Using simulated annealing for resource allocation

Abstract

Many resource allocation issues, such as land use- or irrigation planning, require input from extensive spatial databases and involve complex decisionmaking problems. Spatial decision support systems (SDSS) are designed to make these issues more transparent and to support the design and evaluation of resource allocation alternatives. Recent developments in this field focus on the design of allocation plans that utilise mathematical optimisation techniques. These techniques, often referred to as multi-criteria decision-making (MCDM) techniques, run into numerical problems when faced with the high dimensionality encountered in spatial applications. In this paper we demonstrate how simulated annealing, a heuristic algorithm, can be used to solve high-dimensional non-linear optimisation problems for multi-site land use allocation (MLUA) problems. The optimisation model both minimises development costs and maximises spatial compactness of the land use. Compactness is achieved by adding a non-linear neighbourhood objective to the objective function. The method is successfully applied to a case study in Galicia, Spain, using an SDSS for supporting the restoration of a former mining area with new land use.