Optimal Treated Wastewater Allocation Among Stakeholders Based on an Agent-based Approach

被引:0
|
作者
Nafiseh Bahrami
Mohammad Reza Nikoo
Ghazi Al-Rawas
Khalifa Al-Jabri
Amir H. Gandomi
机构
[1] Iran University of Science and Technology,School of Civil Engineering
[2] Sultan Qaboos University,Department of Civil and Architectural Engineering
[3] University of Technology Sydney,Faculty of Engineering and IT
来源
关键词
Treated wastewater allocation; Agent-based modeling (ABM); Conflict resolution; R-method; Multi-criteria decision making (MCDM);
D O I
暂无
中图分类号
学科分类号
摘要
Using unconventional water resources, such as treated wastewater (TWW), is an excellent alternative to meet excess water demands. Policymakers should consider optimal and equitable allocation of TWW to relieve conflicts among stakeholders. In the current research, an agent-based model (ABM) is integrated with a multi-objective optimization method (MOM) to fairly distribute water among different beneficiaries in Tehran Province, Iran. In ABM there are two groups of agents: water users and managers. Water users seek to minimize water shortages, and water managers are responsible for allocating water to the users fairly. Managers also assess different bankruptcy scenarios (BSs) for allocating TWW to each stakeholder, and the most agreeable scenario is selected. The Conditional Value-at-Risk (CVaR)-based objective functions are used to assess the risk of uncertainties under different confidence levels. Then, to prioritize the Pareto-optimal solutions, a novel multi-criteria decision-making (MCDM) method, named R-method, is utilized. Results show that considering stakeholders’ objectives and interactions can lead to finding a more equitable solution. Interactions among beneficiaries can diminish water shortages in the study area through an investment by the industrial sector in the agricultural sector to improve the efficiency of agricultural activities.
引用
收藏
页码:135 / 156
页数:21
相关论文
共 50 条
  • [21] Impact of water allocation oversight in irrigation systems: an agent-based model approach
    Gomes, Yan Ranny Machado
    Souza, Christopher Freire
    da Cunha, Augusto Hugo Farias
    Montenegro, Suzana Maria Gico Lima
    RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, 2023, 28
  • [22] An agent-based and market-oriented approach to distributed ISR resource allocation
    Applin, D
    Coleman, P
    McCoy, P
    Rouff, C
    2002 IEEE AEROSPACE CONFERENCE PROCEEDINGS, VOLS 1-7, 2002, : 2771 - 2780
  • [23] An agent-based model for water allocation optimization and comparison with the game theory approach
    Noori, Mahsa
    Emadi, Alireza
    Fazloula, Ramin
    WATER SUPPLY, 2021, 21 (07) : 3584 - 3601
  • [24] Incorporating Bayesian learning in agent-based simulation of stakeholders' negotiation
    Pooyandeh, Majeed
    Marceau, Danielle J.
    COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2014, 48 : 73 - 85
  • [25] An agent-based approach to ANN training
    Czarnowski, I.
    Jedrzejowicz, P.
    KNOWLEDGE-BASED SYSTEMS, 2006, 19 (05) : 304 - 308
  • [26] An approach to agent-based modeling with Modelica
    Sanz, Victorino
    Bergero, Federico
    Urquia, Alfonso
    SIMULATION MODELLING PRACTICE AND THEORY, 2018, 83 : 65 - 74
  • [27] An agent-based approach for workflow management
    Gou, HM
    Huang, BQ
    Liu, WH
    Ren, SJ
    Li, Y
    SMC 2000 CONFERENCE PROCEEDINGS: 2000 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOL 1-5, 2000, : 292 - 297
  • [28] An agent-based approach to multisensor coordination
    Hodge, L
    Kamel, M
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, 2003, 33 (05): : 648 - 662
  • [29] Agent-based approach for assembly control
    Seliger, G
    Kruetzfeldt, D
    CIRP ANNALS 1999 - MANUFACTURING TECHNOLOGY, 1999, : 21 - 24
  • [30] An agent-based approach to security service
    Shakshuki, E
    Luo, ZH
    Gong, J
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2005, 28 (03) : 183 - 208