GIS-Based Evolutionary Approaches Using Multiple-Criteria Decision Analysis for Spatial Issues

GIS-Based Evolutionary Approaches Using Multiple-Criteria Decision Analysis for Spatial Issues

Soumaya Elhosni (LTSIRS Laboratory, National Engineering School, Tunis, Tunisia) and Sami Faiz (LTSIRS Laboratory, National Engineering School, Tunis, Tunisia)
Copyright: © 2021 |Pages: 13
DOI: 10.4018/978-1-7998-1954-7.ch003
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Geographic information systems (GIS) have been considered as good decision support tools to provide the decision maker (DM). However, their spatial data functionalities fail to provide any report about the potentials of the information and cannot make rational choice between conflicting alternatives. Literature review shows that the integration of GIS with multiple-criteria decision analysis (MCDA) makes GIS more robust in decision making process. While MCDA are used to support DMs to deal and solve spatial multi-objective optimisation problems (SMOPs), the use of their methods are suited for eliciting the preferences of small group of stakeholders. Unlike to MCDA, Multi-Objective Evolutionary Algorithms (MOEA) perform well on solving SMOPS conflicting objectives since only one iteration of the algorithm gives rise to a set of trade-off solutions. However, only choosing better compromise doesn't completely solve the problem. Recently, a growing interest in combining MCDA and MOEA techniques has been seen. The chapter approaches the idea of integration of GIS, MOEA, and MCDA to solve SMOP.
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Literature Review

Spatial optimization uses mathematics to find solutions to spatial decision problems that seek the best arrangement of locations under strictly defined conditions. It is a branch of spatial MCDA, which focus on designing a collection of spatial options and judging how close each of these options satisfies a given set of objectives (Malczewski, 1999). Two broad types of problem are considered. First, the decision may involve choosing a location for a particular activity or facility. Second, the problem may focus on selecting an activity for given locations. Either way, space is the essential component of the decision problem, and the best solution offers the optimal design of space under given conditions.

Garrison in (Garrison, 1959) presented the formalization of methods and concepts of spatial optimization in geography, the writer used linear programming for locating manufacturing plants and solving freight transportation problems between delivery and receiving sites. Through the literature there are also other important publications on spatial optimization, naming a few (Church & Murray, 2009; Gorry & Scott Morton, 1971; Haggett et al., 1977; Harvey et al., 1974; Tong & Murray, 2012). In this section, we present the literature review related to GIS-MOEA integration and to MOEA-MCDA integration research fields.

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