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The overall objective of this paper is to show how a formal decision support method can be used effectively to support a land-use planning problem. Central to our approach is a heuristic algorithm based on a goal-programming/reference-point approach. The algorithm is tested on a small region in the Netherlands. To demonstrate the potential use of the algorithm, a planning problem is defined for this region. An interactive session with a land-use planner is then simulated, to show how feedback from the planner is used to generate a plan in a number of rounds. It is concluded that the approach has potential for the support of land-use problems especially in the first rounds of policy design as long as maps are used to interface between planner and algorithm. It is also shown that computational problems still hinder the achievement of realistic detail in the representation of the plan area.

This study examines the use of spatial optimization techniques for multi-site land-use allocation problems (MLUA). 'Multi-site' refers to the problem of allocating more than one land-use type in an area, which are difficult problems as they involve multiple stakeholders with conflicting goals and objectives. Spatial optimization methods consist of (1) an optimization model and (2) an algorithm to solve the model. This study demonstrates a goal-programming model to solve the MLUA problem. The model is solved using both simulated annealing and genetic algorithms. Special attention has been given to introduce a spatial compactness objective in the model. It is shown that the compactness objectives in the optimization model generate compact patches of the same land use for using both the simulated annealing procedure and the genetic algorithm. In addition, it appears that using the proper settings of the compactness objectives, connectivity between patches of land use is promoted. The method is tested for a fictive study and then demonstrated for a real case study, both measuring 20 × 20 cells. The genetic algorithm generally performs better than simulated annealing in terms of solution time and achieving compactness.

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