The traveling salesman problem is one of the most famous combinatorial optimization problems and has been intensively studied. Many extensions to the basic problem have also been proposed, with the aim of making the resulting mathematical models as realistic as possible. We present a new extension to the basic problem, where travel times are specified as a range of possible values. This model reflects the intrinsic difficulties of estimating travel times in reality. We apply the robust deviation criterion to drive optimization over the interval data problem so obtained. Some interesting theoretical properties of the new optimization problems are identified and discussed, together with a new mathematical formulation and some exact and heuristic algorithms. Computational experiments are finally presented.
This study analyzes the transportation network of a major rail freight operator in order to obtain a model of delay propagation of trains connecting intermodal terminals. Operational management of a rail freight operator needs to take into account deviations due to unexpected events such as unplanned maintenance, strikes, railroad works, traffic congestion. The dispatcher makes train assignment decisions based on a number of performance indicators and also on the expectancy that a given train, currently delayed, could recover or limit the amount of delay in the future. We have developed a Markov-chain based model in order to evaluate the evolution of train delays as a train visits successive terminals. Our model is based on the examination of a large set of historical data and we show how we can classify different terminals according to their ability either to absorb or to amplify delays.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.