Prescriptive Analytics for Data-Driven Capacity Management

Pascal M. Notz ()
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Pascal M. Notz: Chair of Logistics and Quantitative Methods, Julius-Maximilians-Universität Würzburg

Chapter Chapter 4 in Operations Research Proceedings 2022, 2023, pp 27-33 from Springer

Abstract: Abstract Prescriptive analytics approaches integrate machine learning prediction and optimization to directly derive decisions for planning problems from historical observations of demand and a large set of features (co-variates). This paper summarizes the key results of the author’s dissertation and presents two prescriptive analytics approaches, kernelized empirical risk minimization and weighted sample average approximation, to solve complex capacity planning problems. It demonstrates the applicability of both approaches to a real-world two-stage capacity planning problem and evaluates their performance relative to traditional parametric approaches that first estimate a demand distribution and then solve a stochastic optimization problem, and a traditional non-parametric approach (sample average approximation). The results of numerical analyses demonstrate that the new prescriptive analytics approaches can lead to substantial performance improvements of up to 58% compared to traditional approaches.

Keywords: Prescriptive analytics; Machine learning; Data-driven operations management; Capacity planning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-24907-5_4

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DOI: 10.1007/978-3-031-24907-5_4

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