An explainability oriented approach to manage dependent supply chain risks. This project aims to help supply chain companies model the impact on their operations by capturing the uncertainties impacting their upstream suppliers. In the current uncertain business environment, the project's outcome will benefit service-based industries to have an enhanced understanding of their operating environment and take decisions accordingly to avoid failures. This will significantly increase the productivity ....An explainability oriented approach to manage dependent supply chain risks. This project aims to help supply chain companies model the impact on their operations by capturing the uncertainties impacting their upstream suppliers. In the current uncertain business environment, the project's outcome will benefit service-based industries to have an enhanced understanding of their operating environment and take decisions accordingly to avoid failures. This will significantly increase the productivity of Australian service-based industries across different domains. The expected outcome is that it generates new knowledge by which risk managers of a focal company can conjointly consider risk identification/assessment with risk management analysis to develop explainable strategies for managing uncertainties. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101281
Funder
Australian Research Council
Funding Amount
$329,278.00
Summary
An Efficient Computational Solver for Complex Engineering Problems. This project aims to address significant gaps in the existing knowledge about solving complex engineering problems that involve conflicting objectives and unquantifiable features. In these problems, the decision-maker is interested in knowing high-quality and dissimilar solutions that determine the trade-off between the problem objectives. The intended outcomes of this project include a novel robust computational solver that can ....An Efficient Computational Solver for Complex Engineering Problems. This project aims to address significant gaps in the existing knowledge about solving complex engineering problems that involve conflicting objectives and unquantifiable features. In these problems, the decision-maker is interested in knowing high-quality and dissimilar solutions that determine the trade-off between the problem objectives. The intended outcomes of this project include a novel robust computational solver that can automatically find such solutions. The decision-makers can then choose the final solution based on their expertise and preferences. This expects to offer significant benefits to diverse engineering disciplines by finding superior and more practical solutions to their complex multidisciplinary problems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101245
Funder
Australian Research Council
Funding Amount
$445,047.00
Summary
Automated Modelling Assistance for the Creation of Complex Planning Models. Artificial Intelligence (AI) planning technology is used to control systems like automated factories, robots, or to solve complex optimisation problems. Creating these models is however rather complex and error-prone and requires experts to create them in the first place. This project aims at developing techniques and tools for automated modelling support. They will make the modelling process easier and guarantee desired ....Automated Modelling Assistance for the Creation of Complex Planning Models. Artificial Intelligence (AI) planning technology is used to control systems like automated factories, robots, or to solve complex optimisation problems. Creating these models is however rather complex and error-prone and requires experts to create them in the first place. This project aims at developing techniques and tools for automated modelling support. They will make the modelling process easier and guarantee desired model properties such as the desired system behaviour. The tools will thus contribute towards making the technology more easily accessible to companies that might want to deploy them, while reducing costs for doing so and increasing the quality of these models.
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