Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

No description supplied.</p

Evolving Scheduling Heuristics via Genetic Programming With Feature Selection in Dynamic Flexible Job-Shop Scheduling

No description supplied.</p

Leveraging explainable AI for enhanced decision making in humanitarian logistics: An adversarial coevolution (ACTION) framework

Abstract: This study examines the potential of AI-enabled wargames to enhance strategic decisionmaking in humanitarian assistance and disaster relief (HADR). We introduce an Adversarial CoevoluTION (ACTION) framework, which showcases AI’s capacity to evolve adaptable policies capable of responding to dynamic changes and adversarial actions in HADR wargame scenarios. The framework presented employs a grammar-based genetic programming algorithm to evolve intelligent and interpretable player policies. We apply the ACTION framework to a HADR wargame case study, commonly used by the Australian Defence Science and Technology Group for research purposes. The case study centres on a hypothetical disaster relief scenario in the fictional Joadia Islands, struck by a tsunami, necessitating the evacuation of dispersed civilians. Experimental results illustrate that the ACTION framework can evolve policies that adapt to environmental uncertainties and respond effectively to adversarial actions. This study offers evidence of the potential and practical application of AI-enabled technology in real-life humanitarian situations. Our findings suggest practical guidelines for humanitarian practitioners to enhance the efficiency and effectiveness of logistics planning for humanitarian aid, ultimately leading to improved outcomes in HADR scenarios.</p

Fairness optimisation with multi-objective swarms for explainable classifiers on data streams

Recently, advanced AI systems equipped with sophisticated learning algorithms have emerged, enabling the processing of extensive streaming data for online decision-making in diverse domains. However, the widespread deployment of these systems has prompted concerns regarding potential ethical issues, particularly the risk of discrimination that can adversely impact certain community groups. This issue has been proven to be challenging to address in the context of streaming data, where data distribution can change over time, including changes in the level of discrimination within the data. In addition, transparent models like decision trees are favoured in such applications because they illustrate the decision-making process. However, it is essential to keep the models compact because the explainability of large models can diminish. Existing methods usually mitigate discrimination at the cost of accuracy. Accuracy and discrimination, therefore, can be considered conflicting objectives. Current methods are still limited in controlling the trade-off between these conflicting objectives. This paper proposes a method that can incrementally learn classification models from streaming data and automatically adjust the learnt models to balance multi-objectives simultaneously. The novelty of this research is to propose a multi-objective algorithm to maximise accuracy, minimise discrimination and model size simultaneously based on swarm intelligence. Experimental results using six real-world datasets show that the proposed algorithm can evolve fairer and simpler classifiers while maintaining competitive accuracy compared to existing state-of-the-art methods tailored for streaming data

Enhancing constraint programming via supervised learning for job shop scheduling

Constraint programming (CP) is a powerful technique for solving constraint satisfaction and optimization problems. In CP solvers, the variable ordering strategy used to select which variable to explore first in the solving process has a significant impact on solver effectiveness. To address this issue, we propose a novel variable ordering strategy based on supervised learning, which we evaluate in the context of job shop scheduling problems. Our learning-based methods predict the optimal solution of a problem instance and use the predicted solution to order variables for CP solvers. Unlike traditional variable ordering methods, our methods can learn from the characteristics of each problem instance and customize the variable ordering strategy accordingly, leading to improved solver performance. Our experiments demonstrate that training machine learning models is highly efficient and can achieve high accuracy. Furthermore, our learned variable ordering methods perform competitively compared to four existing methods. Finally, we showcase the benefits of integrating machine learning-based variable ordering methods with conventional domain-based approaches through tie-breaking

A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions

Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitters of greenhouse gas emissions. Reducing building energy consumption directly contributes to achieving energy sustainability, emissions reduction, and addressing the challenges of a warming planet, while also supporting the rapid urbanization of human society. Energy Conservation Measures (ECM) that are digitalized using advanced sensor technologies are a formal approach that is widely adopted to reduce the energy consumption of building infrastructure. Measurement and Verification (M&V) protocols are a repeatable and transparent methodology to evaluate and formally report on energy savings. As savings cannot be directly measured, they are determined by comparing pre-retrofit and post-retrofit usage of an ECM initiative. Given the computational nature of M&V, artificial intelligence (AI) algorithms can be leveraged to improve the accuracy, efficiency, and consistency of M&V protocols. However, AI has been limited to a singular performance metric based on default parameters in recent M&V research. In this paper, we address this gap by proposing a comprehensive AI approach for M&V protocols in energy-efficient infrastructure. The novelty of the framework lies in its use of all relevant data (pre and post-ECM) to build robust and explainable predictive AI models for energy savings estimation. The framework was implemented and evaluated in a multi-campus tertiary education institution setting, comprising 200 buildings of diverse sensor technologies and operational functions. The results of this empirical evaluation confirm the validity and contribution of the proposed framework for robust and explainable M&V for energy-efficient building infrastructure and net zero carbon emissions