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This paper examines the value of utilizing dynamic, revenue-maximizing, congestion pricing on a privately operated tolled route, when the only alternative is a highly congested, public, free-access route. Three methods are specified by which revenue can be maximized—through the selection of a fixed percent of users to set the toll price for, through the selection of a fixed level of service on the toll road, and through the use of a nonlinear optimization model. These methods are tested on a case study involving a highly congested corridor. In simulation, the corridor’s flow rates are observed following the posting of a toll, the toll is updated, and the new flow observed until a subsequent round of revenue maximizing is applied to the new conditions. It was concluded that in a highly congested corridor a dynamic, revenue-maximizing toll can finance the construction of new capacity without degrading social welfare.

Case studies report that corruption is not easily combated by policy changes, and that reform policies can have unexpected side-effects in practice. Using agent-based simulation, this paper studies potential anti-corruption policies in the maritime customs context. We detail an agent-based simulation calibrated on the processes of an archetypal Mediterranean container port (where in-group relationships contribute to a situation of endemic corruption), and describe insights gained about the costs and benefits of various reform policies. Results from the simulation model provide commentary on cases reported in the literature, and offer novel evaluation of policies involving process re-engineering.

In most real-world settings, a transportation plan requires modifications during execution. A thorough evaluation of transportation planning methods thus requires testing and comparison in a dynamic environment. We give conditions on a simulation environment that follow from this requirement, and propose a multi-agent simulator meeting these conditions. In addition, we propose a new measure that captures robustness in such dynamic settings. The multi-agent simulator and the robustness measure are then used to compare three different transportation methods (two multi-agent planners and one online optimization approach) in settings with release time uncertainty and truck breakdown incidents.

Decision support within transport companies should not only use traditional objective functions, but also reason about qualitative effects on all involved actors. We propose a fuzzy logic rule base that can be used in addition to traditional operation research tools to calculate not just optimal solutions, but solutions that are optimal with respect to knowledge about the preferences and long-term effects on customers, employees, and the environment. We propose a fuzzy logic based judge module that is capable of evaluating logistical performance considering all parties involved in the act transporting a container. It is based on measurements of selected key performance indicators that are fuzzified and combined into satisfaction scores of customers, employees and society. Our proposed method not only enables the continuity of the quality of planning by storing and maintaining valuable expert knowledge, but can also explain decisions based on this knowledge.

Port community systems (PCSs) can be defined as holistic, geographically bounded information hubs in global supply chains that primarily serve the interest of a heterogeneous collective of port related companies. These heterogeneous companies often include terminal operators, carriers (ocean, road, and rail), freight forwarders, enforcement agencies (i.e. customs), port authorities, and various lobby groups (including workers’ unions, environmentalists, and other policy makers). Port community systems that bring these diverse parties together in transaction recordkeeping and information sharing can serve to improve the flow of goods. Despite these potential benefits of port community systems it is challenging to find documentation on PCS implementation lessons learned. The process of designing and implementing a PCS can be roughly divided across four stages: project initiation, system analysis and design, implementation and adoption, and maintenance and growth. In this article, we document lessons learned within each life cycle stage as derived from an international scan of PCS deployments.

The term dray dates back to the 14th century when it was used commonly to describe a type of very sturdy sideless cart . In the 1700s the word drayage came into use meaning “to transport by a sideless cart”. Today, drayage commonly refers to the transport of containerized cargo to and from port or rail terminals and inland locations. With the phenomenal growth of containerized freight since the container’s introduction in 1956, the drayage industry has also experienced significant growth. In fact, according to the Bureau for Transportation Statistics, the world saw total maritime container traffic grow to approximately 417 million twenty foot equivalent units (TEUs) in 2006. Unfortunately, the drayage portion of a door-to-door container move tends to be the most costly part of the move. There are a variety of reasons for this disproportionate assignment of costs, including a great deal of uncertainty at the interface of modes. For example, trucks moving containers to and from a port terminal are often uncertain as to how long it will take them to pick up a designated container coming from a ship, from the terminal stack, or from customs. This uncertainty leads to much difficulty and inefficiency in planning a profitable routing for multiple containers in one day. We study this problem from three perspectives using both empirical and theoretical techniques.

The operation of a port, including its customs import processes, is an instance of a complex socio-technical system with multiple stakeholders. In order to provide insight for policy analysis, we posit the appropriateness of agent-based modelling techniques for the simulation of potential anti-corruption policies in the maritime customs context. We outline the design of an agent-based simulation calibrated on the customs processes of an archetypal Mediterranean container port.

Federal laws require regular condition reporting for portions of the national highway system. Most agencies use instrumented vehicles to collect data on and report the required information. The routing of these vehicles, however, is often managed in an ad hoc manner with significant expense. This paper introduces a practical optimization method for designing the routes of pavement data collection equipment. The proposed model requires, as input, only the global positioning system (GPS) coordinates of the segments' start and end points along with a set of viable hotels. The authors tested the model on two real-world instances provided by a pavement testing consultant. The optimized routes show a cost savings of up to 30% relative to the routes generated using current best-practices in the field.

In many real-life routing problems there is more uncertainty with respect to the required timing of the service than with respect to the service locations. We focus on a pickup and delivery problem with time windows in which the pickup and drop-off locations of the service requests are fully known in advance, but the time at which these jobs will require service is only fully revealed during operations. We develop a sample-scenario routing strategy to accommodate a variety of potential time realizations while designing and updating the routes. Our experiments on a breadth of instances show that advance time related information, if used intelligently, can yield benefits. Furthermore, we show that it is beneficial to tailor the consensus function that is used in the sample-scenario approach to the specifics of the problem setting. By doing so, our strategy performs well on instances with both short time windows and limited advance confirmation.

This paper brings forth from the literature a series of learning curve models and evaluates them through the lens of the construction industry. The review suggests that there is still no consensus on which model provides the best fit and predictability for construction data. As such, this paper puts forth a new model that is suitable for the modern construction industry as it accommodates for both mechanization and forgetting. The proposed model is similar to the Wright model (an exponential model of learning), but, through recursion, places more emphasis on recent data. The proposed model shows an error of less than 1% when predicting the cumulative average completion times in three out of four cases examined.

Many full truckload pick-up and delivery problems in the intermodal freight container transport industry can be modeled as Asymmetric Traveling Salesman Problems (ATSPs). Several authors have noted that while ATSPs are NP-hard, some instances are readily solved to optimality in only a short amount of time. Furthermore, the literature contains several references to the Stacker Crane Problem (SCP) as an “easy” problem amidst the ATSPs. We put this hypothesis to test by using statistical methods to build a model relating measurable distance matrix structures to the amount of time required by two existing exact solvers in finding solutions to over 500 ATSP instances. From this analysis we conclude that SCPs are not necessarily easier than other ATSPs, but a special subset of SCPs, termed drayage problems, are more readily solved. We speculate that drayage problems are “easy” because of a comparatively high number of zeros in symmetric locations within the distance matrix. In real-world drayage problems (i.e. the movement of containers a short distance to/from a port or rail terminal), these zeros correspond to the prevalence of jobs originating at or destined to a fixed number of freight terminals.

Freight planning is an increasingly visible topic in the arena of transportation planning. Freight planning metrics and benchmarks can help state agencies select and assess the impact of improvement projects with respect to freight mobility. Given the unique patterns of freight flow, freight metrics are most valuable when derived from freight-specific data. Extraction of timely freight-specific data to support freight planning efforts is not, however, always straightforward. Washington State, the primary focus of this study, has been developing potential methodologies for extracting and applying freight-specific data in benchmarking freight projects. The Freight Mobility Strategic Investment Board (FMSIB) of Washington State deployed intelligent transportation systems (ITS) technologies [Global Positioning Systems (GPS) and automated vehicle identification (AVI)] with the stated goal of collecting freight-specific data. An examination of the data collected for the FMSIB benchmarking test reveals that both sources of data—GPS and AVI—can support the freight planning process. This paper highlights results of the FMSIB study, the comparative accuracy of the two data sources, and the uses of each for planning purposes.

In many real-life routing problems there is more uncertainty with respect to the required timing of the service than with respect to the service locations. We focus on a pickup and delivery problem with time windows in which the pickup and drop-off locations of the service requests are fully known in advance, but the time at which these jobs will require service is only fully revealed during operations. We develop a sample-scenario routing strategy to accommodate a variety of potential time realizations while designing and updating the routes. Our experiments on a breadth of instances show that advance time related information, if used intelligently, can yield benefits. Furthermore, we show that it is beneficial to tailor the consensus function that is used in the sample-scenario approach to the specifics of the problem setting. By doing so, our strategy performs well on instances with both short time windows and limited advance confirmation.