CENTRAL ASIAN JOURNAL OF THEORETICAL AND APPLIED SCIENCES Volume: 03 Issue: 06 | June 2022, ISSN: 2660-5317 © 2022, CAJOTAS, Central Asian Studies, All Rights Reserved 160 Copyright (c) 2022 Author (s). This is an open-access article distributed under the terms of Creative Commons Attribution License (CC BY).To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ CENTRAL ASIAN JOURNAL OF THEORETICAL AND APPLIED SCIENCES Volume: 03 Issue: 06 | June 2022 ISSN: 2660-5317 Review of Recent Uncertainty Strategies within Optimization Techniques Ahmed Hasan ALRIDHA Ministry of Education, General Directorate of Education in Babylon, Iraq. amqa92@yahoo.com Ekhlas Annon Mousa Ministry of Education, General Directorate of Education in Babylon, Iraq. ekhlasanoon@yahoo.com Ahmed Sabah Al-Jilawi Mathematics department, University of Babylon, Iraq. aljelawy2000@yahoo.com Received 24 th March 2022, Accepted 28 th May 2022, Online 12 th June 2022 Abstract: Despite the great progress in improvement methodologies, modernity may be a precedent for this progress. Actually, on the supply chain management scenario the decision-making becomes more challenging especially that various sources of model uncertainty are required to ensure the quality of the solution or even practical feasibility. Therefore, one of the most pressing problems today is incorporating variability in process parameters such as manufacturing time and reaction conditions. In this paper, some interactive methods are summarized that modify the actual plan obtained from the authoritative version of the system to correspond to the modifications or updated system data. Finally, the methods of dealing with problems were divided into two main approaches, the reactive approach and the preventive approach. Keywords: Robust optimization, Model Predictive Control, Stochastic Programming, Fuzzy programming methods, Rolling-horizon approach. I. INTRODUCTION Optimization is an important and effective area when considering the study of systems at the scheduling level, systems of physical and chemical reactions, industrial planning, site and transportation difficulties, resource allocation in engineering design and financing systems.5. It was recognized from the outset of the application of optimization to these challenges that natural and technical system analyzers are virtually always confronted with uncertainty [1,2,3,4,5,6,7,8,9]. This review's main goal is to give a quick understanding of optimization under uncertainty. Since the seminal works of Beale (1955), Bellman (1957), Bellman and Zadeh (1970), Charnes and Cooper (1959), Dantzig (1955), and Tintner (1955), both the theory and techniques of optimization under uncertainty have seen substantial development (1955). It