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Multi-Objective Optimization in Supply Chain Management Using Simulated Annealing: Im...

CR Srikanth
Multi-objective optimization is critical in SCM to balance conflicting objectives such as cost minimization and service level maximization. This paper presents a comprehensive study on the application of Simulated Annealing (SA) as a metaheuristic approach to address these challenges. The study includes detailed mathematical formulations, Python code implementation, and empirical evaluations using real-world SCM datasets to demonstrate SA's effectiveness and practical application.
Optimizing Supply Chain Inventory Management Using Particle Swarm Optimization: A Com...

CR Srikanth
Efficient inventory management is crucial for reducing costs and improving service levels in supply chains. This paper presents the application of Particle Swarm Optimization (PSO) for optimizing inventory management. The implementation is evaluated using real-world datasets to demonstrate the efficiency and effectiveness of the PSO algorithm. We explore advanced variants of PSO to enhance performance, providing both mathematical formulations and practical code examples.
Technical Paper: Bessel Functions in Image Processing and Pattern Recognition

CR Srikanth
Bessel functions have gained prominence in image processing and pattern recognition for their ability to extract robust and discriminative features from radial intensity profiles. This paper explores the utilization of Bessel Fourier descriptors (BFDs) as a powerful method for shape and texture analysis in digital images. We present an in-depth analysis of Bessel functions' theoretical foundations, their implementation in Python, benefits over traditional methods, and practical applications using benchmark datasets.
Transforming Supply Chain Inventory Management with Hybrid Particle Swarm Optimizatio...

CR Srikanth
Efficient inventory management is crucial for reducing costs and improving service levels in supply chains. This paper presents the application of a Hybrid Particle Swarm Optimization (PSO) algorithm, which combines PSO with Genetic Algorithms (GAs), to optimize inventory management. The implementation is evaluated using real-world datasets to demonstrate the efficiency, accuracy, and cost-saving benefits of this approach. We also explore the performance and convergence improvements achieved through hybridization, providing a detailed analysis of the results.
Enhancing Supply Network Planning with the Adaptive Genetic Optimization Algorithm (A...

CR Srikanth
This research paper introduces the Adaptive Genetic Optimization Algorithm (AGOA) as a novel solution for optimizing supply network planning. AGOA leverages the principles of genetic algorithms to adaptively improve network efficiency and cost-effectiveness. Through comprehensive simulations and real-world case studies, AGOA demonstrates superior performance compared to traditional methods. This paper provides a detailed overview of the algorithm, its implementation, and its potential impact on the supply chain industry.