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We consider online versions of the traveling salesman problem (TSP) and traveling repairman problem (TRP) where instances are not known in advance. Cities (points) to be visited are revealed over time, while the server is en route serving previously released requests. These problems are known in the literature as the online TSP (TRP, respectively). The corresponding offline problems are the TSP (TRP) with release dates, problems where each point has to be visited at or after a given release date. In the current literature, the assumption is that a request becomes known at the time of its release date. In this paper we introduce the notion of a request's disclosure date, the time when a city's location and release date are revealed to the server. In a variety of disclosure date scenarios and metric spaces, we give new online algorithms and quantify the value of this advanced information in the form of improved competitive ratios. We also provide a general result on polynomial-time online algorithms for the online TSP. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new heuristic method for the Traveling Salesman Problem with Time Windows, based on the solution of an auxiliary problem. The idea is to solve an assignment problem with an ad hoc objective function to obtain a solution close enough to a feasible solution of the original problem. Given this solution, made by a long main tour containing the depot and few small subtours, it is easy to insert all the subtours into the main path using a greedy insertion procedure. The algorithm described applies the proposed constructive scheme and then uses a local search procedure to improve the initial solution. The computational results show the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Many transportation problems can be formulated as a linearly-constrained convex programming problem whose objective function consists of entropy functions and other cost-related terms. In this paper, we propose an unconstrained convex programming dual approach to solving these problems. In particular, we focus on a class of linearly-constrained entropy maximization problem with quadratic cost, study its Lagrangian dual, and provide a globally convergent algorithm with a quadratic rate of convergence. The theory and algorithm can be readily applied to the trip distribution problem with quadratic cost and many other entropy-based formulations, including the conventional trip distribution problem with linear cost, the entropy-based modal split model, and the decomposed problems of the combined problem of trip distribution and assignment. The efficiency and the robustness of this approach are confirmed by our computational experience. [ABSTRACT FROM AUTHOR]

This paper investigates the accuracy of the pro bit approximation based solution of the spatial aggregation problem of traffic assignment. It tests the normal approximation to uniform intrazonal travel times and the Clark approximation to the solution of the choice probabilities between alternatives characterized by normally distributed travel times. Some guidelines for using the technique are developed. [ABSTRACT FROM AUTHOR]