Project description
Bridging machine learning and optimisation
Organizations across various industries face the challenge of efficiently scheduling their production processes and rostering their workforce optimally. An increased challenge is the uncertainty around factors such as demand fluctuations and variable costs. Such factors can be estimated with machine learning, where the real challenge is a tight integration of the learning, predictions and the scheduling. What is needed is a framework for solving prediction + optimization problems that bridges the machine learning and combinatorial optimization solving tools. In my ERC Consolidator project 'Conversational Human-Aware Technology for Optimisation', we started building such a library: CPMpy. This proof of concept project will increase the technological readiness level of CPMpy, to demonstrate its potential and align it with industry needs.
Objective
In today’s world, organizations across various industries face the challenge of efficiently scheduling their production processes and rostering their workforce optimally. However, despite consistent improvements in combinatorial optimization software for scheduling and rostering, the complexity of this task continues to grow due to uncertainty about multiple factors such as employee availability, demand fluctuations, supplier variability, variable prices, the impact of weather and the increasing need for energy efficiency. Machine learning can be used to make estimates about these uncertain factors, but the real challenge is in integrating predictions and the optimization of scheduling and rostering problems. Or more precisely *that predictions and optimization over these predictions need to be developed and evaluated together*.
While many combinatorial optimisation solvers for solving scheduling and rostering exists, including Constraint Programming and Mixed Integer Programming solvers; few of these solvers can be easily integrated with machine learning libraries. Futhermore, in a machine learning pipeline, the requirements for the solver change. What is needed is a framework for solving prediction + optimization problems that bridges the machine learning and combinatorial optimization solving tools. It should allow actors to discover what a data-driven approach can signifigy to their scheduling and rostering problem, by allowing them to easily experiment and prototype, both on the learning side, the solving side and the combination of the two.
In my ERC Consolidator project 'Conversational Human-Aware Technology for Optimisation', we started building such a library: CPMpy. We notice an increasing industrial interest in solving Prediction + Optimisation problems, but a lack of unified tools to do so. This proposal sets out to increase the Technological Readiness Level of CPMpy from TRL 4 to 6; and to demonstrate its potential and align it with industry needs.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2023-POC
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
3000 Leuven
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.