OPTIMIZATION TECHNIQUES IN OPERATIONS RESEARCH: A REVIEW

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Published: Apr 10, 2019
DOI: https://doi.org/10.61841/turcomat.v10i1.14604
Keywords:
Operations Research, Optimization Techniques, Linear Programming, Heuristic Optimization, Metaheuristic Optimization, Hybrid Methods, Multi-Objective Optimization, Big Data, Machine Learning, Artificial Intelligence

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Badri Vishal Padamwar
Professor, Faculty of Science, ISBM University, Gariyaband, Chhattisgarh, India.
Hemant Pandey
Assistant Professor, Faculty of Science, ISBM University, Gariyaband, Chhattisgarh, India

Abstract

This review paper provides an in-depth exploration of optimization techniques in Operations Research (OR), highlighting their significance, recent advancements, challenges, and future directions. Operations Research utilizes mathematical and analytical methods to optimize decision-making processes across various industries. The paper begins with an overview of OR, discussing its definition, scope, and historical development. It then delves into the fundamentals of optimization, covering different types of optimization problems and algorithms. Classical optimization techniques such as Linear Programming, Integer Programming, and Nonlinear Programming are discussed, followed by an exploration of heuristic and metaheuristic optimization techniques including Genetic Algorithms, Simulated Annealing, Tabu Search, Particle Swarm Optimization, and Ant Colony Optimization. Recent advances in optimization, including hybrid methods, multi-objective optimization, optimization for big data, and integration with machine learning and artificial intelligence, are analyzed. Furthermore, the paper examines the challenges faced by optimization research and outlines emerging trends and future prospects. By synthesizing current knowledge and identifying areas for future research, this paper aims to contribute to the ongoing development and application of optimization techniques in OR and related fields.

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How to Cite
Padamwar, B. V. ., & Pandey, H. (2019). OPTIMIZATION TECHNIQUES IN OPERATIONS RESEARCH: A REVIEW. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 10(1), 746–752. https://doi.org/10.61841/turcomat.v10i1.14604
Issue
Vol. 10 No. 1 (2019)
Section
Research Articles
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