A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction Red Yeast Rice in the job completion time.As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article.Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered.The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints.
The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized.Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric PS-100 and asymmetric.The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times.