Next-generation quay crane scheduling

Kasm, Omar Abou; Diabat, Ali

doi:10.1016/j.trc.2020.02.015

Cited by 17 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Obtained results showed the positive performance of the suggested branch and cutalgorithmm in comparison with the branch and boundalgorithmm developed by Kim and Park [9]. Kasm and Diabat [13] developed a mixed-integer program and a two-stage exact solution methodology to solve the QCSP. Sun et al [14] address the QCSP with vessel stability constraints, an exact algorithm based on logic-based benders decomposition.…”

Section: Related Workmentioning

confidence: 93%

A self-adaptation algorithm for quay crane scheduling at a container terminal

Yassen

Ayob

Jihad

et al. 2021

IJ-AI

View full text Add to dashboard Cite

Quay cranes scheduling at container terminals is a fertile area of study that is attracting researchers as well as practitioners in different parts of the world, especially in OR and artificial intelligence (AI). This process efficiency may affect the accomplishment and the competitive merits. As such, four local search algorithms (LSs) are utilized in the current work. These are hill climbing (HC), simulated annealing (SA), tabu search (TS), and iterated local search (ILS). The results obtained demonstrated that none of these LSs succeeded to achieve good results on all instances. This is because different QCSP instances have different characteristics with NP-hardness nature. Therefore, it is difficult to define which LS can yield the best outcomes for all instances. Consequently, appropriate LS selection should be governed by the type of problem and search status. The current work proposes to achieve this, the self-adaptation heuristic (self-H). The self-H is composed of two separate stages: The upper (LS-controller) and the lower (QCSP-solver). The LS-controller embeds an adaptive selection mechanism to adaptively select which LS is to be adopted by the QCSP-solver to solve the given problem. The results revealed that the self-H outperformed others as it attained better results over most instances and competitive results.

show abstract

Section: Related Workmentioning

confidence: 93%

A self-adaptation algorithm for quay crane scheduling at a container terminal

Yassen

Ayob

Jihad

et al. 2021

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Several scholars have focused on applying innovative technologies in port operations to improve efficiency and effectiveness. Abou Kasm and Diabat [25] have analyzed the scheduling problem of an innovative terminal with a short bridge capable of handling four containers in two adjacent harbors. Tan et al [26] investigated the automated quay crane scheduling problem (AQCSP) within an automated container terminal.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Jiang,

Ma,

Zhang

et al. 2024

Sustainability

View full text Add to dashboard Cite

Motivated by the need for a green and low-carbon economy, we explore the co-scheduling optimization of berths and cranes. Our aim is to balance the carbon tax and operating costs of ports under uncertain conditions, proposing an innovative nonlinear mixed-integer programming formulation. To address this optimization challenge, we have developed an enhanced version of the adaptive spiral flying dung beetle algorithm (ASFDBO). In order to evaluate the performance of the ASFDBO algorithm, we performed a benchmark function test and a convergence analysis with other recognized metaheuristics. In addition, we verified the practical applicability of the ASFDBO algorithm in different test scenarios. Through numerical experiments, we analyze the feasibility and effectiveness of the algorithm’s scheduling solutions and improvement strategies. Results indicate that our collaborative scheduling optimization, which considers both carbon and production costs, achieves feasible solutions and reduces carbon expenses. Finally, we investigate the impact of different carbon tax rates on the joint scheduling optimization of berths and quay cranes, and the results show that a reasonable carbon tax policy can effectively reduce the carbon emissions of ports.

show abstract

“… Xu et al (2021a) looked into the integrated scheduling between DTQC, vehicles and YCs. To our best knowledge, there is only one study looking into the management problem of the MTSC, which is a standard scheduling research solved with solver, heuristic algorithm and branch-and-price algorithm ( Abou Kasm and Diabat, 2020 ).…”

Section: Trends In Emerging Technology and Management Researchmentioning

confidence: 99%