<p>A simple and efficient heuristic is designed for the Traveling Salesman Problem (TSP). A potential solution of a TSP is a permutation of nodes associated with it, and a shuffling of the positions of two nodes in the permutation is known as a swap operation (SO). Using SO, four perturbation rules are designed for searching a neighbor path of any potential path of the salesman. Similarly, cyclic crossover operation is used to design four more perturbation rules. The search process begins with a set of randomly generated potential paths(initial population). In each iteration, one rule is selected from this set of eight rules based on their performance to determine the perturbed (child) population from the parent population. To eliminate redundant paths, the population for the next iteration is selected from the union of parent and child populations. K-opt operation(for K=3) is also used to enhance any stagnant path. The algorithm is tested experimentally using several test problems from TSPLIB. The efficiency and consistency of the algorithm for solving large-sized TSPs are verified. Statistical studies are performed to check the performance of the approach concerning state-of-the-art heuristics, and the superiority of the proposed approach is established. Coordinating internet of things(IoT), GIS(geographic information system), Bluetooth technology, and scheduling strategies of the TSPs, several real-life problems can be dealt efficiently, e.g., emergency medical service, home delivery of online business, disaster management, etc. Using this approach, a model on urban health care for senior citizens is proposed and illustrated. As real-time estimations of different parameters are usually imprecise, proper methodologies are outlined to solve the TSPs involving imprecise cost matrices. The same approaches are used to demonstrate the model with fuzzy and rough data.</p>