The role of optimization in some recent advances in data-driven decision-making

Abstract

Data-driven decision-making has garnered growing interest as a result of the increasing availability of data in recent years. With that growth many opportunities and challenges have sprung up in the areas of predictive and prescriptive analytics. Often, optimization can play an important role in tackling these issues. In this paper, we review some recent advances that highlight the difference that optimization can make in data-driven decision-making. We discuss some of our contributions that aim to advance both predictive and prescriptive models. First, we describe how we can optimally estimate clustered models that result in improved predictions. Next, we consider how we can optimize over objective functions that arise from tree ensemble models in order to obtain better prescriptions. Finally, we discuss how we can learn optimal solutions directly from the data allowing for prescriptions without the need for predictions. For all these new methods, we stress the need for good performance but also the scalability to large heterogeneous datasets.

Process Optimization in Manufacturing Industries Using Simulation Technologies

“…In manufacturing industries, the main target of the producer is to minimize the production costs and at the same time maximize the profits realized without jeopardizing the product quality. Thus, process optimization is basically a positive and intrinsically human perception of maximization or minimization in order to achieve the best or most favorable outcome while putting all other factors that surround into consideration [7]. Designing systems and products requires a deep understanding of influences that surround the desirable performance.…”

“…For a continuous process, a dynamic model can specify varying throughput impact; feed and composition changes; startup and shutdown; troubleshooting; real-time optimization and control loop tuning. For a batch process, a dynamic simulation can be adopted to investigate thermal performance and kinetics of reaction in a stirred-tank reactor; batch distillation cut decisions and control strategies effect; and emergency relief systems suitability [7].…”

Manufacturing Process Optimization in the Process Industry

“…KPIs were considered a basis for assessing the efficacy of process enhancement strategies. Baardman et al (2023) proposed design of dynamic experiments, an innovative data-driven experimental design methodology for optimizing industrial crystallization processes (OICP). In this case, the technique was applied to the batch crystallization process to establish the optimum lowering improvement, which when integrated, yielded the optimal temperature profile.…”