Primal-Dual 2-Approximation Algorithm for the Monotonic Multiple Depot Heterogeneous Traveling Salesman Problem

We study a Multiple Depot Heterogeneous Traveling Salesman Problem (MDHTSP) where the cost of the traveling between any two targets depends on the type of the vehicle. The travel costs are assumed to be symmetric, satisfy the triangle inequality, and are monotonic, i.e., the travel costs between any two targets monotonically increases with the index of the vehicles. Exploiting the monotonic structure of the travel costs, we present a 2-approximation algorithm based on the primal-dual method

Combinatorial Path Planning for a System of Multiple Unmanned Vehicles

In this dissertation, the problem of planning the motion of m Unmanned Vehicles (UVs) (or simply vehicles) through n points in a plane is considered. A motion plan for a vehicle is given by the sequence of points and the corresponding angles at which each point must be visited by the vehicle. We require that each vehicle return to the same initial location(depot) at the same heading after visiting the points. The objective of the motion planning problem is to choose at most q(≤ m) UVs and find their motion plans so that all the points are visited and the total cost of the tours of the chosen vehicles is a minimum amongst all the possible choices of vehicles and their tours. This problem is a generalization of the wellknown Traveling Salesman Problem (TSP) in many ways: (1) each UV takes the role of salesman (2) motion constraints of the UVs play an important role in determining the cost of travel between any two locations; in fact, the cost of the travel between any two locations depends on direction of travel along with the heading at the origin and destination, and (3) there is an additional combinatorial complexity stemming from the need to partition the points to be visited by each UV and the set of UVs that must be employed by the mission. In this dissertation, a sub-optimal, two-step approach to motion planning is presented to solve this problem:(1) the combinatorial problem of choosing the vehicles and their associated tours is based on Euclidean distances between points and (2) once the sequence of points to be visited is specified, the heading at each point is determined based on a Dynamic Programming scheme. The solution to the first step is based on a generalization of Held-Karp’s method. We modify the Lagrangian heuristics for finding a close sub-optimal solution. In the later chapters of the dissertation, we relax the assumption that all vehicles are homogenous. The motivation of heterogenous variant of Multi-depot, Multiple Traveling Salesmen Problem (MDMTSP) derives form applications involving Unmanned Aerial Vehicles (UAVs) or ground robots requiring multiple vehicles with different capabilities to visit a set of locations

Approximation Algorithms and Heuristics for a Heterogeneous Traveling Salesman Problem

Unmanned Vehicles (UVs) are developed for several civil and military applications. For these applications, there is a need for multiple vehicles with different capabilities to visit and monitor a set of given targets. In such scenarios, routing problems arise naturally where there is a need to plan paths in order to optimally use resources and time. The focus of this thesis is to address a basic optimization problem that arises in this setting. We consider a routing problem where some targets have to be visited by specific vehicles. We approach this problem by dividing the routing into two sub problems: partitioning the targets while satisfying vehicle target constraints and sequencing. We solve the partitioning problem with the help of a minimum spanning tree algorithm. We use 3 different approaches to solve the sequencing problem; namely, the 2 approximation algorithm, Christofide's algorithm and the Lin - Kernighan Heuristic (LKH). The approximation algorithms were implemented in MATLAB. We also developed an integer programming (IP) model and a relaxed linear programming (LP) model in C with the help of Concert Technology for CPLEX, to obtain lower bounds. We compare the performance of the developed approximation algorithms with both the IP and the LP model and found that the heuristic performed very well and provided the better quality solutions as compared to the approximation algorithms. It was also found that the approximation algorithms gave better solutions than the apriori guarantees

On Maximising the Total Information Gain in a Vehicle Routing Problem

Information gain-based approach is frequently used for exploration in human-machine systems where multiple robots aid a remotely located operator in classification. The amount of information and the marginal increment in information gain depends on the time spent (dwell time) by a robot at the POI. If there are multiple POIs to be monitored, not all of them may be simultaneously monitored. In such a case, the information gain must be discounted by the duration between successive revisits to the same POI. Based on the discounted information gain, a robot can adaptively choose the dwell time for each POI to aid the operator in better classification. This thesis develops a mathematical formulation for maximizing the total discounted information gain when monitoring multiple POIs using a human-machine system. In this framework, an interface typically takes multiple POIs as input from the human operator (who often serves as a classifier-in-the-loop) and computes the order in which they should be visited, and the dwell time of robots sent for monitoring at each POI. The underlying technical problem consists of determining the optimal assignment of POIs to visit for each robot, the sequence of POIs to visit by each robot, and the dwell time at each robot. For the single robot case, the problem simplifies to the determination of last two sets of variables. In this thesis, the log-concavity of total discounted information gain is exploited to show that the optimal routing for the single robot reduces to the determination of optimal tour (using TSP solution), and optimal dwell time through a gradient ascent or equivalent approaches. In the multiple robot case, this thesis presents a partitioning heuristic for POIs based on k-means clustering; once the clusters are determined, a robot is assigned to a cluster to which it belongs, and the resulting problem reduces to the single robot case. Numerical simulations presented corroborate the algorithms developed in this thesis

Heuristics for Routing Heterogeneous Unmanned Vehicles with Fuel Constraints

This paper addresses a multiple depot, multiple unmanned vehicle routing problem with fuel constraints. The objective of the problem is to find a tour for each vehicle such that all the specified targets are visited at least once by some vehicle, the tours satisfy the fuel constraints, and the total travel cost of the vehicles is a minimum. We consider a scenario where the vehicles are allowed to refuel by visiting any of the depots or fuel stations. This is a difficult optimization problem that involves partitioning the targets among the vehicles and finding a feasible tour for each vehicle. The focus of this paper is on developing fast variable neighborhood descent (VND) and variable neighborhood search (VNS) heuristics for finding good feasible solutions for large instances of the vehicle routing problem. Simulation results are presented to corroborate the performance of the proposed heuristics on a set of 23 large instances obtained from a standard library. These results show that the proposed VND heuristic, on an average, performed better than the proposed VNS heuristic for the tested instances

Algorithms for Multiple Vehicle Routing Problems

Surveillance and monitoring applications require a collection of heterogeneous vehicles to visit a set of targets. This dissertation considers three fundamental routing problems involving multiple vehicles that arise in these applications. The main objective of this dissertation is to develop novel approximation algorithms for these routing problems that find feasible solutions and also provide a bound on the quality of the solutions produced by the algorithms. The first routing problem considered is a multiple depot, multiple terminal, Hamiltonian Path problem. Given multiple vehicles starting at distinct depots, a set of targets and terminal locations, the objective of this problem is to find a vertex-disjoint path for each vehicle such that each target is visited once by a vehicle, the paths end at the terminals and the sum of the distances travelled by the vehicles is a minimum. A 2-approximation algorithm is presented for this routing problem when the costs are symmetric and satisfy the triangle inequality. For the case where all the vehicles start from the same depot, a 5/3-approximation algorithm is developed. The second routing problem addressed in this dissertation is a multiple depot, heterogeneous traveling salesman problem. The objective of this problem is to find a tour for each vehicle such that each of the targets is visited at least once by a vehicle and the sum of the distances travelled by the vehicles is minimized. A primal-dual algorithm with an approximation ratio of 2 is presented for this problem when the vehicles involved are ground vehicles that can move forwards and backwards with a constraint on their minimum turning radius. Finally, this dissertation addresses a multiple depot heterogeneous traveling salesman problem when the travel costs are asymmetric and satisfy the triangle inequality. An approximation algorithm and a heuristic is developed for this problem with simulation results that corroborate the performance of the proposed algorithms. All the main algorithms presented in the dissertation advance the state of art in the area of approximation algorithms for multiple vehicle routing problems. This dissertation has its value for providing approximation algorithms for the routing problems that involves multiple vehicles with additional constraints. Some algorithms have constant approximation factor, which is very useful in the application but difficult to find. In addition to the approximation algorithms, some heuristic algorithms were also proposed to improve solution qualities or computation time