Energy, Water, Food Nexus Decision-Making for Sustainable …
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who assessed the impact of environmental contaminants’ accumulation on crops
quality as a major concern in food security. The study utilised a neural network to
predict the contaminants concentration factor in plants [100].
4.3 Optimisation Models
While simulation models are capable of describing real-life systems with all their
interacting stakeholders and entities, risk quantification and assessment techniques
offer an opportunity to account for all uncertainties that disturb systems and reduce
their efficiencies. Alternatively, optimisation models are used to enhance the overall
performance of systems. They are usually used as a complementary tool to the simulation models, such that the output from the simulation is used as an input in the
optimisation [101]. Optimisation is a widely used technique in decision-making as it
can accommodate all decision levels, counting the strategic, tactical and operational
offering a promising opportunity to solve problems in multifaceted food systems [38,
102]. Optimisation is primarily used to improve the performance of any system/sector
by enabling the selection of the best solution that maximises benefits and minimises
losses. It is a mathematical representation of a problem requiring the selection of
an alternative amongst a set of choices given a set of constraints [103]. Throughout
the history of its application, optimisation models have demonstrated their effectiveness in addressing modern resource management issues through holistically tackling
their challenges. In the context of the EWF nexus systems and the food security
targets, optimisation has assisted in alleviating the multi-dimensional challenges
of food systems. It was used to balance the diverse objectives by means of multiobjective optimisation. For instance, Namany et al. developed a framework based on
a multi-objective optimisation model to determine the optimal energy and water mix
to maximise food self-sufficiency, while minimising economic and environmental
costs [104]. Similarly, Al-Thani et al. adopted the same method to maximise both the
self-sufficiency for some specific food categories along with their nutritional value,
while considering some predetermined water and energy amounts [65]. Optimisation techniques are also widely used in instances where the system is undergoing
uncertainties. For short-term risks, Karan et al. suggested a stochastic optimisation
model that minimises the capital and operating costs of a greenhouse considering
stochasticity in energy supply [105]. For long-term risks, Beh et al. proposed an
approach based on robust optimisation to account for deep uncertainty affecting a
system for water supply. Strong uncertainty is due to variability in climatic conditions and changes in population dynamics [106]. As mentioned previously, resources
and food systems involve multiple sub-systems and entities governed by multiple
stakeholders. Quite often, stakeholders have different visions and goals that might
create unhealthy competition and negatively influence the performance of systems.
To transform the emerging competition into a collaborative cooperation, cooperative
game theoretic approaches are used as part of optimisation models. Namany et al.
proposed a linear programming model with a Stackelberg game theoretic approach
209
who assessed the impact of environmental contaminants’ accumulation on crops
quality as a major concern in food security. The study utilised a neural network to
predict the contaminants concentration factor in plants [100].
4.3 Optimisation Models
While simulation models are capable of describing real-life systems with all their
interacting stakeholders and entities, risk quantification and assessment techniques
offer an opportunity to account for all uncertainties that disturb systems and reduce
their efficiencies. Alternatively, optimisation models are used to enhance the overall
performance of systems. They are usually used as a complementary tool to the simulation models, such that the output from the simulation is used as an input in the
optimisation [101]. Optimisation is a widely used technique in decision-making as it
can accommodate all decision levels, counting the strategic, tactical and operational
offering a promising opportunity to solve problems in multifaceted food systems [38,
102]. Optimisation is primarily used to improve the performance of any system/sector
by enabling the selection of the best solution that maximises benefits and minimises
losses. It is a mathematical representation of a problem requiring the selection of
an alternative amongst a set of choices given a set of constraints [103]. Throughout
the history of its application, optimisation models have demonstrated their effectiveness in addressing modern resource management issues through holistically tackling
their challenges. In the context of the EWF nexus systems and the food security
targets, optimisation has assisted in alleviating the multi-dimensional challenges
of food systems. It was used to balance the diverse objectives by means of multiobjective optimisation. For instance, Namany et al. developed a framework based on
a multi-objective optimisation model to determine the optimal energy and water mix
to maximise food self-sufficiency, while minimising economic and environmental
costs [104]. Similarly, Al-Thani et al. adopted the same method to maximise both the
self-sufficiency for some specific food categories along with their nutritional value,
while considering some predetermined water and energy amounts [65]. Optimisation techniques are also widely used in instances where the system is undergoing
uncertainties. For short-term risks, Karan et al. suggested a stochastic optimisation
model that minimises the capital and operating costs of a greenhouse considering
stochasticity in energy supply [105]. For long-term risks, Beh et al. proposed an
approach based on robust optimisation to account for deep uncertainty affecting a
system for water supply. Strong uncertainty is due to variability in climatic conditions and changes in population dynamics [106]. As mentioned previously, resources
and food systems involve multiple sub-systems and entities governed by multiple
stakeholders. Quite often, stakeholders have different visions and goals that might
create unhealthy competition and negatively influence the performance of systems.
To transform the emerging competition into a collaborative cooperation, cooperative
game theoretic approaches are used as part of optimisation models. Namany et al.
proposed a linear programming model with a Stackelberg game theoretic approach