Decision Support in Optimization A Comparative Review of Techniques for Engineering Applications

Optimization plays a vital role in engineering, where complex systems must be designed for maximum efficiency, cost-effectiveness, and reliability. Given the wide range of optimization techniques available, it is impractical to cover them all in a single discussion. Therefore, this review highlights a selection of optimization methods that are widely used in engineering. Each technique is examined in terms of its operational mechanics, applications, advantages, and limitations in supporting informed decision-making in problem solving. Research significance: Understanding the various optimization strategies is crucial in engineering and applied sciences, where the outcomes often affect cost, performance, and safety. This review emphasizes frequently used optimization techniques, avoiding narrow or highly specialized methods to maintain broad relevance. Designers and engineers can benefit from a structured comparison of these methods, improving their ability to match the most appropriate approach to a given problem, thereby improving solution quality and resource efficiency in real-world situations. Methology: Alternatives: Gradient Descent Optimization, Genetic Algorithm, Simulated Annealing, Linear Programming (LP), Particle Swarm Optimization (PSO), Conjugate Gradient Method. Evaluation Parameters: Quality of material, Additional discount on quantity, Warranty period, Reliability. Result: The results show that Conjugate Gradient Method received the highest ranking, whereas Gradient Descent Optimization received the lowest ranking. Conclusion: Conjugate Gradient Method has the highest value for optimization according to the EDAS approach.