Yard crane scheduling to minimize total weighted vessel loading time in container terminals

Huang, Shell Ying; Li, Ya

doi:10.1007/s10696-017-9299-1

Cited by 11 publications

(8 citation statements)

References 50 publications

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“…Table A2 reports that the weights modulate the tardiness of flatcars, external trucks, and the total sum of tardiness. In general, the weight [50,50] are balanced.…”

Section: B Experimental Results From Weights Between a Flatcar And Ementioning

confidence: 99%

“…Similarly, Li et al [49] propose a heuristic to sequence yard crane jobs by minimizing the earliness and tardiness of each job. Huang and Li [50] present three proxies for the job sequencing problem: the first considers the minimization of weighted vessel loading time, the second considers the minimization of weighted vehicle tardiness, and the third considers the minimization of makespan.…”

Section: B Yard Crane Job Sequencingmentioning

confidence: 99%

See 1 more Smart Citation

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

Fibrianto¹,

Kang

Hong

2020

IEEE Access

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This study proposes a job scheduling model and its heuristics for an automated container terminal with an overhead shuttle crane (OS) to reduce the total tardiness time of flatcars and external trucks by considering the separation of each job into a main job, and a premarshaling or remarshaling job. The OS is busy or idle according to the fluctuations in the processing times of different pieces of equipment. We identify the OS job sequencing problem considering job separation (OSJSPS) as a mixed-integer programming (MIP) model, which simultaneously sequences a set of jobs and searches for their possible separation into premarshaling and remarshaling jobs. We present a two-stage genetic algorithm (TGA) based on two local improvement procedures: an iterative local search procedure and an opportunistic job separation procedure. We conclude that the two-stage genetic algorithm reduces the total tardiness time of the container terminal's flatcars and external trucks as the number of OS jobs increases.

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“…Table A2 reports that the weights modulate the tardiness of flatcars, external trucks, and the total sum of tardiness. In general, the weight [50,50] are balanced.…”

Section: B Experimental Results From Weights Between a Flatcar And Ementioning

confidence: 99%

Section: B Yard Crane Job Sequencingmentioning

confidence: 99%

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

Fibrianto¹,

Kang

Hong

2020

IEEE Access

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show abstract

“…Subsequent investigations by [6] revealed the effectiveness of particle swarm optimization combined with genetic algorithm operators in solving the same problem, particularly on smaller instances. Additionally, [20,21] extended the optimal search strategy introduced in [7] to tardiness related objectives for single YC scheduling, demonstrating superiority in minimizing vessel turnaround times through simulation studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To ensure compliance, internal trucks and YCs must be scheduled in advance. Each YC IR operation is assigned a due date as well as the priority of the associated vessel [21]. The due date represents the latest time by which a YC must complete a task to meet the deadline set by the QC schedule [21].…”

Section: Problem Description and Formulationmentioning

confidence: 99%

Enhancing Online Yard Crane Scheduling through a Two-Stage Rollout Memetic Genetic Programming

Jin,

Bai,

Zhou

et al. 2024

Preprint

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Over the past decade, the surge in global container port throughput has heightened the demand for terminal efficiency, with the container yard operations being central to the overall port performance. However, the unpredictable arrival of external trucks poses significant challenges for yard cranes, requiring the simultaneous scheduling of internal and external loading tasks.Traditional yard crane scheduling methods often rely on outdated assumptions that fail to account for the dynamic impact of external tasks.In response, container terminals increasingly model the yard crane scheduling as an online problem. A notable advancement in online scheduling is the online rollout method, which evaluates the decisions based on the potential outcomes of their future rollout solutions rather than immediate priorities. Although this method outperforms the latter approach, it faces two main issues: the rollout is time consuming, and decisions based solely on objective of rollout solution may not align with long-term scheduling objectives.To overcome these limitations, we have developed a two-stage adaptive rollout decision model. In the first stage, less desirable tasks are dynamically filtered out to reduce the number of rollouts, while the second stage employs a genetic programming evolved evaluation functions to infuse forward-looking insights into the scheduling process. This approach has proven to significantly enhance yard scheduling efficiency and performance, as confirmed by experimental validation. Given the dynamic nature of yard crane operations, we believe this method could be beneficially applied to other dynamic scheduling contexts.

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“…Some research is restricted to scheduling container operations only at one of the three introduced stages (Dik and Kozan 2017; Huang and Li 2017;Yu et al 2017;Lim et al 2015;Jin et al 2014). In some cases side-effects such as environmental impact (Yu et al 2017) or disturbance recovery (Lim et al 2015) are considered.…”

Section: Related Workmentioning

confidence: 99%

Coordinated optimization of equipment operations in a container terminal

et al. 2019

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Increasing international maritime transport drives the need for efficient container terminals. The speed at which containers can be processed through a terminal is an important performance indicator. In particular, the productivity of the quay cranes (QCs) determines the performance of a container terminal; hence QC scheduling has received considerable attention. This article develops a comprehensive model to represent the waterside operations of a container terminal. Waterside operations comprise single and twinlift handling of containers by QCs, automated guided vehicles and yard cranes. In common practice, an uncoordinated scheduling heuristic is used to dispatch the equipment operating on a terminal. Here, uncoordinated means that the different machines that operate in the container terminal seek optimal productivity solely considering their own respective stage. By contrast, our model provides a coordinated schedule in which operations of all terminal equipment can be considered at once to achieve productivity closer to the QC optimal. The model takes the form of a hybrid flow shop (HFS) with novel features for bi-directional flows and job pairing. The former enables jobs to move freely through the HFS in both directions; the latter constrains certain jobs to be performed simultaneously by a single machine. We solve the coordinated model by means of a tailored simulated annealing (SA) algorithm that balances solution quality and computational time. We empirically study time-bounded variants of SA and compare them with a branchand-bound algorithm. We show that our approach can produce coordinated schedules for a terminal with up to eight QCs in near real time.

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Yard crane scheduling to minimize total weighted vessel loading time in container terminals

Cited by 11 publications

References 50 publications

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

A Job Sequencing Problem of an Overhead Shuttle Crane in a Rail-Based Automated Container Terminal

Enhancing Online Yard Crane Scheduling through a Two-Stage Rollout Memetic Genetic Programming

Coordinated optimization of equipment operations in a container terminal

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