Optimization & Operations Research
Nabil Sellami; Romaissa Mellal; Basim A. Hassan
Abstract
This paper introduces two hybrid nonlinear conjugate gradient algorithms, NRB1 and NRB2, for solving unconstrained optimization problems. Both methods are constructed as a convex combination of the AlBayati–AlAssady (BA) and Conjugate Descent methods (CD). The first variant, denoted NRB1, employs ...
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This paper introduces two hybrid nonlinear conjugate gradient algorithms, NRB1 and NRB2, for solving unconstrained optimization problems. Both methods are constructed as a convex combination of the AlBayati–AlAssady (BA) and Conjugate Descent methods (CD). The first variant, denoted NRB1, employs an adaptive combination parameter designed to align its search direction more closely with Newton’s method, improving curvature approximation and acceleration of convergence. The second variant, NRB2, independently satisfies the conjugacy condition without relying on line search mechanisms, enhancing numerical stability. Both methods guarantee sufficient descent and global convergence properties under the strong Wolfe line search criteria. Extensive numerical experiments, using the performance profile of Dolan and Moré, demonstrate that NRB1 and NRB2 consistently outperform some classical conjugate gradient methods, including BA, CD, and Dai–Yuan, particularly for large-scale problems. Furthermore, NRB1 is applied to image restoration under salt-and-pepper noise, achieving competitive or superior peak signal-to-noise ratios (PSNR) compared to the Fletcher–Reeves (FR) algorithm with significantly fewer iterations, especially at high noise intensities.
Optimization & Operations Research
Niousha Zeidyahyaee; Sajjad Shokouhyar; Alireza Motameni
Abstract
This study develops a mathematically informed optimization framework for decision-making in reverse supply chain management, with an application to Apple’s MacBook product line. The proposed framework integrates Failure Mode and Effects Analysis (FMEA) with deep learning, based sentiment analysis ...
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This study develops a mathematically informed optimization framework for decision-making in reverse supply chain management, with an application to Apple’s MacBook product line. The proposed framework integrates Failure Mode and Effects Analysis (FMEA) with deep learning, based sentiment analysis in a multi-stage structure designed to quantify risk factors and predict consumer-driven outcomes. The dataset consists of 91 days of Twitter user feedback on Apple notebooks, processed using supervised learning algorithms to extract sentiment scores and thematic indicators of product performance. The analysis identifies “power and battery” and “storage” as the most critical components contributing to user dissatisfaction and elevated risk severity. These data-driven insights are incorporated into an optimization model that supports decisions on product recycling, refurbishment, and reuse. The hybrid framework enhances decision stability and accuracy compared with conventional reverse logistics models, while improving operational efficiency and environmental performance. The results demonstrate the model’s suitability as a scalable, machine-learning-supported optimization tool for reverse supply chain systems.
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