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In the berth-allocation problem (BAP) the aim is to optimally schedule and assign ships to berthing areas along a quay. The objective is the minimization of the total (weighted) service time for all ships, defined as the time elapsed between the arrival in the harbor and the completion of handling. Two versions of the BAP are considered: the discrete case and the continuous case. The discrete case works with a finite set of berthing points. In the continuous case ships can berth anywhere along the quay. Two formulations and a tabu search heuristic are presented for the discrete case. Only small instances can be solved optimally. For these sizes the heuristic always yields an optimal solution. For larger sizes it is always better than a truncated branch-and-bound applied to an exact formulation. A heuristic is also developed for the continuous case. Computational comparisons are performed with the first heuristic and with a simple constructive procedure.

PurposeAn integrated queueing network focused on container storage/retrieval operations occurring on the yard of a transshipment hub is proposed. The purpose of the network is to support decisions related to the organization of the yard area, while also accounting for operations policies and times on the quay.Design/methodology/approachA discrete-event simulation model is used to reproduce container handling on both the quay and yard areas, along with the transfer operations between the two. The resulting times, properly estimated by the simulation output, are fed to a simpler queueing network amenable to solution via algorithms based on mean value analysis (MVA) for product-form networks.FindingsNumerical results justify the proposed approach for getting a fast, yet accurate analytical solution that allows carrying out performance evaluation with respect to both organizational policies and operations management on the yard area.Practical implicationsPractically, the expected performance measures on the yard subsystem can be obtained avoiding additional time-expensive simulation experiments on the entire detailed model.Originality/valueAs a major takeaway, deepening the MVA for generally distributed service times has proven to produce reliable estimations on expected values for both user- and system-oriented performance metrics.

The quay crane scheduling problem (QCSP) is at the basis of a major logistic process in maritime container terminals: the process of discharging/loading containers from/on berthed vessels. Several groups of containers, laying in one or more stowage portions of a containership, have to be assigned to multiple cranes and discharge/loading operations have to be optimally sequenced, under some complicating constraints imposed by the practical working rules of quay cranes. The QCSP has been the object of a great deal of research work since the last decade and it is focused in this paper, with the aim of consolidating a promising solution approach based upon the combination of specialized branch & bound (B&B) and heuristic algorithms. A cost-effective solution technique that incorporates the local branching method within a refined B&B algorithm is proposed and its effectiveness is assessed by numerical comparisons against the latest algorithm available in literature.

The discharge/loading process of a single container ship by multiple quay cranes and shuttle vehicles moving back and forth from the quay to the yard and vice versa is focused in this paper. The core problem of this major operational issue reduces to finding the optimal assignment and optimal sequencing (schedule) of bays (jobs) processed by a fixed number of available cranes (machines). Under the classical assumption that machines have no release time and that their processing occurs with continuity, at a constant rate, in literature it has been tackled as a deterministic machine scheduling problem and formulated by integer programming as the quay crane scheduling problem (QCSP). Here, instead, the QCSP is viewed as a decisional step within an uncertain and dynamic logistic process where the quay cranes are the resources to be managed at the best, i.e., by minimizing the time spent waiting for each other due to conflicts, as well as the time wasted for blocking and starvation phenomena due to congestion occurring along the path from the quay area and to the stacking yard and vice versa. We present a simulation-based optimization (SO) model for this wider modeling problem with the objective of finding the schedule which optimizes a classical objective function. The search process for the optimal schedule is accomplished by a simulated annealing (SA) algorithm, while performance estimation of the overall container discharge/loading process is provided by the simulation framework as a whole. Numerical experiments on a real instance are presented for tuning purposes of the SA procedure implemented within the simulator.

In a terminal container the yard management is a critical and dynamic component that requires governance and flexibility to adapt to and address even the most complex issues arising out of yard planning and operations. With particular reference to the assignment and deployment of Rubber Tired Gantry Cranes among yard blocks, an optimisation model is proposed. Its purpose is to determine the block pairs between which yard cranes will be transferred during the period under examination in order to satisfy the crane capacity requirements and minimise the total cost for block matching and crane activation. The use of this stand-alone optimisation approach is then extended by introducing the architecture of an integrated framework, which includes both optimisation and simulation techniques and is based on an all-inclusive queuing network representing the main subsystems of a terminal. During scenario analysis, the framework is designed to evaluate which policy for assigning yard cranes to yard blocks is best for meeting the dynamic and constantly updating requirements of the yard subsystem. To this purpose, some Ranking and Selection (R&S) techniques are considered. The ongoing numerical experiments mean to demonstrate how a newly proposed R&S procedure is sufficiently robust for use in practice.

This paper addresses the problem of optimizing a function over a finite or countable infinite set of alternatives, whenever the objective function cannot be evaluated exactly, but has to be estimated via simulation. We present an iterative method, based on the simulated annealing framework, for solving such discrete stochastic optimization problems. In the proposed method, we combine the robustness of this metaheuristic method with a statistical procedure for comparing the solutions that are generated. The focus of our work is on devising an effective procedure rather than addressing theoretical issues. In fact, in our opinion, although significant progresses have been made in studying the convergence of a number of simulation-optimization algorithms, at present there is no procedure able to consistently provide good results in a reasonable amount of time. In addition, we present a parallelization strategy for allocating simulation runs on computing resources. [PUBLICATION ABSTRACT]

In the berth-allocation problem (BAP) the aim is to optimally schedule and assign ships to berthing areas along a quay. The objective is the minimization of the total (weighted) service time for all ships, defined as the time elapsed between the arrival in the harbor and the completion of handling. Two versions of the BAP are considered: the discrete case and the continuous case. The discrete case works with a finite set of berthing points. In the continuous case ships can berth anywhere along the quay. Two formulations and a tabu search heuristic are presented for the discrete case. Only small instances can be solved optimally. For these sizes the heuristic always yields an optimal solution. For larger sizes it is always better than a truncated branch-and-bound applied to an exact formulation. A heuristic is also developed for the continuous case. Computational comparisons are performed with the first heuristic and with a simple constructive procedure. Reprinted by permission of the Institute for Operations Research and Management Science (INFORMS)

Within a maritime container terminal, we refer to the operations required to reposition containers in the storage yard as housekeeping. The objective of housekeeping operations, which are typically performed a few hours prior to boarding time, is to accelerate container loading on outgoing vessels. In this paper, we describe our experiences in modeling and simulating the housekeeping process and integrating the related simulation tool into the existing container management system at Medcenter Container Terminal SpA (MCT), the company that runs the container terminal at the port of Gioia Tauro, Italy. MCT planners use this operations research tool to perform quantitative analysis of different scenarios to evaluate alternative policies and rules for housekeeping; hence, they can provide more rapid container loading for shipping companies.