Abstract: In current city sanitation management, the environment is characterized by frequent natural disasters and social emergencies, and the behavior of various entities within city sanitation (residents, sanitation personnel, sanitation robots, etc.), under the attacks from environment, also has various catastrophe phenomena. Under such internal and external attacks, the layout of city waste infrastructure should not only aim at the minimum cost and pollution, but also consider the resilience of layout solutions against internal and external attacks. Thus, nowadays the waste infrastructure layout is a classical multi-objective optimization decision issue.
For this reason, this work proposes a multi-criteria decision method with the integration of optimization theory, simulation analysis and catastrophe analysis. First, a double-objective integer programming model for the waste infrastructure layout with minimum cost and environmental pollution is established, and the Pareto optimal solution set, i.e., multiple alternative solutions of infrastructure layout strategies with minimum cost and environmental pollution, is obtained by using genetic algorithm NSGA-II. Second, according to the operation of the infrastructure layout strategies, i.e., the working process of city waste cleaning and transportation, a multi-agent simulation model for city waste cleaning and transportation is developed using AnyLogic 8.8, including the whole process operations from the residents in various city areas putting out waste, to the transportation and storage at waste transfer stations, and then to the terminal disposal points (including landfills or waste-to-energy incineration plants). The performance of each layout solution is tested under various uncertainty internal and external attacks, and simulation data, e.g., the average amount of waste generation per person, the amount of waste explosion caused by emergencies, the average daily speed of waste collection vehicles, the number of collection vehicles, the average daily speed of transfer vehicles, the number of transfer vehicles, the waste processing capacity at transfer stations, the waste processing capacity at terminal disposal sites, and the total amount of city waste processed, are collected. Finally, cusp catastrophe theory is employed to build the catastrophe model, and analyze the catastrophe hazards of layout solutions under multiple uncertain attacks using the simulation data. Basing on the catastrophe model of the layout of city waste infrastructure, resilience index of each layout solution is calculated and the layout solution with the smallest resilience index is determined as the one with the largest resilience under the various attacks, which represents the multi-objective optimization solution with the minimum cost and pollution, and the maximum resilience. While the other infrastructure layouts of the Pareto optimal solution set are filtered out.
The above proposed method is applied to the case of Wuhan city’s waste infrastructure layout to perform the validation. According to the actual investigation and data collection in Wuhan city, the following data are collected, including the total population of the city, the total annual amount of waste generated in the city, number of candidate landfill sites, number of candidate transfer stations, the capacity of each landfill site and transfer station, the annual fixed cost of landfill site and transfer station, unit transportation cost between population centers and transfer stations and unit transportation cost from transfer station to landfill site. The thirteen double-objective optimization layouts with the minimum cost and pollution are calculated by using genetic algorithm NSGA-II. The simulation analysis is conducted for each layout. The data of input and output of simulation are used to build the catastrophe model and calculate the resilience index. The layout solution 5, i.e., the most resilient layout, is obtained. The comparison of resilience index calculation process with other solutions is performed. It is found that the resilience is related to the number of transfer stations, disposal plants and transportation vehicles. Too much infrastructure can easily be suffered from attacks of multiple concurrent points. The hazards are amplified. While, too lack of infrastructure can lead to the limited capability of processing wastes. When the two pressures accumulate to a certain degree in the system, it will cause the system to be in a highly unstable state that catastrophe may happen at any time.
The integrated innovative method proposed in this work, combining operations research optimization, simulation analysis, and catastrophe analysis, has academic value for multi-objective optimization of infrastructure layout with resilience consideration. The research results have important theoretical and practical significance for the infrastructure layout problem of smart cities, as well as city resilience management under frequent serious environmental and social attacks.
In the next works, optimization models can not only consider minimizing cost and pollution, but also other objectives such as social minimizing dissatisfaction and transportation time. In addition to the nine factors of catastrophe analysis considered in this work, more city sanitation operation factors can also be considered. The proposed method requires further validation through case studies from more cities.

Key words: infrastructure layout, multi-criteria decision-making, multi-agent simulation, catastrophe, resilience

摘要： 当今城市环境具有自然灾害和社会突发事件频发的特点,在频遭环境冲击的条件下,城市垃圾清扫清运基础设施选址,不仅要考虑成本、污染最小,更要考虑选址方案的韧性最大,以抵御环境的冲击,为此本文提出了一种集成运筹优化、仿真分析和突变分析的多准则决策方法。先以成本和污染最小的双目标,构建垃圾基础设施选址整数规划模型,用遗传算法NSGA-II求解得到帕累托最优解集,即多个等效的选址方案;再开发基于基础设施选址的垃圾清运仿真系统,测试每个选址方案在各种环境冲击下的性能;最后针对仿真测试数据,用突变分析方法计算选址方案在各种冲击下的韧性指数,选择韧性能力最大的选址方案,此即为成本和污染最小、韧性最大的多目标优化方案。利用武汉市垃圾基础设施选址案例,检验了本文方法,解释了影响基础设施韧性的机制。研究成果对韧性智慧城市建设具有重要的理论与现实意义。

关键词: 设施选址, 多准则决策, 多智能体仿真, 突变, 韧性