Dissecting Drayage: An Examination of Structure, Information, and Control in Drayage Operations

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

How Much is Location Information Worth? A Competitive Analysis of the Online Traveling Salesman Problem with Two Disclosure Dates

In this paper we derive the worst-case ratio of an online algorithm for the Traveling Salesman Problem (TSP) with two disclosure dates. This problem, a variant of the online TSP with release dates, is characterized by the disclosure of a job’s location at one point in time followed by the disclosure of that job’s release date at a later point in time. We present an online algorithm for this problem restricted to the positive real number line. We then derive the worst-case ratio of our algorithm and show that it is best-possible in two contexts – the first, one in which the amount of time between the disclosure events and release time are fixed and equal for all jobs; and a second in which the time between disclosure events va

The Value of Inaccurate Advance Time Window Information in a Pick-up and Delivery Problem

We examine different routing strategies to cope with inaccurate time window in- formation in the context of a dynamic pick-up and delivery problem with time windows. Our experiments show that advance information, even if inaccurate, can provide benefits from a planning perspective. We propose a novel stochastic strategy that consistently performs well compared to several benchmark strategies

Strategies for Handling Temporal Uncertainty in Pickup and Delivery Problems with Time Windows

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 real- izations 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

Multi Agent Systems in Logistics: A Literature and State-of-the-art Review

Based on a literature survey, we aim to answer our main question: “How should we plan and execute logistics in supply chains that aim to meet today’s requirements, and how can we support such planning and execution using IT?” Today’s requirements in supply chains include inter-organizational collaboration and more responsive and tailored supply to meet specific demand. Enterprise systems fall short in meeting these requirements The focus of planning and execution systems should move towards an inter-enterprise and event-driven mode. Inter-organizational systems may support planning going from supporting information exchange and henceforth enable synchronized planning within the organizations towards the capability to do network planning based on available information throughout the network. We provide a framework for planning systems, constituting a rich landscape of possible configurations, where the centralized and fully decentralized approaches are two extremes. We define and discuss agent based systems and in particular multi agent systems (MAS). We emphasize the issue of the role of MAS coordination architectures, and then explain that transportation is, next to production, an important domain in which MAS can and actually are applied. However, implementation is not widespread and some implementation issues are explored. In this manner, we conclude that planning problems in transportation have characteristics that comply with the specific capabilities of agent systems. In particular, these systems are capable to deal with inter-organizational and event-driven planning settings, hence meeting today’s requirements in supply chain planning and execution