A Multiobjective Optimisation Approach for Sustainable Resource Consumption and Production in Food-Energy-Water Nexus

被引:4
|
作者
Okola, Isaac [1 ]
Omullo, Elisha Opiyo [2 ]
Ochieng, Daniel Orwa [2 ]
Ouma, Gilbert [3 ]
机构
[1] KCA Univ, Fac Comp & Informat Management, Nairobi, Kenya
[2] Univ Nairobi, Sch Comp & Informat, Nairobi, Kenya
[3] Univ Nairobi, Inst Climate Change Adaptat, Nairobi, Kenya
来源
2019 IEEE AFRICON | 2019年
关键词
Food-Energy-Water Nexus; Multiobjective Optimization; Agent Based Evolutionary Algorithm; Sustainable Consumption and Production; GENETIC ALGORITHMS; CLIMATE-CHANGE; CHALLENGES; ADAPTATION;
D O I
10.1109/africon46755.2019.9133749
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
People need food, energy and water for survival and prosperity. However, it is a challenge to meet the demand for these necessities because urbanization and climate change impacts are destroying food, energy and water systems. Resource optimization in one sector can undermine security in another due to competition for the same resources. Therefore, multiobjective optimization approaches have been used to understand the complexity associated with the Food, Energy Water Nexus. However most of these approaches focus on either maximizing resource production or minimizing resource consumption in the FEW Nexus. Maximizing production and minimizing consumption of resources simultaneously are two conflicting objectives that have not been well addressed by existing multiobjective optimization approaches. Therefore, it is difficult for these approaches to recognize feedback and cascading effects occurring in the FEW Nexus due to the interdependencies of the food, energy and water. For Sustainability of the FEW Nexus to be achieved, sustainable consumption and production of the resources within it need to be emphasized. Therefore, a novel multiobjective optimization approach that can explore alternative ways of both maximizing production and minimizing consumption simultaneously is proposed. This approach can be implemented using an Agent Based Evolutionary Algorithm as future work.
引用
收藏
页数:5
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