Distributionally robust optimization for the berth allocation problem under uncertainty

Agra, Agostinho; Rodrigues, Filipe

doi:10.1016/j.trb.2022.07.009

Cited by 24 publications

(16 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the experiments, two sets of instances were used. The first set is the same one used in Agra and Rodrigues (2022), which derives from the original instances set used in Rodrigues and Agra (2021). It is composed of 100 instances with a number of vessels ranging from 6 to 15, with 10 instances for each number of vessels.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Here, we analyze how the proposed IBLH performs when tardiness is the criterion to be optimized. The requested departure time of each vessel k was generated as in Agra and Rodrigues (2022), that is, according to the formula

D_k = a_k + h_k + 0.2\times r\times h_k

, where r is a random number generated from the uniform distribution U (0, 1).…”

Section: Computational Resultsmentioning

confidence: 99%

Section: Baps With Deadlinesmentioning

confidence: 99%

See 2 more Smart Citations

Improved sequential insertion heuristics for berth allocation problems

Rodrigues

2023

Int Trans Operational Res

Self Cite

View full text Add to dashboard Cite

Berth allocation decisions affect all subsequent decisions in port terminals. Thus assigning berthing positions and berthing times to vessels—known as the berth allocation problem (BAP)—is one of the most important problems in port terminals. Because this is an NP‐hard problem, most of the solution methods proposed for solving it are heuristics. Although very different, some heuristics applied to the continuous BAP share a common step usually performed several times: the insertion of vessels from an insertion sequence in the time‐space diagram that represents a BAP solution. This process is frequently done by using minimum cost sequential insertion heuristics such as bottom‐left‐based heuristics. Although fast and simple, these heuristics may not lead to high‐quality solutions. In this paper, we propose improved sequential insertion heuristics that generally outperform the traditional ones and keep their simplicity and speed. The proposed heuristics are tested on a large set of instances of BAPs with different optimization objectives, and the obtained results show that they can lead to solutions up to 70% better than those obtained by traditional sequential insertion heuristics.

show abstract

Section: Computational Resultsmentioning

confidence: 99%

D_k = a_k + h_k + 0.2\times r\times h_k

, where r is a random number generated from the uniform distribution U (0, 1).…”

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Improved sequential insertion heuristics for berth allocation problems

Rodrigues

2023

Int Trans Operational Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Properties of these ambiguity sets can be found in Esfahani and Kuhn (2018). For an application of DRO with a Wasserstein ambiguity set to the berth allocation problem, see Agra and Rodrigues (2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model

Rodrigues

Agra

2023

Int Trans Operational Res

Self Cite

View full text Add to dashboard Cite

Quay cranes are among the most important resources in port terminals, and their efficient use is crucial for port terminals to remain competitive in the market. The problem of determining the sequence of tasks performed by the quay cranes that minimizes the turnaround time of a vessel is an NP‐hard problem known as the quay crane scheduling problem (QCSP).In this paper, we consider the unidirectional QCSP under uncertain processing times, where the cranes are only allowed to move in a specific direction. We start by presenting the first distributionally robust optimization (DRO) model for this problem and an exact decomposition algorithm to solve it. The proposed DRO model makes it possible to derive a stochastic programming model and a robust optimization model for the unidirectional QCSP by an appropriate choice of the risk‐averse parameter of the model. Through extensive numerical results, we compare these three approaches—stochastic programming, robust optimization, and DRO—to investigate whether significant differences among the solutions obtained exist. Finally, we propose a new method for helping practitioners to determine a representative set of different DRO solutions.

show abstract

“…Moreover, maritime operations often involve significant costs, and even slight fluctuations in these uncertain parameters can result in substantial economic losses. Given this situation, the modeling of, and research into container port optimization under stochastic conditions are gradually becoming the focus of academic research [8,17,18].…”

Section: Introductionmentioning

confidence: 99%

Distributionally Robust Programming of Berth-Allocation-with-Crane-Allocation Problem with Uncertain Quay-Crane-Handling Efficiency

Tang,

Liu,

et al. 2023

Sustainability

View full text Add to dashboard Cite

In order to promote the efficient and intelligent construction of container ports, we focus on the optimization of berth-and-quay-crane (QC) allocation in tidal terminal operations. This paper investigates the quay-crane-profile-(QC-profile)-based assignment problem, and considers the uncertainty in QC profiles regarding QC efficiency for the first time. A mixed-integer programming (MIP) model is established for a discrete berth allocation with a crane-assignment problem (BACAP), considering the tide time window. We aim to minimize the total time loss caused by anchorage and the delay of vessels. Leveraging the theory of uncertainty optimization, the proposed deterministic model is extended into a stochastic programming (SP) model and a distributionally robust optimization (DRO) model, via the consideration of the random QC efficiency. To solve the proposed models, a column generation (CG) algorithm is employed, utilizing the mathematical method and subproblem-solving approach. The numerical experiments with different instances demonstrate that the DRO model yields a smaller variation in the objective function values, and the effectiveness of the CG method. The experimental results verify the robustness of the constructed models, and the efficiency of the proposed algorithm.

show abstract

Distributionally robust optimization for the berth allocation problem under uncertainty

Cited by 24 publications

References 37 publications

Improved sequential insertion heuristics for berth allocation problems

Improved sequential insertion heuristics for berth allocation problems

Handling uncertainty in the quay crane scheduling problem: a unified distributionally robust decision model

Distributionally Robust Programming of Berth-Allocation-with-Crane-Allocation Problem with Uncertain Quay-Crane-Handling Efficiency

Contact Info

Product

Resources

About