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Continuous location models generally use the Euclidean or rectangular norm to approximate travel in a transportation network. This paper considers a new and more accurate distance measure, termed the weighted one-two norm, which is a positive linear combination of the preceding norms. A directional bias function is introduced to show the equivalence of this distance measure to the well known l[sub p] norm. We then formulate a simple linear regression model to fit the parameters of our distance function to a given data set. Some novel applications based on standard statistical tests are derived which provide practical insights into the nature of networks with rectangular bias. The results are readily extended to other types of networks. [ABSTRACT FROM AUTHOR]

International shipping is a multibillon dollar business and shipping companies may expect large benefits from improving the routing and scheduling processes of their ships. In this paper, we describe a container-ship routing scenario in which a shipping company provides services to a network of ports. We formulate a mathematical programming model that maximizes total profit (i.e., revenue minus operating costs) for multiple ships and determines: (a) the optimal sequence of ports of call for each ship, (b) the number of trips each ship makes in a planning horizon, and (c) the amount of cargo transported between any two ports by each ship. The model contains discrete, 0-1 and continuous variables, and nonlinear complicating constraints. The multiple container ship model is quite different from those of vehicle routing and traveling salesman problems. We use a decomposition method for the model as well as for the network in order to solve the problem. Several problems on 10- to 20-port networks are solved and the results presented. [ABSTRACT FROM AUTHOR]

The paper introduces a cutting-plane matheuristic for the design-balanced capacitated multicommodity network design problem, one of the premier formulations for the service network design problem with asset management concerns increasingly faced by carriers within their tactical planning processes. The matheuristic combines a cutting-plane procedure efficiently computing tight lower bounds and a variable-fixing procedure feeding a MIP solver. Learning mechanisms embedded into the cutting-plane procedure provide the means to identify promising variables and thus both reduce the dimension of the problem instance, making it addressable by a MIP solver, and guide the latter toward promising solution spaces. Extensive computational experiments show the efficiency of the proposed procedures in obtaining high-quality solutions, outperforming the current best methods from the literature. [ABSTRACT FROM AUTHOR]

Modeling of transportation systems must invariably balance behavioral richness and computational tractability. In this paper, we develop a transportation equilibrium model and an algorithm for the simultaneous prediction of trip generation, trip distribution, modal split, and trip assignment on large-scale networks. The model achieves a practical compromise between behavioral and computational aspects of modeling the problem. It is formulated as an equivalent convex optimization problem, yet it is behaviorally richer than other models that can be cast as equivalent convex programs. Although the model is not as behaviorally rich as the most general equilibrium models, it has computational advantages. The applications reported in this paper of the model to real systems, i.e., the intercity transport network of Egypt and the urban transportation network of Austin, Texas, illustrate the computational attractiveness of the approach. These results indicate that equivalent optimization formulations are not as restrictive as previously thought, and hence, the equivalent convex programming approach for modeling and predicting equilibrium on transportation systems remains a viable and fruitful avenue for future consideration. [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 discusses a model for the problem of locating garages in an urban mass transit system and the associated assignment of vehicles to garages for background and peak services of transit lines. The objective of this model is to minimize the sum of fixed site-related costs, variable vehicle costs, and crew relief costs. The model is solved by an iterative procedure involving a location interchange heuristic and a network flow-based algorithm. The implementation of the model is discussed through its various stages, from data preparation to the final policy recommendations. [ABSTRACT FROM AUTHOR]

We consider algorithms proposed for solving the fixed demand user optimized network equilibrium problem with asymmetric user costs. Because the Jacobian matrix for the costs is asymmetric, no known equivalent convex optimization problem exists and alternative solution methods to nonlinear programming techniques must be sought. The purpose of this paper is to compare the performance of several existing methods for determining the equilibrium network flows of a small realistic network model in which intersection controls are incorporated which lead to particularly asymmetric cost functions. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with simultaneous distribution and collection (VRPSDC) is the variation of the capacitated vehicle routing problem that arises when the distribution of goods from a depot to a set of customers and the collection of waste from the customers to the depot must be performed by the same vehicles of limited capacity and the customers can be visited in any order. We study how the branch-and-price technique can be applied to the solution of this problem and in particular we compare two different ways of solving the pricing subproblem: exact dynamic programming and state space relaxation. By applying a bidirectional search we experimentally prove its effectiveness in solving the subproblem. We also devise suitable branching strategies for both the exact and the relaxed approach and we report on an extensive set of computational experiments on benchmark instances with both simple and composite demands. [ABSTRACT FROM AUTHOR]

We consider a bilevel programming formulation of a freight tariff-setting problem where the leader consists in one among a group of competing carriers and the follower is a shipper. At the upper level, the leader's revenue corresponds to the total tariffs levied, whereas the shipper minimizes its transportation cost, given the tariff schedule set by the leader. We propose for this problem a class of heuristic procedures whose relative efficiencies, on small problem instances, could be validated with respect to optimal solutions obtained from a mixed integer reformulation of the mathematical model. We also present numerical results on large instances that could not be solved to optimality by an exact method. [ABSTRACT FROM AUTHOR]