1. 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. Business School, Sichuan University, Chengdu 610064, China
  • Received:2021-07-06 Published:2023-03-09

摘要: 针对产量小且布局分散的农村生活污水,与污水的收集和处理相对应,建立一个基于聚类和双目标混合整数规划 (BOMIP) 的环境友好型决策框架,运用改进的非支配排序遗传算法 (NSGA-II) 求解模型,得到处理厂选址与管网铺设的相关决策。以长三角某典型村庄的基础数据为例的计算结果表明,改进算法和常规约束法的求解质量相近,但求解速度显著加快。提出的框架及算法能帮助决策者构建经济有效且环境友好的农村污水治理网络。

关键词: 农村污水, 管网优化, 环境负效应, 双目标混合整数规划 (BOMIP), 决策支持, 非支配排序遗传算法 (NSGA-II)

Abstract: Given rural domestic wastewater with small quantity and scattered distribution, an environment-friendly decision framework based on the hybridization of clustering and bi-objective mixed-integer programming (BOMIP) is proposed to plan wastewater treatment networks systematically and efficiently. The clustering corresponds to the wastewater collection stage, while the BOMIP matches the wastewater treatment stage. An improved NSGA-II algorithm is found to solve the model to make decisions related to plants locations and pipes laying. A numerical example verifies the case with the basic data of a typical village in the Yangtze River Delta region. The computational results demonstrate that the solution quality of the improved algorithm is close to that of the conventional constraint method but with less time. The proposed framework and algorithm could help decision-makers to build cost-effective and environment-friendly rural wastewater treatment networks.

Key words: rural wastewater, pipe network optimization, environment disutility, bi-objective mixed-integer programming, decision support, non dominated sorting genetic algorithm-II (NSGA-II)

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