Marjan Herwijnen

A genetic algorithm approach to multiobjective land use planning

This paper describes a class of spatial planning problems in which different land uses have to be allocated across a geographical region, subject to a variety of constraints and conflicting management objectives. A goal programming/reference point approach to the problem is formulated, which leads however to a difficult nonlinear combinatorial optimization problem. A special purpose genetic algorithm is developed for the solution of this problem, and is extensively tested numerically. The model and algorithm is then applied to a specific land use planning problem in The Netherlands. The ultimate goal is to integrate the algorithm into a complete land use planning decision support system.

Multiobjective decision support for land use planning

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.

Using simulated annealing and spatial goal programming for solving a multi site land use allocation problem

Many resource allocation issues, such as land use- or irrigation planning, require input from extensive spatial databases and involve complex decision-making problems. Recent developments in this field focus on the design of allocation plans that utilize mathematical optimization 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, it is demonstrated how both Simulated annealing, a heuristic algorithm, and Goal Programming techniques can be used to solve high-dimensional optimization problems for multi-site land use allocation (MLUA) problems. The optimization models both minimize development costs and maximize spatial compactness of the allocated land use. The method is applied to a case study in The Netherlands.

Map transformation and aggregation methods for spatial decision support

The performance of policy alternatives for environmental management is often represented in maps. These maps are used to compare alternatives and ultimately to select the one preferred. Since most people use reference maps in everyday life, such as road maps or town maps, they are familiar with them and consider them as an appropriate decision support instrument. In practice effective use of maps is a difficult task for many people. Spatial evaluation methods are designed to support this task. Spatial evaluation methods help the decision maker by structuring and simplifying the map representations of the alternatives. This can be done by transformation or aggregation of the information presented in a map. In transformation, maps are transformed or combined in such a way that only the information relevant to the decision is presented. For aggregation, indices are defined that represent certain aggregate features of the map that are relevant to the decision. Spatial evaluation methods presented in this chapter are developed to support the comparison of mapped policy alternatives for the Green Heart of the Netherlands. Seven examples of policy questions are used to illustrate the use of spatial evaluation methods. It is shown that the use of these methods increases understanding of the differences between the alternatives. The spatial evaluation methods used do not provide the decision maker with a single answer, such as which alternative is the best, but result in a series of answers depending on the specific questions asked. All methods are included in a small software package that invites decision makers to use the methods to experiment with their problem.

How to use the DEFINITE demonstration disks

The enclosed two disks contain the demonstration version of the system to support DEcisions on a FINITE set of alternatives (DEFINITE). The on-line help facilities that are available in both the full program as in this demonstration version will provide you with the necessary information while you work through the example. In addition, a short description of the system can be found in Chapter 5 of Janssen (1992), Multiobjective decision support for environmental management, Kluwer Academic Publishers, Dordrecht, ISBN 0-7923-1908-7. All methods included in DEFINITE are described in Chapters 3 and 4 of this book.