Purpose: This paper studies a 3-dimensional pallet loading problem considering interlock stacking pattern, box dimensions, humidity, and storage time, where overlapping and overhanging are not allowed. Despite the importance of this problem in the literature, our work provides the first method...

We present an approximation method for the hybrid flow shop scheduling problem based on relaxation and machine learning techniques. Our model combines a suitable relaxation of the objective function to a continuous solution space with a self-supervised learning method for neural networks, which...

Sustainability in supply chain management addresses various challenges, from waste minimization to resource efficiency maximization. Two-dimensional cutting problems are common problems in most supply chains where small rectangular items need to be cut from large rectangular stock sheets to meet...

Metaheuristics have been successfully used for solving complex structural optimization problems. Many algorithms are proposed for truss structure size and shape optimization under some constraints. This study considers eight population-based meta-heuristic methods: African Vultures Optimization...

This study proposes a multi-objective Sustainable Supply Chain Network (SSCN) model considering human resources limitations with different levels of expertise. The proposed model includes multiple suppliers, factories, and customers, where the construction of factories is a strategic decision,...

This study presents the design and development of a Remora Optimization -Tilt Integral-Derivative-Filter (RO-TIDF) controller to infuse ideal insulin doses to manage Blood Glucose (BG) levels in Type-I Diabetes Mellitus (TIDM) patients with Artificial Pancreas (AP). In this control technique,...

Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle...

Sentiment analysis is crucial in understanding and analyzing public opinions, feedback, and social media data. In this study, we propose a modified Bayesian Boosting algorithm with weight-guided optimal feature selection for sentiment analysis. The goal is to improve the accuracy and efficiency...