Optimal berth scheduling and sequencing under unexpected events

Al–Refaie, Abbas; Abedalqader, Hala

doi:10.1080/01605682.2020.1843981

Cited by 14 publications

(3 citation statements)

References 26 publications

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“…To deal with uncertainties, reactive and proactive approaches have been discussed. The reactive approach minimizes the deviation from the initial schedule to respond to real‐time disruptions (Zeng et al., 2011; Li et al., 2015; Umang et al., 2017; Xiang et al., 2018; Lv et al., 2020; Kim et al., 2021; Al‐Refaie and Abedalqader, 2022), whereas the proactive approach absorbs uncertainties in a baseline schedule (Lu and Xi, 2010; Xu et al., 2012; Rodriguez‐Molins et al., 2014; Mohammadi and Forghani, 2018; Iris and Lam, 2019; Liu et al., 2020; Zhen et al., 2021). In the literature, studies on proactive approaches are largely divided into two groups: robust and stochastic approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Proactive berth scheduling with data‐driven buffer time in container terminals

Woo,

Park,

Cho

et al. 2023

Int Trans Operational Res

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Global shipment volumes have been increasing due to changes in the business environment of e‐commerce and manufacturing. Consequently, container vessels carry more cargo for international trade, increasing uncertainties in terminal management. Terminal operators manage terminals by establishing a proactive schedule that responds to disruptions such as vessel delays, and the introduction of buffer time is a representative proactive strategy. In this study, by analyzing historical delay data with machine learning, we propose data‐driven buffer times to consider the heterogeneous arrival uncertainty of vessels. Thus, we proactive scheduling with data‐driven buffer times according to the desired robustness levels. This is a novel study on berth scheduling that applies data mining approaches to improve operations research techniques. Numerical experiments were conducted on the berth scheduling with time‐invariant quay crane assignment using real‐life data to validate the effectiveness of the proposed method. These experimental results revealed that applying the data‐driven buffer time could effectively reduce the cost incurred at the terminal by balancing baseline and recovery costs. In addition, our proposed methodology ensured the quality of the solution compared with a stochastic method and reduced the computational burden of a stochastic problem by using the data‐driven buffer times obtained prior to the solution construction. Therefore, the proposed method can be introduced into terminal operations to overcome the deficiencies of traditional approaches in terms of academic perspective.

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Section: Literature Reviewmentioning

confidence: 99%

Proactive berth scheduling with data‐driven buffer time in container terminals

Woo,

Park,

Cho

et al. 2023

Int Trans Operational Res

View full text Add to dashboard Cite

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“…A comprehensive review of the BAP under uncertainties was given by [14]. According to the review, uncertain factors regarding the arrival time [15][16][17][18], handling time [19][20][21][22], quay crane breakdown [23][24][25], and unscheduled vessel calling [26,27] were commonly considered in BAP problems. Arrival time and handling time are the most common uncertain parameters to be considered in the BAP.…”

Section: Literature Reviewmentioning

confidence: 99%

Port Call Optimization at a Ferry Terminal with Stochastic Servicing Time and Additional Visits

Qi,

Chen,

Zheng

et al. 2023

JMSE

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Ferry shipping is an indispensable method of public transportation, especially in areas with well-developed river systems or coastal areas. The increasing demand for transport requires additional visits and introduces the problem of ship visit schedule engineering at ferry terminals with stochastic servicing time. In this paper, we propose a ferry visit planning problem to maximize the total profit, in which the berthing time, berthing location, and servicing time for each ferry visit are optimized. Then, a mixed-integer nonlinear programming model is proposed to formulate the focal problem. We propose a tailored solution method to convert the mixed-integer nonlinear programming model to a mixed-integer linear programming model. We further devise an inserting algorithm to test the performance of our model. A comparison between the results of the basic instance yielded by our model and those of the inserting algorithm validates our model and solution method. We then conduct sensitivity analyses of the impacts of different numbers of existing ferry visits and added ferry visits, different expectations of the real time taken by all the ferry visits, and different distribution patterns of existing ferry visits, to further validate the performance of our model.

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“…The objective function (1) minimizes the average waiting time. Constraint (2) determines the makespan, which corresponds to the completion time of all tasks. Constraints (3) and (4) define the first and the last tasks for each quay crane, respectively.…”

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confidence: 99%

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

Pérez-Rodríguez

2021

MCA

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The aim of the quay crane scheduling problem (QCSP) is to identify the best sequence of discharging and loading operations for a set of quay cranes. This problem is solved with a new hybrid estimation of distribution algorithm (EDA). The approach is proposed to tackle the drawbacks of the EDAs, i.e., the lack of diversity of solutions and poor ability of exploitation. The hybridization approach, used in this investigation, uses a distance based ranking model and the moth-flame algorithm. The distance based ranking model is in charge of modelling the solution space distribution, through an exponential function, by measuring the distance between solutions; meanwhile, the heuristic moth-flame determines who would be the offspring, with a spiral function that identifies the new locations for the new solutions. Based on the results, the proposed scheme, called QCEDA, works to enhance the performance of those other EDAs that use complex probability models. The dispersion results of the QCEDA scheme are less than the other algorithms used in the comparison section. This means that the solutions found by the QCEDA are more concentrated around the best value than other algorithms, i.e., the average of the solutions of the QCEDA converges better than other approaches to the best found value. Finally, as a conclusion, the hybrid EDAs have a better performance, or equal in effectiveness, than the so called pure EDAs.

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Optimal berth scheduling and sequencing under unexpected events

Cited by 14 publications

References 26 publications

Proactive berth scheduling with data‐driven buffer time in container terminals

Proactive berth scheduling with data‐driven buffer time in container terminals

Port Call Optimization at a Ferry Terminal with Stochastic Servicing Time and Additional Visits

A Hybrid Estimation of Distribution Algorithm for the Quay Crane Scheduling Problem

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