Groundwater management refers to the problem of finding pumping schedules that minimise associated discounted present costs given resource and supply constraints. The costs have 2 components: facility installation costs and energy costs that result from pumping and conveying. Constraints exist in the form of drawdown restrictions, gradient criteria as well as quantitative supply constraints. The search space is highly dimensional since potentially, pumps can be installed at any location to tap the groundwater system. For each of these pumps, the pumping schedule is a time-series of pumping rates. Due to installation costs as well due to the complex nature of the resource, the problem is highly non-dimensional and standard requirements such as continuity and differentiability do not exist.
Various users from differing institutional and economic background access groundwater to satisfy their demand each one pursuing his objective. Hence, optimal groundwater management is intrinsically a multi-objective task.
Here, we present a multi-objective optimisation tool (MatLab) that is based on a genetic evolutionary algorithm that couples to a finite difference Modflow representation of the underlying aquifer. Installation, pumping as well as conveyance costa are taken into account. Third, an adaptive heuristics ensures constraint compliance and moves resp. switches on and off boreholes. Third, the optimisation problem is truly multi-objective and does not rely on the aggregation of the objective vector. With this, we can approximate the Pareto-optimal front of solutions that provides a base for further negotiations to be carried out between the users.