Yat-wah Wan

Storage space allocation in container terminals

Container terminals are essential intermodal interfaces in the global transportation network. Efficient container handling at terminals is important in reducing transportation costs and keeping shipping schedules. In this paper, we study the storage space allocation problem in the storage yards of terminals. This problem is related to all the resources in terminal operations, including quay cranes, yard cranes, storage space, and internal trucks. We solve the problem using a rolling-horizon approach. For each planning horizon, the problem is decomposed into two levels and each level is formulated as a mathematical programming model. At the first level, the total number of containers to be placed in each storage block in each time period of the planning horizon is set to balance two types of workloads among blocks. The second level determines the number of containers associated with each vessel that constitutes the total number of containers in each block in each period, in order to minimize the total distance to transport the containers between their storage blocks and the vessel berthing locations. Numerical runs show that with short computation time the method significantly reduces the workload imbalance in the yard, avoiding possible bottlenecks in terminal operations.

Dynamic crane deployment in container storage yards

Storage yards at container terminals serve as temporary buffers for inbound and outbound containers. Rubber tyred gantry cranes (RTGCs) are the most frequently used equipment in yards for container handling. The efficiency of yard operations heavily depends on the productivity of these RTGCs. As the workload distribution in the yard changes over time, dynamic deployment of RTGCs among storage blocks (container stacking areas) is an important issue of terminal operations management. This paper addresses the crane deployment problem. Given the forecasted workload of each block in each period of a day, the objective is to find the times and routes of crane movements among blocks so that the total delayed workload in the yard is minimized. The problem is formulated as a mixed integer programming (MIP) model and solved by Lagrangean relaxation. To improve the performance of this approach, we augment the Lagrangean relaxation model by adding additional constraints and modify the solution procedure accordingly. Computational experiments show that the modified Lagrangean relaxation method generates excellent solutions in short time.

A decision support system for operations in a container terminal

We describe a variety of inter-related decisions made during daily operations at a container terminal. The ultimate goal of these decisions is to minimize the berthing time of vessels, the resources needed for handling the workload, the waiting time of customer trucks, and the congestion on the roads and at the storage blocks and docks inside the terminal; and to make the best use of the storage space. Given the scale and complexity of these decisions, it is essential to use decision support tools to make them. This paper reports on work to develop such a decision support system (DSS). We discuss the mathematical models and algorithms used in designing the DSS, the reasons for using these approaches, and some experimental results.

Rubber tired gantry crane deployment for container yard operation

Container terminals competitiveness is generally measured by the vessel discharging and loading time. The shore crane operation has attracted a number of research works. However, yard operation management has been very much experience based and did not receive much attention until last decade. This paper presents an algorithm and a mathematical model for the optimal yard crane deployment. The potential of the model in optimizing yard crane deployment was tested with a set of real operation data extracted from a major container yard terminal.

A stochastic dynamic traveling salesman problem with hard time windows

Just-in-time (JIT) trucking service, i.e., arriving at customers within specified time windows, has become the norm for freight carriers in all stages of supply chains. In this paper, a JIT pickup/delivery problem is formulated as a stochastic dynamic traveling salesman problem with time windows (SDTSPTW). At a customer location, the vehicle either picks up goods for or delivers goods from the depot, but does not provide moving service to transfer goods from one location to another. Such routing problems are NP-hard in deterministic settings, and in our context, complicated further by the stochastic, dynamic nature of the problem. This paper develops an efficient heuristic for the SDTSPTW with hard time windows. The heuristic is shown to be useful both in controlled numerical experiments and in applying to a real-life trucking problem.

Hongkong International Terminals Gains Elastic Capacity Using a Data-Intensive Decision-Support System

As the flagship of Hutchison Port Holdings (HPH), Hongkong International Terminals (HIT) is the busiest container terminal on the planet. HIT receives over 10,000 trucks and 15 vessels a day, about six million twenty-foot equivalent units (TEUs) a year. HIT makes hundreds of operational decisions a minute. HITs terminal management system, the productivity plus program (3P), optimizes resources throughout the container yard using operations research/management science (OR/MS) techniques and algorithms. It manages such interrelated decisions as how to route container trucks in the yard, where to store arriving containers, how many quay cranes to use for each vessel, how many trucks to assign to each crane, how many yard cranes to assign to each container storage block, and when to schedule incoming trucks for container pickup. As the number of container terminals in Asia grows, competition has become price driven and service driven. HIT realized its future rests not only with moving boxes but with mastering the associated information. This meant developing a decision-support system (DSS) to provide superior and differentiated services by generating optimal decisions, one that is very robust under uncertain arrival times of trucks and vessels. In its 10 years of operation, the implementation of the DSS through 3P has helped HIT to become the worlds most efficient and flexible terminal operator. HIT alone saves US

Algorithms for sensitivity analysis of Markov systems through potentials and perturbation realization

100 million per year. By optimizing internal truck use at its sister terminals, the HPH group saves an additional US

Price Competition of Make-to-Order Firms

54 million per year.

Warehouse location problems for air freight forwarders: a challenge created by the airport relocation

We provide algorithms to compute the performance derivatives of Markov chains with respect to changes in their transition matrices and of Markov processes with respect to changes in their infinitesimal generators. Our algorithms are readily applicable to the control and optimization of these Markov systems, since they are based on analyzing a single sample path and do not need explicit specification of transition matrices, nor infinitesimal generators. Compared to the infinitesimal perturbation analysis, the algorithms have a wider scope of application and require nearly the same computational effort. Numerical examples are provided to illustrate the applications of the algorithms. In particular, we apply one of our algorithms to a closed queueing network and the results are promising.

Capacity competition of make-to-order firms

In recent years, there has been considerable research on price competition in a market where customers are sensitive to production or service delays. Most of these works assume identical firms with only different service speeds (capacities), and find that the firm with the higher speed can usually charge a premium price and take a larger market share. We consider the (non-cooperative) competition of two make-to-order firms. In addition to different service capacities, the competing firms may provide different values of service, and have firm-dependent unit costs of waiting. We obtain sufficient conditions for the existence of a Nash equilibrium, and we characterize the equilibrium analytically for some cases and numerically for some other cases. Our results confirm that the firm with the higher speed of service can usually charge a premium price and does take a larger market share. In addition, we find that the firm with the higher value of service and lower cost of waiting can usually charge a premium price and also take a larger market share. However, we do find cases where the faster (while otherwise identical) firm may charge a lower price, or take a smaller market share, or even generate less profit. In response to an increase in its service capacity, a firm may either raise or cut its optimal price, even though a higher service capacity always leads to a shorter expected waiting time. The firm may either raise or cut its price in response to a hike in its unit waiting cost.