A Comparison of Anticipatory Algorithms for the Dynamic and Stochastic Traveling Salesman Problem

Advances in information technology and telecommunications, together with ever growing amounts of data, offer opportunities for transportation companies to improve the quality of the service that they provide to their customers. This paper explores this issue in the context of a dynamic and stochastic routing problem in which a single, uncapacitated vehicle serves a set of known customers locations. Two solution approaches are explored. One approach, sample-scenario planning, offers the potential for higher quality solutions, but at the expensive of greater computational effort. Anticipatory insertion offers reduced computation and increased managerial ease, but with the potential for reduced solution quality due to restrictions on solution structure. Our results show that anticipatory insertion can often match the quality of sample-scenario planning, particularly when the degree of dynamism is low.