A Novel Proactive–Reactive Scheduling Approach for the Quay Crane Scheduling Problem: A VUCA Perspective

Chargui, Kaoutar; Zouadi, Tarik; Sreedharan, V. Raja; Fallahi, Abdellah El; Reghioui, Mohamed

doi:10.1109/tem.2022.3151842

Cited by 7 publications

(2 citation statements)

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“…A proactive–reactive approach for the QCSP considering QC reliability and workload variability is proposed in Chargui et al. (2022). Al‐Dhaheri et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Stochastic programming approaches were followed in Al-Dhaheri et al (2016), Hu et al (2019), andMa et al (2021), a robust optimization approach was presented in Li and Zhang (2021), and a simulation-based approach can be found in Rouky et al (2019). A proactive-reactive approach for the QCSP considering QC reliability and workload variability is proposed in Chargui et al (2022). Al-Dhaheri et al (2016) proposed a stochastic mixed integer programming model that considers the entire container handling process involving both seaside operations and container transfer operations.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Rodrigues

Agra

2023

Int Trans Operational Res

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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.

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“…A proactive–reactive approach for the QCSP considering QC reliability and workload variability is proposed in Chargui et al. (2022). Al‐Dhaheri et al.…”

Section: Literature Reviewmentioning

confidence: 99%