Document Type : Research Paper
Authors
Abstract
Multi-criteria shortest path problems (MSPP) are called as NP-Hard. For MSPPs, a unique solution for optimizing all the criteria simultaneously will rarely exist in reality. Algorithmic and approximation schemes are available to solve these problems; however, the complexity of these approaches often prohibits their implementation on real-world applications. This paper describes the development of a geospatial information system (GIS)-based genetic algorithm (GA) approach to MSPP on simple networks with multiple independent criteria. The GA approach is shown to explore the underlying network space, generate large candidate path sets, and evolve high quality approximations to the optimal MSPP solution(s) adequately.
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