Energy, Water, Food Nexus Decision-Making for Sustainable …
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4.1 Simulation Models
The food sector, and the EWF nexus framework to which it belongs, is comprised of a
set of systems and sub-systems characterised by a range of operational complexities
owing to the diverse processes they require to function. Furthermore, the environment surrounding these systems is a nest of various risks emerging due to societal,
economic, environmental and geopolitical circumstances that can induce several
volatilities and instabilities [61]. Under these pressures exerted on the food sector
and its associated resource systems (water and energy), which could result in unpredictable events that occur at any given point in time; investing in food technologies
and related projects without prior risk assessment and control methodologies might be
adventurous and could engender undesirable results, such as financial losses and environmental damages. Simulation models mitigate such systemic failures by providing
a reality-like representation of systems and allow for future planning considering
exogenous events. Simulation modelling is defined as the procedure used to virtually represent a real-life problem such us a systemic process in order to predict
its behaviours. It allows for the identification of strengths and bottlenecks in the
system as a means for performance enhancement and contribute towards avoiding
the snowball effect [69]. Simulation models are used to assist policy-makers and
stakeholders in any discipline in the understanding of the output of their decisions
and strategies prior to real-life implementation. It also enables the dynamic interactions and flexible communication between different stakeholders and sectors which
result in maximising payoffs and shared mitigation of uncertainties and risks. Furthermore, simulations models and software, contrary to the physical modelling that are
time consuming and expensive permit the possibility to modify and upgrade the
experiment during the running time of the simulations providing more control over
the system of study. In the context of food security, many simulation modelling
techniques have been used to enhance the performance of the food sector. In the
following sections, three widely used simulation methods will be discussed to illustrate the effectiveness of simulation techniques in providing an opportunity for risk
mitigation as part of the food security target.
4.1.1 Discrete Event Simulation
Discrete event simulation (DES) is an extensively adopted simulation technique
used in instances where the process or operation of a system is structured as a
sequence of events [70]. In DES, the state variables vary at some specific points
in time representing the events, incidentally, any change in the system modelled
using DES occurs only in the advent of an event, and no variations are witnessed
over the entire modelling life-time. State variables, representing the independent variables, are characterised with particular exclusive attributes defining their parameters
and functionalities. Information associated with each attribute is subject to variation
203
4.1 Simulation Models
The food sector, and the EWF nexus framework to which it belongs, is comprised of a
set of systems and sub-systems characterised by a range of operational complexities
owing to the diverse processes they require to function. Furthermore, the environment surrounding these systems is a nest of various risks emerging due to societal,
economic, environmental and geopolitical circumstances that can induce several
volatilities and instabilities [61]. Under these pressures exerted on the food sector
and its associated resource systems (water and energy), which could result in unpredictable events that occur at any given point in time; investing in food technologies
and related projects without prior risk assessment and control methodologies might be
adventurous and could engender undesirable results, such as financial losses and environmental damages. Simulation models mitigate such systemic failures by providing
a reality-like representation of systems and allow for future planning considering
exogenous events. Simulation modelling is defined as the procedure used to virtually represent a real-life problem such us a systemic process in order to predict
its behaviours. It allows for the identification of strengths and bottlenecks in the
system as a means for performance enhancement and contribute towards avoiding
the snowball effect [69]. Simulation models are used to assist policy-makers and
stakeholders in any discipline in the understanding of the output of their decisions
and strategies prior to real-life implementation. It also enables the dynamic interactions and flexible communication between different stakeholders and sectors which
result in maximising payoffs and shared mitigation of uncertainties and risks. Furthermore, simulations models and software, contrary to the physical modelling that are
time consuming and expensive permit the possibility to modify and upgrade the
experiment during the running time of the simulations providing more control over
the system of study. In the context of food security, many simulation modelling
techniques have been used to enhance the performance of the food sector. In the
following sections, three widely used simulation methods will be discussed to illustrate the effectiveness of simulation techniques in providing an opportunity for risk
mitigation as part of the food security target.
4.1.1 Discrete Event Simulation
Discrete event simulation (DES) is an extensively adopted simulation technique
used in instances where the process or operation of a system is structured as a
sequence of events [70]. In DES, the state variables vary at some specific points
in time representing the events, incidentally, any change in the system modelled
using DES occurs only in the advent of an event, and no variations are witnessed
over the entire modelling life-time. State variables, representing the independent variables, are characterised with particular exclusive attributes defining their parameters
and functionalities. Information associated with each attribute is subject to variation