Seaside operations in container terminals: literature overview, trends, and research directions

Carlo, Héctor J.; Vis, Iris F. A.; Roodbergen, Kees Jan

doi:10.1007/s10696-013-9178-3

Cited by 151 publications

(97 citation statements)

References 89 publications

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“…The literature review presented herein mainly focuses on the solution algorithms that were designed to solve the BSP. For a more detailed description of the BSP papers, this study refers to Meisel (2010, 2015) and Carlo et al (2013). An overview of the collected BSP papers is presented next.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Dulebenets

2017

MABR

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Purpose The volumes of international containerized trade substantially increased over the past years. In the meantime, marine container terminal (MCT) operators are facing congestion issues at their terminals because of the increasing number of large-size vessels, the lack of innovative technologies and advanced handling equipment and the inability of proper scheduling of the available resources. This study aims to propose a novel memetic algorithm with a deterministic parameter control to facilitate the berth scheduling at MCTs and minimize the total vessel service cost. Design/methodology/approach A local search heuristic, which is based on the first-come-first-served policy, is applied at the chromosomes and population initialization stage within the developed memetic algorithm (MA). The deterministic parameter control strategy is implemented for a custom mutation operator, which alters the mutation rate values based on the piecewise function throughout the evolution of the algorithm. Performance of the proposed MA is compared with that of the alternative solution algorithms widely used in the berth scheduling literature, including a MA that does not apply the deterministic parameter control strategy, typical evolutionary algorithm, simulated annealing and variable neighborhood search. Findings Results demonstrate that the developed MA with a deterministic parameter control can obtain superior berth schedules in terms of the total vessel service cost within a reasonable computational time. Furthermore, greater cost savings are observed for the cases with high demand and low berthing capacity at the terminal. A comprehensive analysis of the convergence patterns indicates that introduction of the custom mutation operator with a deterministic control for the mutation rate value would provide more efficient exploration and exploitation of the search space. Research limitations/implications This study does not account for uncertainty in vessel arrivals. Furthermore, potential changes in the vessel handling times owing to terminal disruptions are not captured. Practical implications The developed solution algorithm can serve as an efficient planning tool for MCT operators and assist with efficient berth scheduling for both discrete and continuous berthing layout cases. Originality/value The majority of studies on berth scheduling rely on the stochastic search algorithms without considering the specific problem properties and applying the guided search heuristics. Unlike canonical evolutionary algorithms, the developed algorithm uses a local search heuristic for the chromosomes and population initialization and adjusts the mutation rate values based on a deterministic parameter control strategy for more efficient exploration and exploitation of the search space.

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Section: Literature Reviewmentioning

confidence: 99%

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Dulebenets

2017

MABR

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“…Finally, we use the instance generator from [11] to generate a dataset of 700 instances. 4 The dataset is similar to the one used in [11], and has 25 instances each with 4 tiers; 4, 7 or 10 stacks; a container fill percentage of 0.5, 0.75 or 1; and a "configuration" of either 0, 1, or 2, which describes the priority distributions for the containers. We call this dataset the EMM dataset.…”

Section: Datasetsmentioning

confidence: 99%

Solving the pre-marshalling problem to optimality with A* and IDA*

2016

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We present a novel solution approach to the container pre-marshalling problem using the A* and IDA* algorithms combined with several novel branching and symmetry breaking rules that significantly increases the number of pre-marshalling instances that can be solved to optimality. A* and IDA* are graph search algorithms that use heuristics combined with a complete graph search to find optimal solutions to problems. The container pre-marshalling problem is a key problem for container terminals seeking to reduce delays of inter-modal container transports. The goal of the container pre-marshalling problem is to find the minimal sequence of container movements to shuffle containers in a set of stacks such that the resulting stacks are arranged according to the time each container must leave the stacks. We evaluate our approach on three well-known datasets of pre-marshalling problem instances, solving over 500 previously unsolved instances to optimality, which is nearly twice as many instances as the current state-of-the-art method solves. e

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“…that are difficult to solve using the exact optimization algorithms [29]. Simulation was used in this study to model MCT operations and to evaluate the proposed YT deployment strategies.…”

Section: Model Descriptionmentioning

confidence: 99%

A New Simulation Model for a Comprehensive Evaluation of Yard Truck Deployment Strategies at Marine Container Terminals

Dulebenets

2016

OSJ

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Taking into account increasing volumes of the international seaborne trade and increasing port congestion, marine container terminal operators have to improve efficiency of their operations in order to provide timely service of vessels and avoid product delivery delays to customers. This paper focuses on improvement of container transfer operations between the seaside and the marshaling yard and proposes five yard truck deployment strategies. Performance of the considered marine container terminal is evaluated under each one of the yard truck deployment strategies via simulation. Different performance indicators are estimated to determine how the suggested yard truck deployment strategies will affect all equipment types, involved in container handling and transfer. Results from the extensive simulation experiments showcase that all five yard truck deployment strategies provide similar values of vessel service and quay crane performance indicators, while the shortest distance based yard truck deployment strategy yields superior gantry crane and yard truck performance indicators. The worst values of performance indicators are recorded for the random yard truck deployment strategy. Furthermore, the developed simulation model can serve as an efficient planning tool for marine container terminal operators and enhance productivity of the available equipment.

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Seaside operations in container terminals: literature overview, trends, and research directions

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Cited by 151 publications

References 89 publications

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

A novel memetic algorithm with a deterministic parameter control for efficient berth scheduling at marine container terminals

Solving the pre-marshalling problem to optimality with A* and IDA*

A New Simulation Model for a Comprehensive Evaluation of Yard Truck Deployment Strategies at Marine Container Terminals

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