Catálogo de publicaciones - libros

In the last four decades the container as an essential part of a unitload-concept has achieved undoubted importance in international sea freight transportation. With ever increasing containerization the number of seaport container terminals and competition among them have become quite remarkable. Operations are nowadays unthinkable without effective and efficient use of information technology as well as appropriate optimization (operations research) methods. In this paper we describe and classify the main logistics processes and operations in container terminals and present a survey of methods for their optimization.

At automated container terminals, containers are transshipped from one mode of transportation to another. Automated vehicles transport containers from the stack to the ship and vice versa. Two different types of automated vehicles are studied in this paper, namely automated lifting vehicles and automated guided vehicles. An automated lifting vehicle is capable of lifting a container from the ground by itself. An automated guided vehicles needs a crane to receive and deliver a container.

In designing automated container terminals one have to consider the choice for a certain type of equipment. The choice for a certain type of equipment should be made by performing a feasibility and economic analysis on various types of equipment. In this paper, we examine effects of using automated guided vehicles and automated lifting vehicles on unloading times of a ship, with simulation studies. In choosing a certain type of equipment we have considered criteria such as unloading times of a ship, occupancy degrees and the number of vehicles required. 38% more AGVs need to be used than ALVs. From this specific study, we conclude that, by observing only purchasing costs of equipment, ALVs are a cheaper option than AGVs.

To obtain an accurate analysis we have performed a sensitivity analysis. It can be concluded that the design of the terminal and technical aspects of quay cranes impact the number of vehicles required and as a result the choice for a certain type of equipment.

Significant unproductive and costly waiting occurs during AGV (Automated Guided Vehicle) use, both under the CC (Container Crane) and in the blocks compared to that of a manual yard tractor. A possible solution to this problem is that, in the design of ACT (Automated Container Terminals), ALV (Automated Lifting Vehicles), which can load and unload their own containers, be considered as an alternative. In this paper, the objective is to analyze how increases in the use of ALVs rather than AGVs affects the productivity of ACTs. We derived four inferences regarding the cycle time of vehicles and verified their validity in a simulation. A simulation model of an ACT with perpendicular layout was developed and is described in this paper. From the results of the simulation analysis, we determined the savings effect by cycle time and the required number of vehicles. We demonstrated that the ALV is superior to the AGV in both productivity and efficiency principally because the ALV eliminates the waiting time in the buffer zone.

This paper introduces an approach for generating scenarios of sea port container terminals. The scenarios can be used as input data for simulation models. Furthermore, they can be employed as test data for algorithms to solve optimization problems in container terminal logistics such as berth planning and crane scheduling. A scenario consists of arrivals of deep sea vessels, feeder ships, trains, and trucks together with lists of containers to be loaded and unloaded. Moreover, container attributes such as size, empty, reefer, weight, and destination are included. The generator is based on a large number of parameters that allow the user to produce realistic scenarios of any size. The purpose of this paper is to outline the parameters that are important to produce realistic scenarios of high practical relevance and to propose an algorithm that computes scenarios on the basis of these parameters. The generator discussed here has been developed within the simulation project at the HHLA Container-Terminal Altenwerder in Hamburg, Germany. Nevertheless, its structure is general enough to be applied to any other terminal as well.

Busan is one of the busiest seaports in the world where millions of containers are handled every year. The space of the container terminal at the port is so limited that several small container yards are scattered in the city. Containers are frequently transported between the container terminal and container yards, which may cause tremendous traffic problems. The competitiveness of the container terminal may seriously be aggravated due to the increase in logistics costs. Thus, there exist growing needs for developing an efficient fleet management tool to resolve this situation. This paper proposes a new fleet management procedure based on a heuristic tabu search algorithm in a container transportation system. The proposed procedure is aimed at simultaneously finding the minimum fleet size required and travel route for each vehicle while satisfying all the transportation requirements within the planning horizon. The transportation system under consideration is static in that all the transportation requirements are predetermined at the beginning of the planning horizon. The proposed procedure consists of two phases: In phase one, an optimization model is constructed to obtain a fleet planning with minimum vehicle travel time and to provide a lower bound on the fleet size. In phase two, a tabu search based procedure is presented to construct a vehicle routing with the least number of vehicles. The performance of the procedure is evaluated and compared with two existing methods through computational experiments.

In this paper, we consider the problem of allocating space at berth for vessels with the objective of minimizing total weighted flow time. Two mathematical formulations are considered where one is used to develop a tree search procedure while the other is used to develop a lower bound that can speed up the tree search procedure. Furthermore, a composite heuristic combining the tree search procedure and pair-wise exchange heuristic is proposed for large size problems. Finally, computational experiments are reported to evaluate the efficiency of the methods.

This paper discusses a method for scheduling Berth and Quay cranes, which are critical resources in port container terminals. An integer programming model is formulated by considering various practical constraints. A two-phase solution procedure is suggested for solving the mathematical model. The first phase determines the Berthing position and time of each vessel as well as the number of cranes assigned to each vessel at each time segment. The subgradient optimization technique is applied to obtain a near-optimal solution of the first phase. In the second phase, a detailed schedule for each Quay crane is constructed based on the solution found from the first phase. The dynamic programming technique is applied to solve the problem of the second phase. A numerical experiment was conducted to test the performance of the suggested algorithms.

Abeam search algorithm was applied to solve the load-sequencing problem in port container terminals. The algorithm was used to maximize the operational efficiency of transfer cranes and quay cranes (QCs) while satisfying various constraints on stacking containers onto vessels. The load-sequencing problem consisted of two decision-making subproblems. In the first subproblem, a pickup schedule was constructed in which the travel route of a transfer crane (TC) as well as the number of containers it must pick up at each yard-bay are determined. In the second subproblem, the load sequence for individual containers was determined. This study suggested a search scheme in which an algorithm to solve the second subproblem is imbedded into the algorithm for the first subproblem. Numerical experiments using practical data were performed to test the performance of the developed algorithm.

In this paper, we propose a general model for various scheduling problems that occur in container terminal logistics. The scheduling model consists of the assignment of jobs to resources and the temporal arrangement of the jobs subject to precedence constraints and sequence-dependent setup times. We demonstrate how the model can be applied to solve several different real-world problems from container terminals in the port of Hamburg (Germany).We consider scheduling problems for straddle carriers, automated guided vehicles (AGVs), stacking cranes, and workers who handle reefer containers. Subsequently, we discuss priority rule based heuristics as well as a genetic algorithm for the general model. Based on a tailored generator for experimental data, we examine the performance of the heuristics in a computational study. We obtain promising results that suggest that the genetic algorithm is well suited for application in practice.

This paper is concerned with AGV dispatching in seaport container terminals. Special attention is given to multi-load vehicles which can carry more than one container at a time. The characteristics of this complex application environment and the impact on the AGV dispatching problem are analyzed and various solution techniques considered. For practical application within an online logistics control system, a flexible priority rule based approach is developed, making use of an extended concept of the availability of vehicles. For evaluation reasons, this approach is complemented by an alternative MILP formulation. Finally, the performance of the priority rule based approach and the MILP model are analysed for different scenarios with respect to total lateness of the AGVs. The main focus of the numerical investigation is on evaluating the priority rule based approach for single and dual-load vehicles as well as comparing its performance against the MILP modelling approach.