Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator...

Operating Rooms (ORs) are pivotal hospital resources, significantly impacting expenses and revenue. This paper introduces a stochastic bi-objective model for OR allocation and scheduling of elective surgeries, considering surgeons' preferences for specific ORs and preferred start times, as well...

This study presents a novel Full Multiplicative Data Envelopment Analysis (FMDEA) for solving Multi-Attribute Decision-Making (MADM) problems. The proposed model offers an innovative approach to solving MADM problems by integrating the principles of Data Envelopment Analysis (DEA) with Full...

This study introduces a hybrid feature selection technique with a multi-objective algorithm incorporating Information Gain, Random Forest, and Relief F-based approach. We integrate the strengths of filter and wrapper methodologies to enhance the efficacy of addressing feature selection. The...

The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic method emulating the cooperative behavior of mongooses during food searches. The DMO algorithm effectively addresses various...

Open-pit mining operations depend heavily on the availability and reliability of complex equipment fleets, where the failure of one component can disrupt overall productivity. This study proposes two complementary optimization models to enhance fleet allocation and reliability in the mining...

This study investigates an NP-hard parallel machine scheduling problem, a critical challenge in manufacturing, healthcare, and logistics industries where efficient resource allocation is essential. The issue involves scheduling operations where each task requires an additional resource, with...

Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have...