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Found 6 papers, 0 papers with code
Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams
This paper introduces a novel approach, evolutionary multi-objective optimisation for fairness-aware self-adjusting memory classifiers, designed to enhance fairness in machine learning algorithms applied to data stream classification.
Genetic-based Constraint Programming for Resource Constrained Job Scheduling
In the proposed algorithm, evolved programs represent variable selectors to be used in the search process of constraint programming, and their fitness is determined by the quality of solutions obtained for training instances.
AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling
However, developing an LLM for problem formulation is challenging, due to training data, token limitations, and lack of appropriate performance metrics.
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Finally, we demonstrate that hybridising the machine learning-based variable ordering methods with traditional domain-based methods is beneficial.
Adaptive Population-based Simulated Annealing for Uncertain Resource Constrained Job Scheduling
While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention.
An Efficient Merge Search Matheuristic for Maximising the Net Present Value of Project Schedules
This paper presents a novel matheuristic framework designed for resource constrained project scheduling, Merge search, which is a variable partitioning and merging mechanism to formulate restricted mixed integer programs with the aim of improving an existing pool of solutions.
