Simulation-based optimization for the quay crane scheduling problem

Mazza, Rina Mary; Trunfio, Roberto

doi:10.1109/wsc.2008.4736389

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…Nonetheless, no problem-specific analytical structural information is provided to the optimization algorithm. Legato et al (2008) address a dynamic quay crane scheduling problem at a maritime container terminal. The simulator, a stochastic queueing network model, is also used as a black-box to derive objective function estimates.…”

Section: Dynamic Simulation-based Optimization Problemsmentioning

confidence: 99%

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

Chong

Osorio

2018

Transportation Science

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This paper addresses large-scale urban transportation optimization problems with time-dependent continuous decision variables, a stochastic simulation-based objective function, and general analytical differentiable constraints. We propose a metamodel approach to address, in a computationally efficient way, these large-scale dynamic simulation-based optimization problems. We formulate an analytical dynamic network model that is used as part of the metamodel. The network model formulation combines ideas from transient queueing theory and traffic flow theory. The model is formulated as a system of equations. The model complexity is linear in the number of road links and is independent of the link space capacities. This makes it a scalable model suitable for the analysis of large-scale problems. The proposed dynamic metamodel approach is used to address a time-dependent large-scale traffic signal control problem for the city of Lausanne. Its performance is compared to that of a stationary metamodel approach. The proposed approach outperforms the stationary approach. This comparison illustrates the added value of providing the algorithm with analytical dynamic problem-specific structural information. The performance of a signal plan derived by the proposed approach is also compared to that of an existing signal plan for the city of Lausanne, and to that of a signal plan derived by a mainstream commercial signal control software. The proposed method can systematically identify signal plans with good performance. The online appendix is available at https://doi.org/10.1287/trsc.2016.0717 .

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Section: Dynamic Simulation-based Optimization Problemsmentioning

confidence: 99%

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

Chong

Osorio

2018

Transportation Science

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“…Legato and Trunfio (2007) propose a simulation-modeling paradigm based on process interaction for the optimal management of logistic activities in a modern container terminal. Simulation optimization has been proposed for quay crane scheduling as well as berth allocation (Legato et al, 2008;Arango et al, 2011). Simulation based approaches have been also used for the enhancement of the safety level in the port area, where the role of simulation is of predicting future traffic conditions (discrete event simulation) leaving the task to the optimization procedure to suggest safety maneuvers to the vessel's captain .…”

Section: Optimizing Maritime Logistics Via Simulationmentioning

confidence: 99%

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Huang

Chen

et al. 2015

Asia Pac. J. Oper. Res.

163

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Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss * Corresponding author. how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

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“…Total number of incoming vessels to port (n) [1,n] ID number of terminal [1,2] ID of the assigned berth [1,7] Type of allocated tugboat [1,4] The solution can be represented in an integer n*k matrix ( Table 5). In the investigated problem, k is equal to 4.…”

Section: Solution Encoding Parameters Rangementioning

confidence: 99%

“…Hence, the applications of meta-heuristic approaches with the combination of simulation are more flexible and powerful solutions. A study by [1] proposed simulation-based optimization approach for the quay crane scheduling problem. In [2] a simulation optimization method through genetic algorithm for scheduling loading operations in container terminals has been developed.…”

Section: Introductionmentioning

confidence: 99%

A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem

Ilati

Sheikholeslami

Hassannayebi

2014

PROMET

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Simulation-based optimization for the quay crane scheduling problem

Cited by 17 publications

References 10 publications

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem

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