An Evolutionary Approach for Finding Optimal Automatic Vehicle Identification Reader Locations in Transportation Networks


A modified distance-based genetic algorithm is proposed to solve the multi-objective automatic vehicle identification (AVI) reader location problem studied in this paper. The objectives are: (1) minimizing the number of AVI readers, (2) maximizing the coverage of origin-destination (O-D) pairs, and (3) maximizing the number of AVI readings. These three objectives are strategically designed to catch the maximum number of trips covering the maximum number of O-D pairs with the minimum number of AVI readers. In order to study the trade-off among the three objectives, non-dominated solutions are retained and analyzed. The results show that there is a trade-off between the quality (measured by objectives 2 and 3) and cost (measured by objective 1) of coverage.