Water resources management decision-making under stochastic uncertainty using a firefly algorithm-driven simulation-optimization approach for generating alternatives

Abstract

In solving complex water resources management (WRM) problems, it can prove preferable to create numerous quantifiably good alternatives that provide multiple, disparate perspectives. This is because WRM normally involves complex problems that are riddled with irreconcilable performance objectives and possess contradictory design requirements which are very difficult to quantify and capture when supporting decisions must be constructed. By producing a set of options that are maximally different from each other in terms of their decision variable structures, it is hoped that some of these dissimilar solutions may convey very different perspectives that may serve to address these unmodelled objectives. In environmental planning, this maximally different option production procedure is referred to as modelling-to-generate-alternatives (MGA). Furthermore, many WRM decisionmaking problems contain considerable elements of stochastic uncertainty. This chapter provides a firefly algorithm-driven simulation-optimization approach for MGA that can be used to efficiently create multiple solution alternatives to problems containing significant stochastic uncertainties that satisfy required system performance criteria and yet are maximally different in their decision spaces. This algorithmic approach is both computationally efficient and simultaneously produces a prescribed number of maximally different solution alternatives in a single computational run of the procedure. The effectiveness of this stochastic MGA approach for creating alternatives in “real world”, environmental policy formulation is demonstrated using a WRM case study.