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S. Namany and T. Al-Ansari
across the time of the simulation if any change occurs to the status of parameters. Another central component that constitutes a DES is the resources constituting
the information that are stored and updated across the running time of the simulation. In DES, state variables are passive entities that can only be controlled through
their attributes and associated resources. In the context of decision-making the DES
enables the testing of “what if” scenarios, allowing decision-makers a flexible, efficient and prior assessment of alternatives as means to avoid the risk of potential future
failures in the system due uninformed decisions. In addition, DES is usually applied
in systems requiring complex process modelling and where decisions are often intricate [71]. The food system, one of these complex systems, is a sector where DES
has been extensively used to prevent risks and respond to unexpected events [72].
DES appears frequently in EWF nexus literature as part of efforts to attain food
security targets. Studies in this regard have covered the three essential pillars of food
security with an aim of enhancing food system resilience and stability. Focusing on
crop cultivation, a building block of “food availability”, Van’t Ooster et al. adopted a
DES model to simulate the crops’ cultivation inside greenhouses. The model used is
the Greenhouse Work Simulation (GWorkS), which is used to improve labour conditions for sweet pepper harvesting. The developed dynamic model is used to formulate
technical and economic restrictions that would later help design an innovative labour
conditions for an enhanced cultivation system [73]. The GWorkS model used was
originally developed by the same authors, Van’t Ooster et al., to increase labour and
machine efficiencies in a greenhouse based on mobile rose systems. The purpose of
the model is to test operational scenarios related to labour work and machinery prior
to the implementation. The tailored DES is a generic tool that can accommodate
any greenhouse crop’s harvesting process [74]. Considering another system from
the agricultural sector, Gittins et al. proposed a hybrid framework based on a DES
model to manage livestock farms, the model utilises a combination of simulations
and surveys to model strategies of the farmers. This work comes as a response to
the volatile social and political conditions that have followed the decision for Brexit
decision from the UK. Findings of the research present diverse growth scenarios that
capture the opinions of farmers along with simulated empirical evidences that suggest
the potential technology adjustments that could improve the livestock farming sector
[75]. Supply chain and logistics of food products is also an area where DSE have
contributed to its improvement. For instance, Nilsson used the DES to analyse the
logistics system of reed canary grass and straw, which are plants usually used as
animal forage. However, in this example, the fodder was used as a source of heat for
a water system. The DES model developed provides the optimal mix of technologies, machines, fodder quantities and capacity of storage that ensure the supply of
the forage from the field to the heating plant [76]. In addressing food quality, Van der
Vorst et al. proposed an integrated approach based on a DSE model that enhances
food supply chains and ensures that demands are satisfied while maintaining food
quality and sustainability principles [77]. Lopes et al. focused on the transportation
aspect of food supply chains through developing a DES model that simulates the
transportation network of soybeans export system. Considering the total costs of
the process, results of this study provided decision-making insights related to the
S. Namany and T. Al-Ansari
across the time of the simulation if any change occurs to the status of parameters. Another central component that constitutes a DES is the resources constituting
the information that are stored and updated across the running time of the simulation. In DES, state variables are passive entities that can only be controlled through
their attributes and associated resources. In the context of decision-making the DES
enables the testing of “what if” scenarios, allowing decision-makers a flexible, efficient and prior assessment of alternatives as means to avoid the risk of potential future
failures in the system due uninformed decisions. In addition, DES is usually applied
in systems requiring complex process modelling and where decisions are often intricate [71]. The food system, one of these complex systems, is a sector where DES
has been extensively used to prevent risks and respond to unexpected events [72].
DES appears frequently in EWF nexus literature as part of efforts to attain food
security targets. Studies in this regard have covered the three essential pillars of food
security with an aim of enhancing food system resilience and stability. Focusing on
crop cultivation, a building block of “food availability”, Van’t Ooster et al. adopted a
DES model to simulate the crops’ cultivation inside greenhouses. The model used is
the Greenhouse Work Simulation (GWorkS), which is used to improve labour conditions for sweet pepper harvesting. The developed dynamic model is used to formulate
technical and economic restrictions that would later help design an innovative labour
conditions for an enhanced cultivation system [73]. The GWorkS model used was
originally developed by the same authors, Van’t Ooster et al., to increase labour and
machine efficiencies in a greenhouse based on mobile rose systems. The purpose of
the model is to test operational scenarios related to labour work and machinery prior
to the implementation. The tailored DES is a generic tool that can accommodate
any greenhouse crop’s harvesting process [74]. Considering another system from
the agricultural sector, Gittins et al. proposed a hybrid framework based on a DES
model to manage livestock farms, the model utilises a combination of simulations
and surveys to model strategies of the farmers. This work comes as a response to
the volatile social and political conditions that have followed the decision for Brexit
decision from the UK. Findings of the research present diverse growth scenarios that
capture the opinions of farmers along with simulated empirical evidences that suggest
the potential technology adjustments that could improve the livestock farming sector
[75]. Supply chain and logistics of food products is also an area where DSE have
contributed to its improvement. For instance, Nilsson used the DES to analyse the
logistics system of reed canary grass and straw, which are plants usually used as
animal forage. However, in this example, the fodder was used as a source of heat for
a water system. The DES model developed provides the optimal mix of technologies, machines, fodder quantities and capacity of storage that ensure the supply of
the forage from the field to the heating plant [76]. In addressing food quality, Van der
Vorst et al. proposed an integrated approach based on a DSE model that enhances
food supply chains and ensures that demands are satisfied while maintaining food
quality and sustainability principles [77]. Lopes et al. focused on the transportation
aspect of food supply chains through developing a DES model that simulates the
transportation network of soybeans export system. Considering the total costs of
the process, results of this study provided decision-making insights related to the