Abstract

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided.

Highlights

  • Optimization is applied in many engineering and science fields, including manufacturing, inventory control, transportation, finance, economics [1, 2]

  • 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: 1. How can integrate artificial intelligence techniques with mathematical programming models to develop the intelligencete and data-driven decision support systems (DSS) in uncertain conditions caused by big data?

  • Data-driven optimization refers to the art and science of integrating the data-driven system based on machine learning (ML) to convert data into relevant and useful information and insights, and the model-based system based on mathematical programming to derive the optimal and more accurate decisions from the information

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Summary

Introduction

Optimization is applied in many engineering and science fields, including manufacturing, inventory control, transportation, finance, economics [1, 2]. 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. How can integrate artificial intelligence techniques with mathematical programming models to develop the intelligencete and data-driven decision support systems (DSS) in uncertain conditions caused by big data?. We demonstrate the use of data-driven optimization across three case studies from operations research 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. The book chapter ends with the conclusion, some managerial implications, and future research recommendations

Mathematical optimization under uncertainty
Stochastic programming
Robust optimization
Chance constrained programming
Deep learning (DL)
Reinforcement learning (RL)
Distributionally robust optimization
Data-driven robust optimization
Data-driven chance-constrained program
Leveraging DL techniques in the data-driven optimization
Deep data-driven models
Online learning-based data-driven optimization
Leveraging RL techniques for optimization
Deep RL for solving NP-hard problems
Conclusion and managerial implications
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