In order to manage and evaluate the agricultural operations, many different simulation methods and tools have been developed and demonstrated . In many cases, the analysis involves the optimisation of a system of operations with the objective of minimising, for example, cost or energy consumption.To this end, optimisation techniques such as linear programming (LP), dynamic programming (DP), integer programming (IP), mixed-integer programming (MIP), or various approximate methods (e.g. genetic algorithms) have been used for modelling and solving such optimisation problems ). LP has been applied in agricultural production planning in both primary and secondary sectors, in various ways, such as in land-to-crop allocation problems, modelling land-use planning targeting on sustainable development, planning sustainable agri-food supply chains, and the economic and environmental evaluation of sustainable farming practises.
In order to manage and evaluate the agricultural operations, many different simulation methods and tools have been developed and demonstrated . In many cases, the analysis involves the optimisation of a system of operations with the objective of minimising, for example, cost or energy consumption.To this end, optimisation techniques such as linear programming (LP), dynamic programming (DP), integer programming (IP), mixed-integer programming (MIP), or various approximate methods (e.g. genetic algorithms) have been used for modelling and solving such optimisation problems ). LP has been applied in agricultural production planning in both primary and secondary sectors, in various ways, such as in land-to-crop allocation problems, modelling land-use planning targeting on sustainable development, planning sustainable agri-food supply chains, and the economic and environmental evaluation of sustainable farming practises.
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