Transportation Science

Numerous transportation applications as diverse as capital investment decision-making, vehicle fleet planning, and traffic light signal setting all involve some form of (discrete choice) network design. In this paper, we review some of the uses and limitations of integer programming-based approaches to network design, and describe several discrete and continuous choice models and algorithms. Our objectives are threefold—to provide a unifying view for synthesizing many network design models, to propose a unifying framework for deriving many network design algorithms, and to summarize computational experience in solving design problems. We also show that many of the most celebrated combinatorial problems that arise in transportation planning are specializations and variations of a generic design model. Consequently, the network design concepts described in this paper have great potential application in a wide range of problem settings.

Driven by new laws and regulations concerning the emission of greenhouse gases, carriers are starting to use electric vehicles for last-mile deliveries. The limited battery capacities of these vehicles necessitate visits to recharging stations during delivery tours of industry-typical length, which have to be considered in the route planning to avoid inefficient vehicle routes with long detours. We introduce the electric vehicle-routing problem with time windows and recharging stations (E-VRPTW), which incorporates the possibility of recharging at any of the available stations using an appropriate recharging scheme. Furthermore, we consider limited vehicle freight capacities as well as customer time windows, which are the most important constraints in real-world logistics applications. As a solution method, we present a hybrid heuristic that combines a variable neighborhood search algorithm with a tabu search heuristic. Tests performed on newly designed instances for the E-VRPTW as well as on benchmark instances of related problems demonstrate the high performance of the heuristic proposed as well as the positive effect of the hybridization.

A discrete time model is presented for dynamic traffice assignment with a single destination. Congestion is treated explicitly in the flow equations. The model is a nonlinear and nonconvex mathematical programming problem. A piecewise linear version of the model, with additional assumptions on the objective function, can be solved for a global optimum using a one-pass simplex algorithm—branch-and-bound is not required. The piecewise linear program has a staircase structure and can be solved by decomposition techniques or compactification methods for sparse matrices.

City logistics aims to reduce the nuisances associated to freight transportation in urban areas while supporting their economic and social development. The fundamental idea is to view individual stakeholders and decisions as components of an integrated logistics system. This implies the coordination of shippers, carriers, and movements as well as the consolidation of loads of several customers and carriers into the same environment-friendly vehicles. City logistics explicitly aims to optimize such advanced urban transportation systems. We focus on a challenging city logistics planning issue, the integrated short-term scheduling of operations and management of resources, for the general case involving a two-tiered distribution structure. We investigate the main issues related to the problem, introduce a new problem class, propose both a general model and formulations for the main system components, and identify promising solution avenues.

An investigation of the single-vehicle, many-to-many, immediate-request dial-a-ride problem is developed in two parts (I and II). Part I focuses on the “static” case of the problem. In this case, intermediate requests that may appear during the execution of the route are not considered. A generalized objective function is examined, the minimization of a weighted combination of the time to service all customers and of the total degree of “dissatisfaction” experienced by them while waiting for service. This dissatisfaction is assumed to be a linear function of the waiting and riding times of each customer. Vehicle capacity constraints and special priority rules are part of the problem. A Dynamic Programming approach is developed. The algorithm exhibits a computational effort which, although an exponential function of the size of the problem, is asymptotically lower than the corresponding effort of the classical Dynamic Programming algorithm applied to a Traveling Salesman Problem of the same size. Part II extends this approach to solving the equivalent “dynamic” case. In this case, new customer requests are automatically eligible for consideration at the time they occur. The procedure is an open-ended sequence of updates, each following every new customer request. The algorithm optimizes only over known inputs and does not anticipate future customer requests. Indefinite deferment of a customer’s request is prevented by the priority rules introduced in Part I. Examples in both “static” and “dynamic” cases are presented.

We have witnessed recently the development of a fast growing body of research focused on vehicle routing and scheduling problem structures with time window constraints. It is the aim of this paper to survey the significant advances made for the following classes of routing problems with time windows: the single and multiple traveling salesman problem, the shortest path problem, the minimum spanning tree problem, the generic vehicle routing problem, the pickup and delivery problem including the dial-a-ride problem, the multiperiod vehicle routing problem and the shoreline problem. Having surveyed the state-of-the-art in this area, we then offer some perspectives on future research.

Recently, Daganzo introduced the cell transmission model—a simple approach for modeling highway traffic flow consistent with the hydrodynamic model. In this paper, we use the cell transmission model to formulate the single destination System Optimum Dynamic Traffic Assignment (SO DTA) problem as a Linear Program (LP). We demonstrate that the model can obtain insights into the DTA problem, and we address various related issues, such as the concept of marginal travel time in a dynamic network and system optimum necessary and sufficient conditions. The model is limited to one destination and, although it can account for traffic realities as they are captured by the cell transmission model, it is not presented as an operational model for actual applications. The main objective of the paper is to demonstrate that the DTA problem can be modeled as an LP, which allows the vast existing literature on LP to be used to better understand and compute DTA. A numerical example illustrates the simplicity and applicability of the proposed approach.

This paper reviews studies from the past 30 years that use operations research methods to tackle containership routing and scheduling problems at the strategic, tactical, and operational planning levels. These problems are first classified and summarized, with a focus on model formulations, assumptions, and algorithm design. The paper then gives an overview of studies on containership fleet size and mix, alliance strategy, and network design (at the strategic level); frequency determination, fleet deployment, speed optimization, and schedule design (at the tactical level); and container booking and routing and ship rescheduling (at the operational level). The paper further elaborates on the needs of the liner container shipping industry and notes the gap between existing academic studies and industrial practices. Research on containership routing and scheduling lags behind practice, especially in the face of the fast growth of the container shipping industry and the advancement of operations research and computer technology. The purpose of this paper is to stimulate more practically relevant research in this emerging area.

A probabilistic version of the maximal covering location problem is introduced here. The maximum available location problem (MALP) positions p servers in such a way as to maximize the population which will find a server available within a time standard with α reliability. The maximum availability problem builds on the probabilistic location set covering problem in concept and on backup covering and expected covering models in technical detail. MALP bears the same relation to the probabilistic location set covering problem as the deterministic maximal covering problem bears to the deterministic location set covering problem. The maximum availability problem is structured here as a zero–one linear programming problem and solved on a medium-sized transportation network representing Baltimore City.

Acommon problem faced by carriers in liner shipping is the design of their service network. Given a set of demands to be transported and a set of ports, a carrier wants to design service routes for its ships as efficiently as possible, using the underlying facilities. Furthermore, the profitability of the service routes designed depends on the paths chosen to ship the cargo. We present an integrated model, a mixed-integer linear program, to solve the ship-scheduling and the cargo-routing problems, simultaneously. The proposed model incorporates relevant constraints, such as the weekly frequency constraint on the operated routes, and emerging trends, such as the transshipment of cargo between two or more service routes. To solve the mixed-integer program, we propose algorithms that exploit the separability of the problem. More specifically, a greedy heuristic, a column generation-based algorithm, and a two-phase Benders decomposition-based algorithm are developed, and their computational efficiency in terms of the solution quality and the computational time taken is discussed. An efficient iterative search algorithm is proposed to generate schedules for ships. Computational experiments are performed on randomly generated instances simulating real life with up to 20 ports and 100 ships. Our results indicate high percentage utilization of ships' capacities and a significant number of transshipments in the final solution.