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...

Berge equilibrium offers an alternative to Nash equilibrium in game theory, emphasizing cooperative stability rather than individual optimization. Despite recent interest, a systematic study of Berge equilibria in finite normal form games is still lacking, with fundamental questions like...

Background: Growing environmental concerns and the vulnerability of global supply chains to disruptions, such as pandemics, natural disasters, and logistical failures, necessitate the design of sustainable and resilient supply chains. Methods: A novel multi-period mixed-integer linear...

This paper presents a computationally efficient method for binary classification using Manski's (1975,1985) maximum score model when covariates are discretely distributed and parameters are partially but not point identified. We establish conditions under which it is minimax optimal to allow for...

This study develops a multi-echelon closed-loop supply chain (CLSC) optimization model for durable products by considering refurbishment, recycling processes, and carbon regulation. A mixed integer linear programming (MILP) is developed for CLSC network design involving suppliers, manufacturers,...

This paper addresses freight consolidation in rail transportation planning. Multiple demand destinations are served in a single trip to utilise the volume discount on freight rate. An efficient mixed-integer linear programming (MILP) based modelling framework is proposed to optimally allocate...

The proximal partially-parallel splitting method (PPSM), originally proposed in Wang, Desai, and He (2017), is a hybrid mechanism that inherits the nice properties of both Gauss-Seidel and Jacobian substitution procedures for solving the multiple-block convex minimisation problem, whose...

Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters, specifically flash floods triggered by cloudburst events....

Product recovery is critical in reducing costs, enhancing profitability, and improving supply chain responsiveness to customer demands. Remanufacturing returned products, as part of the circular economy, is a central strategy in achieving these goals. This study presents a model that optimizes...

The paper introduces a new type of shrinkage estimation that is not based on asymptotic optimality but uses artificial intelligence (AI) techniques to shrink the sample eigenvalues. The proposed AI Shrinkage estimator applies to both linear and nonlinear shrinkage, demonstrating improved...

The profitable exploitation of asset portfolios in the European electricity markets has become more challenging in recent years. This is particularly true for combined heat and power (CHP) generation units that are often facing must-run conditions due to heat demands that need to be satisfied....