A chemical tanker scheduling problem: Port of Houston case study

Cankaya, Burak; Wari, Ezra; Tokgoz, Berna Eren

doi:10.1504/ijps.2019.103029

Cited by 3 publications

(3 citation statements)

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“…Machine-learning applications effectively perform predictive tasks, and they are widely used for many business and scientific applications (Delen et al, 2020). Machine learning has produced promising predictions in a variety of business contexts, ranging from healthcare (Almeda et al, 2019) and stochasticity problems in transportation scheduling (Cankaya et al, 2019) to stock market fluctuations (Shen et al, 2012) and the analysis of incident reports (Topuz & Delen, 2021) One problem with the primitive machine-learning application algorithms is that they are black-box algorithms; namely, you can make accurate predictions but cannot understand how the system works, so you cannot explain the root processes for making the predictions (Papernot et al, 2017). Recently, models have been developed that merge predictions with relational inferences and are being used to understand business inferences hidden in the data (Topuz et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Introduction to the Minitrack on Drivers for Next Level Digital Service Innovation: Metaverse, IoT and Generative AI

Tuunanen,

Böhmann,

Leimeister

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "highimpact, low-probability" (HILP) events. Aviation incident reports are inspected by experts, but it is also important to have a comprehensive overview of incidents and their holistic effects. This study provides an interpretable machine-learning framework for predicting aircraft damage. In addition, it describes patterns of flight specifications detected through the use of a simulation tool and illuminates the underlying reasons for specific aviation accidents. As a result, we can predict the aircraft damage with 85% accuracy and 84% in-class accuracy. Most important, we simulate a combination of possible flight-type, aircraft-type, and pilot-expertise combinations to arrive at insights, and we recommend actions that can be taken by aviation stakeholders, such as airport managers, airlines, flight training companies, and aviation policy makers. In short, we combine predictive results with simulations to interpret findings and prescribe actions.

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Section: Literature Reviewmentioning

confidence: 99%

Introduction to the Minitrack on Drivers for Next Level Digital Service Innovation: Metaverse, IoT and Generative AI

Tuunanen,

Böhmann,

Leimeister

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…They can compare these results with current disposal barge rental rates and chose one of these options next time when one of their tankers visits that port. Another application is to track the total time it really takes for tank-cleaning and optimize the chemical tanker schedules exactly with mixedinteger programming or feasibly with heuristics (Cankaya et al, 2019b). There have been studies on using ANN in heuristics for scheduling problems, but it does not directly fit to the chemical tanker scheduling research problem stated above (Abedi et al, 2017).…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

“…Some inefficiencies exist in chemical ship scheduling in the PoH, resulting in long waiting to load/unload chemical cargo (Scully, 2015). One of the reasons for inefficiencies in scheduling in the PoH is the requirement of the tank-cleaning process (Cankaya et al, 2019b). Thus, determining whether the vessel is in the tanker cleaning stage or not is a critical issue, as it may reduce the overall cost of port operations and congestion it the HSC.…”

Section: Introductionmentioning

confidence: 99%

Development of a machine-learning-based decision support mechanism for predicting chemical tanker cleaning activity

Cankaya

Tokgoz

Dağ

et al. 2021

JM2

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Purpose This paper aims to propose a machine learning-based automatic labeling methodology for chemical tanker activities that can be applied to any port with any number of active tankers and the identification of important predictors. The methodology can be applied to any type of activity tracking that is based on automatically generated geospatial data. Design/methodology/approach The proposed methodology uses three machine learning algorithms (artificial neural networks, support vector machines (SVMs) and random forest) along with information fusion (IF)-based sensitivity analysis to classify chemical tanker activities. The data set is split into training and test data based on vessels, with two vessels in the training data and one in the test data set. Important predictors were identified using a receiver operating characteristic comparative approach, and overall variable importance was calculated using IF from the top models. Findings Results show that an SVM model has the best balance between sensitivity and specificity, at 93.5% and 91.4%, respectively. Speed, acceleration and change in the course on the ground for the vessels are identified as the most important predictors for classifying vessel activity. Research limitations/implications The study evaluates the vessel movements waiting between different terminals in the same port, but not their movements between different ports for their tank-cleaning activities. Practical implications The findings in this study can be used by port authorities, shipping companies, vessel operators and other stakeholders for decision support, performance tracking, as well as for automated alerts. Originality/value This analysis makes original contributions to the existing literature by defining and demonstrating a methodology that can automatically label vehicle activity based on location data and identify certain characteristics of the activity by finding important location-based predictors that effectively classify the activity status.

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Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents

Cankaya

Topuz

et al. 2023

Omega

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A chemical tanker scheduling problem: Port of Houston case study

Cited by 3 publications

References 0 publications

Introduction to the Minitrack on Drivers for Next Level Digital Service Innovation: Metaverse, IoT and Generative AI

Introduction to the Minitrack on Drivers for Next Level Digital Service Innovation: Metaverse, IoT and Generative AI

Development of a machine-learning-based decision support mechanism for predicting chemical tanker cleaning activity

Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents

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