Optimizing sustainable development problem under uncertainty: Robust vs fuzzy optimization methods

Abstract

Sustainable development problem involves several conflicting criteria on economy, energy, environment and social aspects and some sources of uncertainty due to its attribute. It is necessary to mitigate risks in sustainable development schedule. However, the existing literature includes limited quantitative research on robust strategies for sustainable development under uncertainty. Under these concerns, this paper presents a robust multi-objective optimization formulation for allocating labor across economic sectors to simultaneously satisfy economy (gross domestic product, GDP), environment (greenhouse gas (GHG) emissions), energy (electricity) and society (labor), in which the per capita GDP, per capita electricity consumption, per capita GHG and per capita rate of unemployment are robust uncertainty. There are three conflicting objectives in our sustainable development problem. The first objective maximizes the sectoral GDP, the second objective minimizes the sectoral electricity consumption and the third objective minimizes the sectoral GHG emissions. We adapt-constraint method to deal with the multiple objectives for the sustainable development problem, and obtain a more flexible result via an interactive decision-making process. Since uncertainty has often led to computationally intractable models, we reformulate the proposed model using robust optimization method into the tractable robust counterpart (RC) forms under two types of uncertainty sets. Finally, to demonstrate the effectiveness and applicability for our model, we conduct a case study for meeting year 2030 sustainable development of the United Arab Emirates (UAE). The numerical results show: (a) the robust multi-objective model is effective in uncertain environment and provides a reliable decision tool than deterministic model for integrated multi-objective sustainable development problem; (b) when the possibility distributions of uncertain parameters are available, fuzzy optimization model can provide better decision-making than robust model under box uncertainty set.

The contribution of using Artificial Intelligence in the Sustainable Development

“…[21] described some of threats of using ML techniques in two exemplary selected application areas: smart agriculture and smart health. Furthermore, [22] tried to compare the fuzzy optimization model and robust model decision-making for the sustainable development .It is clear that many works have tried to provide practical and conceptual frameworks to do adopting of ML techniques in the industry especially in the supply chain area, is the case of authors in [23] and [24]. Many works tried to analyze directly the use of Data Analytics in the achievement of Sustainable development Goals , as it was the case of [25].…”