Meta-heuristic solution approaches for traveling salesman and traveling repairman problems

The traveling salesman problem (TSP) is a combinatorial optimization problem which has been extensively studied for years. TSP is the problem of creating a Hamiltonian cycle in which each node is visited only once to minimize total distance travelled. The ant colony optimization (ACO) is a meta-heuristic approach for solving optimization problems. In the study, an ACO based algorithm is proposed which utilizes local search heuristics. Proposed algorithm is applied to well-known TSP datasets and then the performance of the approach is discussed according to the results obtained from computations.The travelling repairman problem (TRP) is the problem of finding a Hamiltonian path in which the objective is to minimize total waiting time of all customers that are situated at different locations. Genetic algorithms (GA) are meta-heuristic solution methods which are created by taking inspiration from the evolution process. As a second study, a hybrid algorithm which combines genetic algorithm with a local search heuristic is proposed to solve TRP. Proposed algorithm is applied to a set of instances that have been studied in the literature. Performance of the approach is evaluated according to the results of the computational study.Aim of these studies is to develop efficient and effective algorithms that can be applicable to real life problems to solve large scale TSP and TRP problems.As the third study, a case study about a snow disaster situation based on some assumptions is examined as TSP and TRP. Proposed algorithms are applied to the case and results are discussed.