Bi-level GA and GIS for multi-objective route planning in tourism

Abstract

Route planning is usually carried out to achieve a single objective such as calculating minimum transport costs, the shortest distance, or the shortest time. This thesis explores a novel approach to multi-objective route planning using the Genetic Algorithm (GA) and the Geographical Information Systems (GIS). The method is applied to two cases of sight-seeing itineraries for tourists. These two cases are designed to cover a set of places of interest within a given area. The route planning takes into account four criteria, including travel time, vehicle operating costs, safety and the surrounding scenic view quality. The multi-objective route planning in this research can be viewed as an extension of the classical Travelling Salesman Problem (TSP) since a tourist needs to pass through a list of sight points. The four criteria are quantified using the spatial analytic functions of a GIS from which a generalized cost for each link is calculated.