Computing optimal snowplow route plans using Genetic Algorithms

The road network of a small town is represented by a directed graph. Road junctions are the vertices of this graph and each road segment (which has a length and a priority value) is represented by a directed edge. Priority values are numbers 1, 2, etc. with the assumption that 1 is the highest priority. We seek to compute an optimal route map that begins at a particular vertex (the depot) and covers all the edges at least once and returns to the start vertex. The parameters that we wish to minimize are: the total distance covered (thereby minimizing the deadhead miles), the number of u-turns and priority misplacements. In this paper, we propose a Genetic Algorithms-based solution to compute near-optimal route maps in such a graph. Specifically, we have developed a Java software application that generates route maps that minimize a linear combination of the three parameters. We have experimented with reasonably large graphs and obtained good solutions. These solutions are especially useful in snowplow routing for small towns, as plowing costs consume significant portions of the total municipal budgets of these communities. Most of the route planning is currently done manually and routes have evolved over time by experience. In these times of severe budget stress, route planning using our approach can help in performing this essential service in an efficient manner.