Solving the clustered traveling salesman problem with d-relaxed priority rule
This addresses route optimization for delivery systems with urgency levels, offering a trade-off between cost and priority, but it is incremental as it builds on existing formulations and methods.
The paper tackles the Clustered Traveling Salesman Problem with a Prespecified Order on the Clusters by relaxing priority constraints using a d-relaxed priority rule to balance travel cost and urgency, proposing an improved exact method and a meta-heuristic based on Iterated Local Search, with experimental results demonstrating effectiveness.
The Clustered Traveling Salesman Problem with a Prespecified Order on the Clusters, a variant of the well-known traveling salesman problem is studied in literature. In this problem, delivery locations are divided into clusters with different urgency levels and more urgent locations must be visited before less urgent ones. However, this could lead to an inefficient route in terms of traveling cost. This priority-oriented constraint can be relaxed by a rule called d-relaxed priority that provides a trade-off between transportation cost and emergency level. Our research proposes two approaches to solve the problem with d-relaxed priority rule. We improve the mathematical formulation proposed in the literature to construct an exact solution method. A meta-heuristic method based on the framework of Iterated Local Search with problem-tailored operators is also introduced to find approximate solutions. Experimental results show the effectiveness of our methods.