**1. Introduction**

Optimization is applied in many engineering and science fields, including manufacturing, inventory control, transportation, finance, economics [1, 2]. Some parameters involved in optimization problems are subject to uncertainty in real practice due to various reasons, including measurement errors and uncontrollable disturbances [3]. Such uncertain parameters can be product demand and price, raw material supply chain cost, production cost. Disregarding uncertainty could, unfortunately, render the solution of a deterministic optimization problem suboptimal or even infeasible. In the era of big data and deep learning (DL), intelligent use of data and knowledge extraction from them have great benefits for organizations. Besides, in today's complex world, uncertainty on the lack of enough data has been replaced by too much data, which creates numerous opportunities for academicians and practitioners [4]. A large amount of interactive data is routinely created, collected, and archived in different industries; these data are becoming an important asset in process operation, control, and design. Explosive growth in volume and different sorts of data in organizations has created the need to develop technologies that can intelligently and rapidly analyze large volumes of data [4]. The traditional optimization methods cannot face big data satisfactorily. Nowadays, a wide array of emerging machine learning (ML) techniques can be leveraged to analyze data and extract relevant, accurate, and useful information and knowledge for smart decisionmaking. More recently, the dramatic progress of ML, especially DL over the past decade, coupled with recent advances in mathematical programming, sparks a flurry of interest in data-driven optimization [5, 6]. The uncertainty model is formulated based on a data-driven optimization paradigm, allowing uncertainty data to speak for themselves in the optimization algorithm. In this way, rich knowledge underlying uncertainty data set can be extracted and harnessed automatically for smart and data-driven decision making. In such situations, the effectiveness and efficiency of traditional operational research methods are questionable. In recent years, the inefficiency of traditional methods in facing the uncertainty caused by big data has led researchers to integrate *artificial intelligence* (AI) with optimization methods. Integrating AI and optimization methods play a crucial role in solving problems in dynamic and uncertain environments. Nowadays, a wide range of ML tools has emerged that can be leveraged to analyze data automatically and extract relevant, accurate, and useful information for smart and data-driven decision-making. DL is one of the most rapidly growing sub-fields of the ML technique that demonstrates remarkable power in processing and deciphering a large volume of data through a complex architecture. Reinforcement learning (RL) is another ML sub-field that recently is applied to tackle complex sequential decision problems. This branch of ML epitomizes a step toward building autonomous systems by understanding the visual world.

The objective of this study is to provide an overview of the use of data-driven optimization in academia and practice from the following perspectives:


#### *Artificial Intelligence and Its Application in Optimization under Uncertainty DOI: http://dx.doi.org/10.5772/intechopen.98628*

In this regard, this chapter reviews recent advances in data-driven optimization that highlight the integration of mathematical programming and ML for decision-making under uncertainty and identifies potential research opportunities. We compare datadriven optimization performance to conventional models from optimization methodology. We summarize the existing research papers on data-driven optimization under uncertainty and classify them into three categories: Data-driven stochastic program, Data-driven robust optimization, and Data-driven chance-constrained, according to their unique approach to uncertainty modeling distinct optimization structures. Based on the literature survey, we identify five promising future research directions on optimization under uncertainty in the era of big data and DL, (i) Employment of DL in the field of data-driven optimization under uncertainty, (ii) Deep data-driven models, (iii) Online learning-based data-driven optimization, (iv) Leveraging RL techniques for optimization, and (v) Deep RL for solving NP-hard problems and highlight respective research challenges and potential methodologies. We conducted an extensive literature review on recent papers published across the premier journals between 2002 and 2020 in our field, namely, the European Journal of Operational Research, Operations Research, Journal of Cleaner Production, Production and Operations Management, Journal of Operations Management, Computers in Industry, and Decision Sciences. We specifically searched for papers containing "big data", "data-driven optimization", "artificial intelligence", "machine learning", "deep learning", and "Reinforcement learning". However, our research into the existing literature reveals a scarcity of research works utilizing DL and RL in these disciplines.

The remainder of this paper is organized as follows: Section 2 provides an introduction to the mathematical optimization method. In Section 3, a brief review of AI methods such as ML, DL, and RL is provided. In sections 4–6, applying different ML, DL, and RL techniques in data-driven optimization under uncertainty are presented. Finally, the book chapter ends with the conclusion, some managerial implications, and future research recommendations.
